diff --git a/.github/workflows/build-and-test.yml b/.github/workflows/build-and-test.yml
deleted file mode 100644
index 762cdbb7e..000000000
--- a/.github/workflows/build-and-test.yml
+++ /dev/null
@@ -1,295 +0,0 @@
-name: build-and-test
-
-on:
-  workflow_call:
-    inputs:
-      build-all-platforms:
-        description: 'Build wheels for all platforms (Linux ARM64, Windows, macOS)'
-        required: false
-        type: boolean
-        default: true
-permissions:
-  contents: read
-  actions: write
-jobs:
-  check-rust:
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
-      - name: Install Rust toolchain
-        uses: dtolnay/rust-toolchain@6d653acede28d24f02e3cd41383119e8b1b35921
-        with:
-          toolchain: stable
-          components: rustfmt, clippy
-      - uses: prefix-dev/setup-pixi@97359467eae22a2d994114fe900db762fea86261 # v0.8.9
-        with:
-          cache: true
-      - name: Cache
-        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-check-rust
-          path: |
-            ~/.cargo
-            target
-            .pixi
-      - name: Check fmt
-        run: pixi run check-rs-fmt
-      - name: Check warnings
-        run: pixi run check-rs-warnings
-      - name: Check clippy
-        run: pixi run check-rs-clippy
-
-  test-rust:
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
-      - name: Install Rust toolchain
-        uses: dtolnay/rust-toolchain@6d653acede28d24f02e3cd41383119e8b1b35921
-        with:
-          toolchain: stable
-      - uses: prefix-dev/setup-pixi@97359467eae22a2d994114fe900db762fea86261 # v0.8.9
-      - name: Cache
-        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-test-rust2
-          path: |
-            ~/.cargo
-            target
-            .pixi
-      - name: start minio server in the background
-        run: pixi run start-minio &
-      - name: Test rust
-        run: |
-          pixi run test-rs --release
-      - name: Upload test artifacts
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        if: failure()
-        with:
-          name: vegafusion-rt-test-images-linux
-          path: |
-            vegafusion-runtime/tests/output
-
-  check-vegafusion-python:
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
-      - name: Install Rust toolchain
-        uses: dtolnay/rust-toolchain@6d653acede28d24f02e3cd41383119e8b1b35921
-        with:
-          toolchain: stable
-      - uses: prefix-dev/setup-pixi@97359467eae22a2d994114fe900db762fea86261 # v0.8.9
-      - name: Cache
-        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-wasm
-          path: |
-            ~/.cargo
-            target
-            .pixi
-      - name: Dev python
-        run: |
-          pixi run dev-py
-      - name: Check format and lint
-        run: |
-          pixi run lint-check-py
-      - name: Type check
-        run: |
-          pixi run type-check-py -v
-
-  build-vegafusion-python-linux-64:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
-      - name: Clear target/wheels
-        run: rm -rf target/wheels
-      - name: Build wheels (Linux)
-        uses: PyO3/maturin-action@ea5bac0f1ccd0ab11c805e2b804bfcb65dac2eab # v1.45.0
-        with:
-          command: build
-          manylinux: 2014
-          rust-toolchain: stable
-          args: -m vegafusion-python/Cargo.toml --profile release-opt --features=protobuf-src --strip --sdist
-      - name: Upload artifacts
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        with:
-          name: python-wheels-linux-64
-          path: |
-            target/wheels/*.tar.gz
-            target/wheels/*.whl
-          retention-days: 1
-          if-no-files-found: error
-
-  build-vegafusion-python-linux-arm64:
-    if: inputs.build-all-platforms
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
-      - name: Clear target/wheels
-        run: rm -rf target/wheels
-      - name: Build arm64 wheels
-        uses: PyO3/maturin-action@ea5bac0f1ccd0ab11c805e2b804bfcb65dac2eab # v1.45.0
-        with:
-          command: build
-          manylinux: 2_28
-          rust-toolchain: stable
-          args: -m vegafusion-python/Cargo.toml --profile release-opt --features=protobuf-src --strip --target aarch64-unknown-linux-gnu
-      - name: Upload artifacts
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        with:
-          name: python-wheels-linux-arm64
-          path: |
-            target/wheels/*.tar.gz
-            target/wheels/*.whl
-          retention-days: 1
-          if-no-files-found: error
-
-  build-vegafusion-python-win-64:
-    if: inputs.build-all-platforms
-    runs-on: windows-2022
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
-      - name: Install Rust toolchain
-        uses: dtolnay/rust-toolchain@6d653acede28d24f02e3cd41383119e8b1b35921
-        with:
-          toolchain: stable
-      - uses: prefix-dev/setup-pixi@97359467eae22a2d994114fe900db762fea86261 # v0.8.9
-      - name: Cache
-        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-python-win-64
-          path: |
-            ~/.cargo
-            target
-            .pixi
-      - name: Build vegafusion-python
-        run: |
-          pixi run build-py
-      - name: Upload artifacts
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        with:
-          name: python-wheels-win-64
-          path: |
-            target/wheels/*.tar.gz
-            target/wheels/*.whl
-          retention-days: 1
-          if-no-files-found: error
-
-  build-vegafusion-python-osx-64:
-    if: inputs.build-all-platforms
-    runs-on: macos-15-intel
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
-      - name: Install Rust toolchain
-        uses: dtolnay/rust-toolchain@6d653acede28d24f02e3cd41383119e8b1b35921
-        with:
-          toolchain: stable
-      - uses: prefix-dev/setup-pixi@97359467eae22a2d994114fe900db762fea86261 # v0.8.9
-      - name: Cache
-        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-python-osx-64
-          path: |
-            ~/.cargo
-            target
-            .pixi
-      - name: Build vegafusion-python
-        run: |
-          pixi run build-py
-      - name: Upload artifacts
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        with:
-          name: python-wheels-osx-64
-          path: |
-            target/wheels/*.tar.gz
-            target/wheels/*.whl
-          retention-days: 1
-          if-no-files-found: error
-
-  build-vegafusion-python-osx-arm64:
-    if: inputs.build-all-platforms
-    runs-on: macos-15
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
-      - name: Install Rust toolchain
-        uses: dtolnay/rust-toolchain@6d653acede28d24f02e3cd41383119e8b1b35921
-        with:
-          toolchain: stable
-          targets: aarch64-apple-darwin
-      - uses: prefix-dev/setup-pixi@97359467eae22a2d994114fe900db762fea86261 # v0.8.9
-      - name: Cache
-        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          key: osx-arm64-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-python-osx-arm64
-          path: |
-            ~/.cargo
-            target
-            .pixi
-      - name: Build vegafusion-python
-        run: |
-          pixi run build-py --target aarch64-apple-darwin
-      - name: Upload artifacts
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        with:
-          name: python-wheels-osx-arm64
-          path: |
-            target/wheels/*.tar.gz
-            target/wheels/*.whl
-          retention-days: 1
-          if-no-files-found: error
-
-  test-vegafusion-python-linux-64:
-    runs-on: ubuntu-22.04
-    needs: [build-vegafusion-python-linux-64]
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
-      - name: Setup Python
-        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
-        with:
-          python-version: "3.11"
-      - name: Install Chrome
-        uses: browser-actions/setup-chrome@c785b87e244131f27c9f19c1a33e2ead956ab7ce # v1.7.3
-        with:
-          chrome-version: stable
-      - name: Install fonts on Linux
-        if: runner.os == 'Linux'
-        run: |
-          echo ttf-mscorefonts-installer msttcorefonts/accepted-mscorefonts-eula select true | sudo debconf-set-selections
-          sudo apt-get install ttf-mscorefonts-installer
-      - name: Download vegafusion-python wheel
-        uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
-        with:
-          name: python-wheels-linux-64
-          path: target/wheels/
-      - name: install wheels
-        working-directory: target/wheels/
-        run: |
-          ls -la
-          python -m pip install deepnote_vegafusion-*manylinux_2_17_x86_64*.whl
-
-          # Optional dependencies
-          python -m pip install pyarrow pandas polars-lts-cpu "duckdb>=1.0" "vl-convert-python>=1.0.1rc1" scikit-image "pandas>=2.2" jupytext voila anywidget ipywidgets chromedriver-binary-auto
-
-          # Test dependencies
-          python -m pip install pytest altair vega-datasets scikit-image jupytext voila ipykernel anywidget ipywidgets selenium flaky tenacity chromedriver-binary-auto "pyspark==3.5.*"
-      - name: Test vegafusion
-        working-directory: vegafusion-python/
-        env:
-          VEGAFUSION_TEST_HEADLESS: 1
-        run: pytest -vv -rA tests/test_spark_e2e.py
-      - name: Upload test artifacts
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        if: failure()
-        with:
-          name: vegafusion-python-test-failures
-          path: |
-            vegafusion-python/tests/output/*
-
diff --git a/.github/workflows/build_test.yml b/.github/workflows/build_test.yml
new file mode 100644
index 000000000..53dc1a85f
--- /dev/null
+++ b/.github/workflows/build_test.yml
@@ -0,0 +1,648 @@
+name: build_test
+on:
+  pull_request:
+    types: [opened, synchronize]
+jobs:
+  check-rust:
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+          components: rustfmt, clippy
+      - uses: prefix-dev/setup-pixi@v0.8.9
+        with:
+          cache: true
+      - name: Cache
+        uses: actions/cache@v4
+        with:
+          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-check-rust
+          path: |
+            ~/.cargo
+            target
+            .pixi
+      - name: Check fmt
+        run: pixi run check-rs-fmt
+      - name: Check warnings
+        run: pixi run check-rs-warnings
+      - name: Check clippy
+        run: pixi run check-rs-clippy
+
+  test-rust:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os:
+          - ubuntu-22.04
+          - windows-2022
+          - macos-14
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+      - uses: prefix-dev/setup-pixi@v0.8.9
+      - name: Cache
+        uses: actions/cache@v4
+        with:
+          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-test-rust2
+          path: |
+            ~/.cargo
+            target
+            .pixi
+      - name: start minio server in the background
+        run: pixi run start-minio &
+      - name: Test rust
+        run: |
+          pixi run test-rs --release
+      - name: Upload test artifacts
+        uses: actions/upload-artifact@v4
+        if: failure()
+        with:
+          name: vegafusion-rt-test-images-${{ matrix.os }}
+          path: |
+            vegafusion-runtime/tests/output
+
+  build-vegafusion-wasm:
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+          targets: wasm32-unknown-unknown
+      - uses: prefix-dev/setup-pixi@v0.8.9
+      - name: Cache
+        uses: actions/cache@v4
+        with:
+          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-wasm
+          path: |
+            ~/.cargo
+            target
+            .pixi
+      - name: Build package
+        run: |
+          pixi run pack-wasm
+          mkdir -p package
+          cp vegafusion-wasm/pkg/vegafusion-wasm-*.tgz package/
+          echo "timestamp: $(date)" > package/info.txt
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4.4.3
+        with:
+          name: vegafusion-wasm-package
+          path: package
+
+  check-vegafusion-python:
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+      - uses: prefix-dev/setup-pixi@v0.8.9
+      - name: Cache
+        uses: actions/cache@v4
+        with:
+          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-wasm
+          path: |
+            ~/.cargo
+            target
+            .pixi
+      - name: Dev python
+        run: |
+          pixi run dev-py
+      - name: Check format and lint
+        run: |
+          pixi run lint-check-py
+      - name: Type check
+        run: |
+          pixi run type-check-py
+
+  # Use maturin action to build linux wheels within proper manylinux compatible containers
+  # (This is why we don't use the pixi "build-py" action)
+  build-vegafusion-python-linux-64:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Clear target/wheels
+        run: rm -rf target/wheels
+      - name: Build wheels (Linux)
+        uses: messense/maturin-action@v1.45.0
+        with:
+          command: build
+          manylinux: 2014
+          rust-toolchain: stable
+          args: -m vegafusion-python/Cargo.toml --profile release-opt --features=protobuf-src --strip --sdist
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4.4.3
+        with:
+          name: python-wheels-linux-64
+          path: |
+            target/wheels/*.tar.gz
+            target/wheels/*.whl
+          retention-days: 1
+          if-no-files-found: error
+
+  build-vegafusion-python-linux-arm64:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Clear target/wheels
+        run: rm -rf target/wheels
+      - name: Build arm64 wheels
+        uses: messense/maturin-action@v1.45.0
+        with:
+          command: build
+          manylinux: 2_28
+          rust-toolchain: stable
+          args: -m vegafusion-python/Cargo.toml --profile release-opt --features=protobuf-src --strip --target aarch64-unknown-linux-gnu
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4.4.3
+        with:
+          name: python-wheels-linux-arm64
+          path: |
+            target/wheels/*.tar.gz
+            target/wheels/*.whl
+          retention-days: 1
+          if-no-files-found: error
+
+  build-vegafusion-python-win-64:
+    runs-on: windows-2022
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+      - uses: prefix-dev/setup-pixi@v0.8.9
+      - name: Cache
+        uses: actions/cache@v4
+        with:
+          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-python-win-64
+          path: |
+            ~/.cargo
+            target
+            .pixi
+      - name: Build vegafusion-python
+        run: |
+          pixi run build-py
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4.4.3
+        with:
+          name: python-wheels-win-64
+          path: |
+            target/wheels/*.tar.gz
+            target/wheels/*.whl
+          retention-days: 1
+          if-no-files-found: error
+
+  build-vegafusion-python-osx-64:
+    runs-on: macos-13
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+      - uses: prefix-dev/setup-pixi@v0.8.9
+      - name: Cache
+        uses: actions/cache@v4
+        with:
+          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-python-osx-64
+          path: |
+            ~/.cargo
+            target
+            .pixi
+      - name: Build vegafusion-python
+        run: |
+          pixi run build-py
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4.4.3
+        with:
+          name: python-wheels-osx-64
+          path: |
+            target/wheels/*.tar.gz
+            target/wheels/*.whl
+          retention-days: 1
+          if-no-files-found: error
+
+  build-vegafusion-python-osx-arm64:
+    runs-on: macos-14
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+          targets: aarch64-apple-darwin
+      - uses: prefix-dev/setup-pixi@v0.8.9
+      - name: Cache
+        uses: actions/cache@v4
+        with:
+          key: osx-arm64-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-python-osx-arm64
+          path: |
+            ~/.cargo
+            target
+            .pixi
+      - name: Build vegafusion-python
+        run: |
+          pixi run build-py --target aarch64-apple-darwin
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4.4.3
+        with:
+          name: python-wheels-osx-arm64
+          path: |
+            target/wheels/*.tar.gz
+            target/wheels/*.whl
+          retention-days: 1
+          if-no-files-found: error
+
+  test-vegafusion-python-linux-64:
+    runs-on: ubuntu-22.04
+    needs: [build-vegafusion-python-linux-64]
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Setup Python
+        uses: actions/setup-python@v5.3.0
+        with:
+          python-version: "3.11"
+      - name: Install Chrome
+        uses: browser-actions/setup-chrome@v1
+        with:
+          chrome-version: stable
+      - name: Install fonts on Linux
+        if: runner.os == 'Linux'
+        run: |
+          echo ttf-mscorefonts-installer msttcorefonts/accepted-mscorefonts-eula select true | sudo debconf-set-selections
+          sudo apt-get install ttf-mscorefonts-installer
+      - name: Download vegafusion-python wheel
+        uses: actions/download-artifact@v4.1.8
+        with:
+          name: python-wheels-linux-64
+          path: target/wheels/
+      - name: install wheels
+        working-directory: target/wheels/
+        run: |
+          ls -la
+          python -m pip install vegafusion-*manylinux_2_17_x86_64*.whl
+
+          # Optional dependencies
+          python -m pip install pyarrow pandas polars-lts-cpu "duckdb>=1.0" "vl-convert-python>=1.0.1rc1" scikit-image "pandas>=2.2" jupytext voila anywidget ipywidgets chromedriver-binary-auto
+
+          # Test dependencies
+          python -m pip install pytest altair vega-datasets scikit-image jupytext voila ipykernel anywidget ipywidgets selenium flaky tenacity chromedriver-binary-auto
+      - name: Test lazy imports
+        working-directory: vegafusion-python/
+        run: python checks/check_lazy_imports.py
+      - name: Test vegafusion
+        working-directory: vegafusion-python/
+        env:
+          VEGAFUSION_TEST_HEADLESS: 1
+        run: pytest
+      - name: Upload test artifacts
+        uses: actions/upload-artifact@v4.4.3
+        if: failure()
+        with:
+          name: vegafusion-python-test-failures
+          path: |
+            vegafusion-python/tests/output/*
+
+  test-vegafusion-python-osx-arm64:
+    runs-on: macos-14
+    needs: [build-vegafusion-python-osx-arm64]
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Setup Python
+        uses: actions/setup-python@v5.3.0
+        with:
+          python-version: "3.10"
+      - name: Download vegafusion-python wheel
+        uses: actions/download-artifact@v4.1.8
+        with:
+          name: python-wheels-osx-arm64
+          path: target/wheels/
+      - name: install wheels
+        working-directory: target/wheels/
+        run: |
+          ls -la
+          python -m pip install vegafusion-*macosx_11_*_arm64.whl
+
+          # Optional dependencies
+          python -m pip install pyarrow pandas polars "duckdb>=1.0" vl-convert-python "pandas>=2.2"
+
+          # Test dependencies
+          python -m pip install pytest altair vega-datasets scikit-image
+
+      - name: Test vegafusion
+        working-directory: vegafusion-python/
+        run: pytest --ignore=tests/test_jupyter_widget.py
+
+  test-vegafusion-python-win-64:
+    runs-on: windows-2022
+    needs: [build-vegafusion-python-win-64]
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Setup Python
+        uses: actions/setup-python@v5.3.0
+        with:
+          python-version: "3.9"
+      - name: Download vegafusion-python wheel
+        uses: actions/download-artifact@v4.1.8
+        with:
+          name: python-wheels-win-64
+          path: target/wheels/
+      - name: install wheels
+        working-directory: target/wheels/
+        shell: powershell
+        run: |
+          Get-ChildItem -Force
+          $vegafusion = Get-ChildItem -Name "vegafusion-*win_amd64.whl" | Select-Object -First 1
+
+          python -m pip install $vegafusion
+
+          # Optional dependencies
+          python -m pip install pyarrow pandas "numpy<2" polars[timezone] "duckdb>=1.0" vl-convert-python 
+
+          # Test dependencies
+          python -m pip install pytest altair vega-datasets scikit-image
+      - name: Test vegafusion
+        working-directory: vegafusion-python/
+        run: pytest --ignore=tests/test_jupyter_widget.py
+
+  build-vegafusion-server-linux-64:
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+      - uses: prefix-dev/setup-pixi@v0.8.9
+      - name: Cache
+        uses: actions/cache@v4
+        with:
+          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-server-linux-64
+          path: |
+            ~/.cargo
+            target
+            .pixi
+      - name: Build vegafusion-server
+        run: |
+          pixi run build-rs-server
+      - name: zip executable
+        uses: vimtor/action-zip@v1.2
+        with:
+          files: target/release-opt/vegafusion-server
+          dest: vegafusion-server-linux-64.zip
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4.4.3
+        with:
+          name: vegafusion-server-linux-64
+          path: |
+            vegafusion-server-*
+          retention-days: 1
+          if-no-files-found: error
+
+  # # Below is an Attempt to cross compile to linux aarch64 within conda environment.
+  # # It fails in compiling protobuf-src. So instead we fall back to the direct use of
+  # # cargo (without involving conda).
+  #
+  #  build-vegafusion-server-linux-arm64:
+  #    runs-on: ubuntu-22.04
+  #    steps:
+  #      - name: Check out repository code
+  #        uses: actions/checkout@v4.2.2
+  #      - name: Install pixi
+  #        run: curl -fsSL https://pixi.sh/install.sh | bash && echo "${HOME}/.pixi/bin" >> $GITHUB_PATH
+  #      - name: Cache
+  #        uses: actions/cache@v4
+  #        with:
+  #          key: linux-arm64-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-server
+  #          path: |
+  #            ~/.cargo
+  #            target
+  #            .pixi
+  #      - name: Build vegafusion-server
+  #        run: |
+  #          pixi run python automation/download_rust_target.py aarch64-unknown-linux-gnu
+  #          pixi add gcc_linux-aarch64 -p linux-64
+  #          export RUSTFLAGS="-C linker=aarch64-linux-gnu-gcc"
+  #          pixi run build-rs-server --features=protobuf-src --target aarch64-unknown-linux-gnu
+  #      - name: zip executable
+  #        uses: vimtor/action-zip@v1.2
+  #        with:
+  #          files: target/aarch64-unknown-linux-gnu/release/vegafusion-server
+  #          dest: vegafusion-server-linux-arm64.zip
+  #      - name: Upload artifacts
+  #        uses: actions/upload-artifact@v4.4.3
+  #        with:
+  #          name: vegafusion-server-linux-arm64
+  #          path: |
+  #            vegafusion-server-*
+
+  build-vegafusion-server-linux-arm64:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install latest stable Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+      - name: Cache rust dependencies
+        uses: Swatinem/rust-cache@v2.7.5
+        with:
+          cache-on-failure: True
+      - name: Download arm64 toolchain
+        run: |
+          rustup target add aarch64-unknown-linux-gnu
+      - name: Download gcc for cross compilation
+        run: |
+          sudo apt-get update
+          sudo apt-get install gcc-aarch64-linux-gnu
+      - name: Build vegafusion-server
+        env:
+          RUSTFLAGS: "-C linker=aarch64-linux-gnu-gcc"
+        run: |
+          cargo build -p vegafusion-server --features=protobuf-src --profile release-opt --target=aarch64-unknown-linux-gnu
+      - name: zip executable
+        uses: vimtor/action-zip@v1.2
+        with:
+          files: target/aarch64-unknown-linux-gnu/release-opt/vegafusion-server
+          dest: vegafusion-server-linux-arm64.zip
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4.4.3
+        with:
+          name: vegafusion-server-linux-arm64
+          path: |
+            vegafusion-server-*
+          retention-days: 1
+          if-no-files-found: error
+
+  build-vegafusion-server-win-64:
+    runs-on: windows-2022
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+      - uses: prefix-dev/setup-pixi@v0.8.9
+      - name: Cache
+        uses: actions/cache@v4
+        with:
+          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-server-win-64b
+          path: |
+            ~/.cargo
+            target
+            .pixi
+      - name: Build vegafusion-server
+        run: |
+          pixi install -vvv
+          pixi run build-rs-server
+      - name: zip executable
+        uses: vimtor/action-zip@v1.2
+        with:
+          files: target/release-opt/vegafusion-server.exe
+          dest: vegafusion-server-win-64.zip
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4.4.3
+        with:
+          name: vegafusion-server-win-64
+          path: |
+            vegafusion-server-*
+          retention-days: 1
+          if-no-files-found: error
+
+  build-vegafusion-server-osx-64:
+    runs-on: macos-13
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+      - uses: prefix-dev/setup-pixi@v0.8.9
+      - name: Cache
+        uses: actions/cache@v4
+        with:
+          key: ${{ runner.os }}-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-server-osx-64
+          path: |
+            ~/.cargo
+            target
+            .pixi
+      - name: Build vegafusion-server
+        run: |
+          pixi run build-rs-server
+      - name: zip executable
+        uses: vimtor/action-zip@v1.2
+        with:
+          files: target/release-opt/vegafusion-server
+          dest: vegafusion-server-osx-64.zip
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4.4.3
+        with:
+          name: vegafusion-server-osx-64
+          path: |
+            vegafusion-server-*
+          retention-days: 1
+          if-no-files-found: error
+
+  build-vegafusion-server-osx-arm64:
+    runs-on: macos-14
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4.2.2
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+          targets: aarch64-apple-darwin
+      - uses: prefix-dev/setup-pixi@v0.8.9
+      - name: Cache
+        uses: actions/cache@v4
+        with:
+          key: osx-arm64-${{ hashFiles('pixi.lock', 'Cargo.lock', 'vegafusion-python/pyproject.toml') }}-build-vegafusion-server-osx-arm64
+          path: |
+            ~/.cargo
+            target
+            .pixi
+      - name: Build vegafusion-server
+        run: |
+          pixi run build-rs-server --target aarch64-apple-darwin
+      - name: zip executable
+        uses: vimtor/action-zip@v1.2
+        with:
+          files: target/aarch64-apple-darwin/release-opt/vegafusion-server
+          dest: vegafusion-server-osx-arm64.zip
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4.4.3
+        with:
+          name: vegafusion-server-osx-arm64
+          path: |
+            vegafusion-server-*
+          retention-days: 1
+          if-no-files-found: error
+
+  # Bundle all server builds into a single artifact for easier downloading
+  bundle-vegafusion-server-builds:
+    needs:
+      [
+        build-vegafusion-server-linux-64,
+        build-vegafusion-server-linux-arm64,
+        build-vegafusion-server-win-64,
+        build-vegafusion-server-osx-64,
+        build-vegafusion-server-osx-arm64,
+      ]
+    runs-on: ubuntu-latest
+    steps:
+      - name: Merge Server Artifacts
+        uses: actions/upload-artifact/merge@v4.4.3
+        with:
+          name: vegafusion-server-all
+          pattern: vegafusion-server-*
+          delete-merged: true
+          separate-directories: false
+
+  # Bundle all python wheels into a single artifact for easier downloading
+  bundle-vegafusion-python-wheels:
+    needs: [
+        # jobs that build but don't have test dependencies
+        build-vegafusion-python-linux-arm64,
+        build-vegafusion-python-osx-64,
+
+        # jobs that test the wheels
+        test-vegafusion-python-linux-64,
+        test-vegafusion-python-osx-arm64,
+        test-vegafusion-python-win-64,
+      ]
+    runs-on: ubuntu-latest
+    steps:
+      - name: Merge Wheel Artifacts
+        uses: actions/upload-artifact/merge@v4.4.3
+        with:
+          name: vegafusion-python-wheels-all
+          pattern: python-wheels-*
+          delete-merged: true
+          separate-directories: false
diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml
deleted file mode 100644
index 2962f9f32..000000000
--- a/.github/workflows/pr.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-name: 'PR checks'
-on:
-  workflow_dispatch:
-  pull_request:
-    types: [opened, synchronize]
-
-permissions:
-  contents: read
-  actions: write
-
-jobs:
-  build-and-test:
-    uses: ./.github/workflows/build-and-test.yml
-    with:
-      build-all-platforms: false
-    permissions:
-      contents: read
-      actions: write
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
deleted file mode 100644
index 20b98b3c5..000000000
--- a/.github/workflows/release.yml
+++ /dev/null
@@ -1,64 +0,0 @@
-name: 'Release'
-on:
-  release:
-    types: [published]
-    
-permissions:
-  contents: read
-
-jobs:
-  build-and-test:
-    uses: ./.github/workflows/build-and-test.yml
-    permissions:
-      contents: read
-      actions: write
-
-  attach-wheels-to-release:
-    name: Attach wheels to GitHub Release
-    runs-on: ubuntu-latest
-    needs: [build-and-test]
-    permissions:
-      contents: write
-      actions: read
-    steps:
-      - name: Download all wheel artifacts
-        uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
-        with:
-          pattern: python-wheels-*
-          path: wheels/
-          merge-multiple: true
-      - name: List downloaded wheels
-        run: ls -lR wheels/
-      - name: Upload wheels to release
-        env:
-          GH_TOKEN: ${{ github.token }}
-          GH_REPO: ${{ github.repository }}
-          RELEASE_TAG: ${{ github.ref_name }}
-        run: |
-          gh release upload "$RELEASE_TAG" wheels/* --clobber
-
-  publish-to-pypi:
-    name: Publish Python wheels to PyPI
-    runs-on: ubuntu-latest
-    needs: [build-and-test]
-    environment:
-      name: pypi
-      url: https://pypi.org/p/deepnote-vegafusion
-    permissions:
-      id-token: write
-      contents: read
-      actions: read
-    steps:
-      - name: Download all wheel artifacts
-        uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
-        with:
-          pattern: python-wheels-*
-          path: dist/
-          merge-multiple: true
-      - name: List files to be uploaded
-        run: ls -lR dist/
-      - name: Publish to PyPI
-        uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # v1.13.0
-        with:
-          skip-existing: true
-          verbose: true
diff --git a/Cargo.lock b/Cargo.lock
index 6c1939582..2641d6fec 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -4047,6 +4047,7 @@ dependencies = [
 name = "rust-examples"
 version = "0.1.0"
 dependencies = [
+ "async-trait",
  "datafusion",
  "reqwest 0.12.22",
  "serde_json",
@@ -5027,7 +5028,7 @@ dependencies = [
 
 [[package]]
 name = "vegafusion"
-version = "2.0.8"
+version = "2.0.3"
 dependencies = [
  "arrow",
  "async-trait",
@@ -5052,7 +5053,7 @@ dependencies = [
 
 [[package]]
 name = "vegafusion-common"
-version = "2.0.8"
+version = "2.0.3"
 dependencies = [
  "ahash",
  "arrow",
@@ -5076,7 +5077,7 @@ dependencies = [
 
 [[package]]
 name = "vegafusion-core"
-version = "2.0.8"
+version = "2.0.3"
 dependencies = [
  "ahash",
  "async-trait",
@@ -5108,7 +5109,7 @@ dependencies = [
 
 [[package]]
 name = "vegafusion-runtime"
-version = "2.0.8"
+version = "2.0.3"
 dependencies = [
  "async-lock",
  "async-mutex",
@@ -5130,7 +5131,6 @@ dependencies = [
  "datafusion-optimizer",
  "datafusion-physical-expr",
  "datafusion-proto",
- "datafusion-sql",
  "env_logger",
  "float-cmp",
  "futures",
@@ -5165,7 +5165,7 @@ dependencies = [
 
 [[package]]
 name = "vegafusion-server"
-version = "2.0.8"
+version = "2.0.3"
 dependencies = [
  "assert_cmd",
  "clap",
@@ -5189,7 +5189,7 @@ dependencies = [
 
 [[package]]
 name = "vegafusion-wasm"
-version = "2.0.8"
+version = "2.0.3"
 dependencies = [
  "async-trait",
  "chrono",
diff --git a/examples/python-examples/pre_transform_spec_vendor.py b/examples/python-examples/pre_transform_spec_vendor.py
deleted file mode 100644
index a04ed0e5f..000000000
--- a/examples/python-examples/pre_transform_spec_vendor.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import pandas as pd
-import pyarrow as pa
-from typing import Any
-
-try:
-    from pyspark.sql import SparkSession
-except ImportError:
-    print("PySpark not available. Please install pyspark to run this example.")
-    print("pip install pyspark")
-    raise
-
-import vegafusion as vf
-
-
-def create_spark_executor_for_session(spark_session: SparkSession):
-    """Create a Spark executor function that executes SQL queries."""
-
-    def spark_executor(sql_query: str) -> pa.Table:
-        """Execute SQL query using Spark and return Arrow table."""
-        print("🔥 Executing Spark SQL:")
-        print(f"   {sql_query}")
-        print("-" * 60)
-
-        spark_df = spark_session.sql(sql_query)
-
-        pandas_df = spark_df.toPandas()
-        print("Got response from Spark, rows:", len(pandas_df))
-        arrow_table = pa.Table.from_pandas(pandas_df)
-        return arrow_table
-
-    return spark_executor
-
-
-def setup_spark_session() -> SparkSession:
-    """Initialize a local Spark session with appropriate configuration."""
-    print("🚀 Setting up Spark session...")
-
-    spark = (
-        SparkSession.builder.appName("vegafusion-spec-vendor-example")
-        .config("spark.sql.execution.arrow.pyspark.enabled", "true")
-        .config("spark.sql.execution.arrow.pyspark.fallback.enabled", "true")
-        .config("spark.executor.memory", "2g")
-        .config("spark.driver.memory", "2g")
-        .master("local[2]")
-        .getOrCreate()
-    )
-
-    # Set timezone to UTC for consistency
-    spark.sql("SET TIME ZONE 'UTC'")
-    print("✅ Spark session created successfully")
-
-    return spark
-
-
-def load_and_register_movies_data(spark: SparkSession) -> pa.Schema:
-    """Load movies data and register it as a Spark table."""
-    print("📊 Loading movies data...")
-
-    movies_url = "https://raw.githubusercontent.com/vega/vega-datasets/refs/heads/main/data/movies.json"
-    movies_df = pd.read_json(movies_url)
-
-    print(f"✅ Loaded {len(movies_df)} movies")
-    print(f"   Columns: {list(movies_df.columns)}")
-
-    for col in movies_df.columns:
-        if movies_df[col].dtype == "object":
-            non_null_values = movies_df[col].dropna()
-            if len(non_null_values) > 0:
-                try:
-                    pd.to_numeric(non_null_values, errors="raise")
-                    movies_df[col] = pd.to_numeric(movies_df[col], errors="coerce")
-                except (ValueError, TypeError):
-                    movies_df[col] = movies_df[col].astype("string")
-
-    spark_df = spark.createDataFrame(movies_df)
-
-    spark_df.createOrReplaceTempView("movies")
-    print("✅ Movies data registered as 'movies' table in Spark")
-
-    pandas_df_clean = spark_df.toPandas()
-    arrow_schema = pa.Schema.from_pandas(pandas_df_clean)
-    return arrow_schema
-
-
-def main():
-    print("=" * 80)
-    print("VegaFusion pre_transform_spec_vendor Example with Apache Spark")
-    print("=" * 80)
-
-    spark = setup_spark_session()
-
-    try:
-        movies_schema = load_and_register_movies_data(spark)
-
-        spark_executor = create_spark_executor_for_session(spark)
-
-        spec = get_spec()
-
-        print("\n🔧 Running pre_transform_spec_vendor...")
-        print(f"   Using Arrow schema with {len(movies_schema)} fields:")
-        for field in movies_schema:
-            print(f"     - {field.name}: {field.type}")
-
-        # Build a vendor-specific runtime with the provided Spark executor
-        runtime = vf.VegaFusionRuntime.new_vendor(
-            vendor="sparksql",
-            executor=spark_executor,
-        )
-
-        transformed_spec, warnings = runtime.pre_transform_spec(
-            spec=spec,
-            local_tz="UTC",
-            preserve_interactivity=False,
-            inline_datasets={"movies": movies_schema},
-        )
-
-        print("\n✅ Transformation completed!")
-        print(f"   Warnings: {len(warnings)}")
-
-        if warnings:
-            print("⚠️  Warnings:")
-            for warning in warnings:
-                print(f"   - {warning['type']}: {warning['message']}")
-
-        print("\n📋 Transformed specification:")
-        print(f"   Data sources: {len(transformed_spec.get('data', []))}")
-
-        for data_source in transformed_spec.get("data", []):
-            if "values" in data_source:
-                values = data_source["values"]
-                print(f"   - '{data_source['name']}': {len(values)} inline rows")
-            elif "url" in data_source:
-                print(
-                    f"   - '{data_source['name']}': external URL ({data_source['url']})"
-                )
-
-        print("\n🎯 Example completed successfully!")
-
-    finally:
-        print("\n🧹 Stopping Spark session...")
-        spark.stop()
-        print("✅ Cleanup completed")
-
-
-def get_spec() -> dict[str, Any]:
-    """
-    Return a Vega specification that creates a histogram of IMDB ratings.
-    Based on https://vega.github.io/editor/#/examples/vega/histogram-null-values
-    """
-    return {
-        "$schema": "https://vega.github.io/schema/vega/v5.json",
-        "description": "A histogram of film IMDB ratings from the movies dataset.",
-        "width": 500,
-        "height": 300,
-        "padding": 5,
-        "autosize": {"type": "fit", "resize": True},
-        "signals": [
-            {
-                "name": "maxbins",
-                "value": 15,
-                "bind": {"input": "select", "options": [5, 10, 15, 20]},
-            },
-            {"name": "binCount", "update": "(bins.stop - bins.start) / bins.step"},
-            {"name": "nullGap", "value": 10},
-            {"name": "barStep", "update": "(width - nullGap) / (1 + binCount)"},
-        ],
-        "data": [
-            {
-                "name": "movies_table",
-                "url": "vegafusion+dataset://movies",
-                "transform": [
-                    {"type": "extent", "field": "IMDB Rating", "signal": "extent"},
-                    {
-                        "type": "bin",
-                        "signal": "bins",
-                        "field": "IMDB Rating",
-                        "extent": {"signal": "extent"},
-                        "maxbins": {"signal": "maxbins"},
-                    },
-                ],
-            },
-            {
-                "name": "rating_counts",
-                "source": "movies_table",
-                "transform": [
-                    {"type": "filter", "expr": "datum['IMDB Rating'] != null"},
-                    {"type": "aggregate", "groupby": ["bin0", "bin1"]},
-                ],
-            },
-            {
-                "name": "null_ratings",
-                "source": "movies_table",
-                "transform": [
-                    {"type": "filter", "expr": "datum['IMDB Rating'] == null"},
-                    {"type": "aggregate", "groupby": []},
-                ],
-            },
-            {
-                "name": "genre_summary",
-                "source": "movies_table",
-                "transform": [
-                    {"type": "filter", "expr": "datum['Major Genre'] != null"},
-                    {
-                        "type": "aggregate",
-                        "groupby": ["Major Genre"],
-                        "fields": ["IMDB Rating", "Production Budget"],
-                        "ops": ["mean", "mean"],
-                        "as": ["avg_rating", "avg_budget"],
-                    },
-                    {
-                        "type": "filter",
-                        "expr": "datum.count > 5",
-                    },
-                ],
-            },
-        ],
-        "scales": [
-            {
-                "name": "yscale",
-                "type": "linear",
-                "range": "height",
-                "round": True,
-                "nice": True,
-                "domain": {
-                    "fields": [
-                        {"data": "rating_counts", "field": "count"},
-                        {"data": "null_ratings", "field": "count"},
-                    ]
-                },
-            },
-            {
-                "name": "xscale",
-                "type": "linear",
-                "range": [{"signal": "barStep + nullGap"}, {"signal": "width"}],
-                "round": True,
-                "domain": {"signal": "[bins.start, bins.stop]"},
-                "bins": {"signal": "bins"},
-            },
-            {
-                "name": "xscale-null",
-                "type": "band",
-                "range": [0, {"signal": "barStep"}],
-                "round": True,
-                "domain": [None],
-            },
-        ],
-        "axes": [
-            {
-                "orient": "bottom",
-                "scale": "xscale",
-                "title": "IMDB Rating",
-                "tickMinStep": 0.5,
-            },
-            {"orient": "bottom", "scale": "xscale-null", "title": "Null Values"},
-            {
-                "orient": "left",
-                "scale": "yscale",
-                "title": "Number of Movies",
-                "tickCount": 5,
-                "offset": 5,
-            },
-        ],
-        "marks": [
-            {
-                "type": "rect",
-                "from": {"data": "rating_counts"},
-                "encode": {
-                    "update": {
-                        "x": {"scale": "xscale", "field": "bin0", "offset": 1},
-                        "x2": {"scale": "xscale", "field": "bin1"},
-                        "y": {"scale": "yscale", "field": "count"},
-                        "y2": {"scale": "yscale", "value": 0},
-                        "fill": {"value": "steelblue"},
-                        "stroke": {"value": "white"},
-                        "strokeWidth": {"value": 1},
-                    },
-                    "hover": {"fill": {"value": "firebrick"}},
-                },
-            },
-            {
-                "type": "rect",
-                "from": {"data": "null_ratings"},
-                "encode": {
-                    "update": {
-                        "x": {"scale": "xscale-null", "value": None, "offset": 1},
-                        "x2": {"scale": "xscale-null", "band": 1},
-                        "y": {"scale": "yscale", "field": "count"},
-                        "y2": {"scale": "yscale", "value": 0},
-                        "fill": {"value": "#aaa"},
-                        "stroke": {"value": "white"},
-                        "strokeWidth": {"value": 1},
-                    },
-                    "hover": {"fill": {"value": "firebrick"}},
-                },
-            },
-        ],
-    }
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/rust-examples/Cargo.toml b/examples/rust-examples/Cargo.toml
index 3805f20c7..e719058e2 100644
--- a/examples/rust-examples/Cargo.toml
+++ b/examples/rust-examples/Cargo.toml
@@ -4,6 +4,7 @@ version = "0.1.0"
 edition = "2021"
 
 [dev-dependencies]
+async-trait = { workspace = true }
 serde_json = { workspace = true }
 vegafusion-common = { path = "../../vegafusion-common" }
 vegafusion-core = { path = "../../vegafusion-core" }
diff --git a/examples/rust-examples/examples/custom_executor.rs b/examples/rust-examples/examples/custom_executor.rs
new file mode 100644
index 000000000..717747867
--- /dev/null
+++ b/examples/rust-examples/examples/custom_executor.rs
@@ -0,0 +1,140 @@
+use datafusion::prelude::SessionContext;
+use std::sync::Arc;
+use vegafusion_common::data::table::VegaFusionTable;
+use vegafusion_common::datafusion_expr::LogicalPlan;
+use vegafusion_common::error::Result;
+use vegafusion_core::runtime::{PlanExecutor, VegaFusionRuntimeTrait};
+use vegafusion_core::spec::chart::ChartSpec;
+use vegafusion_runtime::datafusion::context::make_datafusion_context;
+use vegafusion_runtime::plan_executor::DataFusionPlanExecutor;
+use vegafusion_runtime::task_graph::runtime::VegaFusionRuntime;
+
+/// A custom executor that logs plan execution and forwards to DataFusion
+#[derive(Clone)]
+struct LoggingExecutor {
+    fallback: Arc<DataFusionPlanExecutor>,
+}
+
+impl LoggingExecutor {
+    fn new(ctx: Arc<SessionContext>) -> Self {
+        Self {
+            fallback: Arc::new(DataFusionPlanExecutor::new(ctx)),
+        }
+    }
+}
+
+#[async_trait::async_trait]
+impl PlanExecutor for LoggingExecutor {
+    async fn execute_plan(&self, plan: LogicalPlan) -> Result<VegaFusionTable> {
+        println!("Custom executor received logical plan");
+        println!("Plan details:\n{}\n", plan.display_indent());
+
+        // Forward to DataFusion for actual execution
+        let result = self.fallback.execute_plan(plan).await?;
+
+        println!(
+            "Custom executor executed plan, returned {} rows\n",
+            result.num_rows()
+        );
+
+        Ok(result)
+    }
+}
+
+/// This example demonstrates how to use a custom plan executor with VegaFusion.
+/// The custom executor logs each plan execution before forwarding to DataFusion.
+#[tokio::main]
+async fn main() {
+    let spec = get_spec();
+
+    // Create a custom executor
+    let ctx = Arc::new(make_datafusion_context());
+    let custom_executor = Arc::new(LoggingExecutor::new(ctx)) as Arc<dyn PlanExecutor>;
+
+    // Create runtime with custom executor
+    let runtime = VegaFusionRuntime::new(None, Some(custom_executor));
+
+    println!("Starting pre-transform with custom executor\n");
+
+    let (_transformed_spec, warnings) = runtime
+        .pre_transform_spec(
+            &spec,
+            &Default::default(), // Inline datasets
+            &Default::default(), // Options
+        )
+        .await
+        .unwrap();
+    println!("Spec transformed");
+    assert_eq!(warnings.len(), 0);
+}
+
+fn get_spec() -> ChartSpec {
+    let spec_str = r##"
+    {
+      "$schema": "https://vega.github.io/schema/vega/v5.json",
+      "description": "A histogram demonstrating custom executor usage",
+      "width": 400,
+      "height": 200,
+      "padding": 5,
+      "data": [
+        {
+          "name": "table",
+          "url": "data/movies.json",
+          "transform": [
+            {
+              "type": "extent", 
+              "field": "IMDB Rating",
+              "signal": "extent"
+            },
+            {
+              "type": "bin", 
+              "signal": "bins",
+              "field": "IMDB Rating", 
+              "extent": {"signal": "extent"},
+              "maxbins": 10
+            },
+            {
+              "type": "aggregate",
+              "groupby": ["bin0", "bin1"],
+              "ops": ["count"],
+              "fields": [null],
+              "as": ["count"]
+            }
+          ]
+        }
+      ],
+      "scales": [
+        {
+          "name": "xscale",
+          "type": "linear",
+          "range": "width",
+          "domain": {"signal": "extent"}
+        },
+        {
+          "name": "yscale",
+          "type": "linear",
+          "range": "height",
+          "round": true,
+          "domain": {"data": "table", "field": "count"},
+          "zero": true,
+          "nice": true
+        }
+      ],
+      "marks": [
+        {
+          "type": "rect",
+          "from": {"data": "table"},
+          "encode": {
+            "update": {
+              "x": {"scale": "xscale", "field": "bin0"},
+              "x2": {"scale": "xscale", "field": "bin1"},
+              "y": {"scale": "yscale", "field": "count"},
+              "y2": {"scale": "yscale", "value": 0}
+            }
+          }
+        }
+      ]
+    }
+    "##;
+    serde_json::from_str(spec_str).unwrap()
+}
diff --git a/pixi.lock b/pixi.lock
index 11f615746..82cf9e5e2 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -204,7 +204,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/nodejs-20.5.1-hb753e55_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py310hb13e2d6_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py310hefbff90_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openjdk-20.0.0-hfea2f88_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.2-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.2-he039a57_2.conda
@@ -232,14 +232,12 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd3deb0d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/py4j-0.10.9.7-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-16.1.0-py310hb7f781d_6.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-16.1.0-py310hac404ae_6_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.16.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pyspark-3.5.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-cov-4.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.10.15-h4a871b0_2_cpython.conda
@@ -509,7 +507,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/nodejs-20.5.1-h119ffd7_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.26.4-py310h4bfa8fc_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-2.2.6-py310h07c5b4d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/openjdk-20.0.0-h7d26f99_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/openssl-3.3.2-hd23fc13_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/orc-2.0.2-hb8ce1e1_2.conda
@@ -534,7 +532,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/psygnal-0.11.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd3deb0d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/py4j-0.10.9.7-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-64/pyarrow-16.1.0-py310h58fd45c_6.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/pyarrow-core-16.1.0-py310h86202ae_6_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda
@@ -543,7 +540,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/pyobjc-core-10.3.1-py310h1c7075f_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/pyobjc-framework-cocoa-10.3.1-py310h1c7075f_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pyspark-3.5.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-cov-4.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.10.15-hd8744da_2_cpython.conda
@@ -823,7 +819,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/nodejs-20.5.1-ha2ed473_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.26.4-py310hd45542a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.2.6-py310h4d83441_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openjdk-20.0.0-hbe7ddab_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.3.2-h8359307_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.1-h47ade37_1.conda
@@ -852,7 +848,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/psygnal-0.11.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd3deb0d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/py4j-0.10.9.7-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-16.1.0-py310h24597f5_4.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-16.1.0-py310h2e300fa_4_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda
@@ -861,7 +856,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyobjc-core-10.3.1-py310hb3dec1a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyobjc-framework-cocoa-10.3.1-py310hb3dec1a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pyspark-3.5.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-cov-4.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.10.15-hdce6c4c_2_cpython.conda
@@ -1103,7 +1097,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/nodejs-20.5.1-h57928b3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-1.26.4-py310hf667824_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.6-py310h4987827_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/openjdk-20.0.0-h57928b3_2.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.2-h2466b09_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.0.2-h34659fe_2.conda
@@ -1126,14 +1120,12 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/psutil-5.9.5-py310h8d17308_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/psygnal-0.11.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/py4j-0.10.9.7-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-16.1.0-py310h05ea346_6.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-16.1.0-py310h399dd74_6_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.16.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyh0701188_6.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pyspark-3.5.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-cov-4.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.10.15-hfaddaf0_2_cpython.conda
@@ -7479,15 +7471,16 @@ packages:
   - pkg:pypi/notebook-shim?source=hash-mapping
   size: 16880
   timestamp: 1707957948029
-- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py310hb13e2d6_0.conda
-  sha256: 028fe2ea8e915a0a032b75165f11747770326f3d767e642880540c60a3256425
-  md5: 6593de64c935768b6bad3e19b3e978be
+- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py310hefbff90_0.conda
+  sha256: 0ba94a61f91d67413e60fa8daa85627a8f299b5054b0eff8f93d26da83ec755e
+  md5: b0cea2c364bf65cd19e023040eeab05d
   depends:
+  - __glibc >=2.17,<3.0.a0
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
-  - libgcc-ng >=12
+  - libgcc >=13
   - liblapack >=3.9.0,<4.0a0
-  - libstdcxx-ng >=12
+  - libstdcxx >=13
   - python >=3.10,<3.11.0a0
   - python_abi 3.10.* *_cp310
   constrains:
@@ -7496,15 +7489,16 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=hash-mapping
-  size: 7009070
-  timestamp: 1707225917496
-- conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.26.4-py310h4bfa8fc_0.conda
-  sha256: 914476e2d3273fdf9c0419a7bdcb7b31a5ec25949e4afbc847297ff3a50c62c8
-  md5: cd6a2298387f558c9ea70ee73a189791
+  size: 7893263
+  timestamp: 1747545075833
+- conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-2.2.6-py310h07c5b4d_0.conda
+  sha256: f1851c5726ff1a4de246e385ba442d749a68ef39316c834933ee9b980dbe62df
+  md5: d79253493dcc76b95221588b98e1eb3c
   depends:
+  - __osx >=10.13
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
-  - libcxx >=16
+  - libcxx >=18
   - liblapack >=3.9.0,<4.0a0
   - python >=3.10,<3.11.0a0
   - python_abi 3.10.* *_cp310
@@ -7514,15 +7508,16 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=hash-mapping
-  size: 6491938
-  timestamp: 1707226191321
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.26.4-py310hd45542a_0.conda
-  sha256: e3078108a4973e73c813b89228f4bd8095ec58f96ca29f55d2e45a6223a9a1db
-  md5: 267ee89a3a0b8c8fa838a2353f9ea0c0
+  size: 6988856
+  timestamp: 1747545137089
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.2.6-py310h4d83441_0.conda
+  sha256: 87704bcd5f4a4f88eaf2a97f07e9825803b58a8003a209b91e89669317523faf
+  md5: f4bd8ac423d04b3c444b96f2463d3519
   depends:
+  - __osx >=11.0
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
-  - libcxx >=16
+  - libcxx >=18
   - liblapack >=3.9.0,<4.0a0
   - python >=3.10,<3.11.0a0
   - python >=3.10,<3.11.0a0 *_cpython
@@ -7533,11 +7528,11 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=hash-mapping
-  size: 5475744
-  timestamp: 1707226187124
-- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-1.26.4-py310hf667824_0.conda
-  sha256: 20ca447a8f840c01961f2bdf0847fc7b7785a62968e867d7aa4ca8a66d70f9ad
-  md5: 93e881c391880df90e74e43a4b67c16d
+  size: 5841650
+  timestamp: 1747545043441
+- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.6-py310h4987827_0.conda
+  sha256: 6f628e51763b86a535a723664e3aa1e38cb7147a2697f80b75c1980c1ed52f3e
+  md5: d2596785ac2cf5bab04e2ee9e5d04041
   depends:
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
@@ -7553,8 +7548,8 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=hash-mapping
-  size: 5977469
-  timestamp: 1707226445438
+  size: 6596153
+  timestamp: 1747545352390
 - conda: https://conda.anaconda.org/conda-forge/linux-64/openjdk-20.0.0-hfea2f88_2.conda
   sha256: a67450cb4bbffd084c407da95fb0c7a0d845cdecd412b9d9b0793634e1cdddc2
   md5: b1d60ca9e1cf8c82214380fc8a8582e0
@@ -8371,17 +8366,6 @@ packages:
   - pkg:pypi/pure-eval?source=hash-mapping
   size: 16551
   timestamp: 1721585805256
-- conda: https://conda.anaconda.org/conda-forge/noarch/py4j-0.10.9.7-pyhd8ed1ab_0.tar.bz2
-  sha256: 5a1d134f58dbc2c77b7985069a5485fe4aa09d49f1b545087913af62559ff738
-  md5: 0f01a52cf858aef86632a8ab08011c0c
-  depends:
-  - python >=3.6
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/py4j?source=hash-mapping
-  size: 186208
-  timestamp: 1660381672873
 - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-16.1.0-py310hb7f781d_6.conda
   sha256: d417cf50d3090f22ad63f7641fb4d4efc6fd1cb77d2412fac4649b801b9bf585
   md5: fdb4acd5dd835ffadde6a8fffe4dff6c
@@ -8669,21 +8653,6 @@ packages:
   - pkg:pypi/pysocks?source=hash-mapping
   size: 18981
   timestamp: 1661604969727
-- conda: https://conda.anaconda.org/conda-forge/noarch/pyspark-3.5.5-pyhd8ed1ab_0.conda
-  sha256: 0da96ab21bc215a05f78788fc8bcee425dada013307967cc32d240ef51d47fbf
-  md5: 1216c9ab11a8116330554bf767a0c600
-  depends:
-  - numpy >=1.15,<2
-  - pandas >=1.0.5
-  - py4j 0.10.9.7
-  - pyarrow >=4.0.0
-  - python >=3.9
-  license: Apache-2.0
-  license_family: APACHE
-  purls:
-  - pkg:pypi/pyspark?source=hash-mapping
-  size: 311167998
-  timestamp: 1740719235517
 - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.3-pyhd8ed1ab_0.conda
   sha256: e99376d0068455712109d233f5790458ff861aeceb458bfda74e353338e4d815
   md5: c03d61f31f38fdb9facf70c29958bf7a
diff --git a/pixi.toml b/pixi.toml
index 243dbb07b..d94f2e2aa 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -120,7 +120,6 @@ nbval = "0.9.6.*"
 selenium = "4.11.2.*"
 toml = "0.10.2.*"
 pytest = ">=4.6"
-pyspark = "3.5.*"
 click = "8.1.6.*"
 python-duckdb = ">=1.1.3,<2"
 pip = "23.2.1.*"
diff --git a/vegafusion-common/Cargo.toml b/vegafusion-common/Cargo.toml
index 41967a6f3..0a28a8533 100644
--- a/vegafusion-common/Cargo.toml
+++ b/vegafusion-common/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "vegafusion-common"
-version = "2.0.8"
+version = "2.0.3"
 edition = "2021"
 description = "Common components required by multiple VegaFusion crates"
 license = "BSD-3-Clause"
diff --git a/vegafusion-common/src/data/table.rs b/vegafusion-common/src/data/table.rs
index a5810e831..f456538d5 100644
--- a/vegafusion-common/src/data/table.rs
+++ b/vegafusion-common/src/data/table.rs
@@ -342,8 +342,8 @@ impl VegaFusionTable {
             let hash = vf_table.get_hash();
 
             // Now rechunk for better multithreaded efficiency with DataFusion
-            let seq = PyList::new(py, vec![("max_chunksize", 8096)])?;
-            let kwargs = PyDict::from_sequence(seq.as_any())?;
+            let kwargs = PyDict::new(py);
+            kwargs.set_item("max_chunksize", 8096)?;
 
             let rechunked_table = data
                 .call_method("rechunk", (), Some(&kwargs))?
diff --git a/vegafusion-common/src/error.rs b/vegafusion-common/src/error.rs
index 4c7d1647d..4640174b4 100644
--- a/vegafusion-common/src/error.rs
+++ b/vegafusion-common/src/error.rs
@@ -65,9 +65,6 @@ pub enum VegaFusionError {
     #[error("Arrow error: {0}\n{1}")]
     ArrowError(ArrowError, ErrorContext),
 
-    #[error("Vendor error: {0}\n{1}")]
-    VendorError(String, ErrorContext),
-
     #[error("DataFusion error: {0}\n{1}")]
     DataFusionError(DataFusionError, ErrorContext),
 
@@ -156,10 +153,6 @@ impl VegaFusionError {
                 context.contexts.push(context_fn().into());
                 VegaFusionError::DataFusionError(err, context)
             }
-            VendorError(msg, mut context) => {
-                context.contexts.push(context_fn().into());
-                VegaFusionError::VendorError(msg, context)
-            }
             #[cfg(feature = "proto")]
             DataFusionProtoError(err, mut context) => {
                 context.contexts.push(context_fn().into());
@@ -207,10 +200,6 @@ impl VegaFusionError {
         }
     }
 
-    pub fn vendor<S: Into<String>>(message: S) -> Self {
-        Self::VendorError(message.into(), Default::default())
-    }
-
     pub fn parse<S: Into<String>>(message: S) -> Self {
         Self::ParseError(message.into(), Default::default())
     }
@@ -270,7 +259,6 @@ impl VegaFusionError {
             DataFusionError(err, context) => {
                 VegaFusionError::ExternalError(err.to_string(), context.clone())
             }
-            VendorError(msg, context) => VegaFusionError::VendorError(msg.clone(), context.clone()),
             #[cfg(feature = "proto")]
             DataFusionProtoError(err, context) => {
                 VegaFusionError::ExternalError(err.to_string(), context.clone())
diff --git a/vegafusion-core/Cargo.toml b/vegafusion-core/Cargo.toml
index 89538ddcb..6ae3274b4 100644
--- a/vegafusion-core/Cargo.toml
+++ b/vegafusion-core/Cargo.toml
@@ -2,7 +2,7 @@
 name = "vegafusion-core"
 license = "BSD-3-Clause"
 edition = "2021"
-version = "2.0.8"
+version = "2.0.3"
 description = "Core components required by multiple VegaFusion crates, with WASM compatibility"
 
 [features]
@@ -48,7 +48,7 @@ workspace = true
 [dependencies.vegafusion-common]
 path = "../vegafusion-common"
 features = ["json", "sqlparser"]
-version = "2.0.8"
+version = "2.0.3"
 
 [dependencies.datafusion-common]
 workspace = true
diff --git a/vegafusion-python/Cargo.toml b/vegafusion-python/Cargo.toml
index 7c941d515..9a342f0b5 100644
--- a/vegafusion-python/Cargo.toml
+++ b/vegafusion-python/Cargo.toml
@@ -2,7 +2,7 @@
 name = "vegafusion"
 license = "BSD-3-Clause"
 edition = "2021"
-version = "2.0.8"
+version = "2.0.3"
 description = "VegaFusion Python interface"
 
 [lib]
@@ -52,16 +52,16 @@ workspace = true
 [dependencies.vegafusion-common]
 path = "../vegafusion-common"
 features = ["pyo3", "base64"]
-version = "2.0.8"
+version = "2.0.3"
 
 [dependencies.vegafusion-core]
 path = "../vegafusion-core"
 features = ["py", "tonic_support"]
-version = "2.0.8"
+version = "2.0.3"
 
 [dependencies.vegafusion-runtime]
 path = "../vegafusion-runtime"
-version = "2.0.8"
+version = "2.0.3"
 
 [dependencies.datafusion]
 workspace = true
diff --git a/vegafusion-python/checks/README.md b/vegafusion-python/checks/README.md
new file mode 100644
index 000000000..5a04f9e7c
--- /dev/null
+++ b/vegafusion-python/checks/README.md
@@ -0,0 +1,4 @@
+## Checks
+Python scripts to run on CI that should not be run as part of the pytest test suite.
+In particular, to check lazy imports.
+
diff --git a/vegafusion-python/checks/check_lazy_imports.py b/vegafusion-python/checks/check_lazy_imports.py
new file mode 100644
index 000000000..3ebf86311
--- /dev/null
+++ b/vegafusion-python/checks/check_lazy_imports.py
@@ -0,0 +1,70 @@
+import sys
+from pathlib import Path
+
+from packaging.version import Version
+
+root = Path(__file__).parent.parent.parent
+
+
+if __name__ == "__main__":
+    # Make sure the prominant dependencies are not loaded on import
+    # Check narwhals version to see if we should skip pandas/pyarrow checks
+    import narwhals
+    import vegafusion as vf  # noqa: F401
+
+    narwhals_version = Version(narwhals.__version__)
+    skip_eager_import_check = narwhals_version >= Version("1.43.0")
+
+    for mod in ["polars", "pandas", "pyarrow", "duckdb", "altair"]:
+        if mod in ["pandas", "pyarrow"] and skip_eager_import_check:
+            # Skip pandas/pyarrow check for narwhals >= 1.43.0 as it may import
+            # them eagerly. This appears to be a regression or change in behavior in
+            # narwhals 1.43.0. We should investigate more and potentially report
+            # upstream.
+            print(
+                f"WARNING: Skipping {mod} lazy import check for narwhals "
+                "{narwhals.__version__}"
+            )
+            continue
+        assert mod not in sys.modules, f"{mod} module should be imported lazily"
+
+    # Create an altair chart with polars and check that pandas and pyarrow are
+    # not loaded
+    import altair as alt
+    import polars as pl
+
+    cars = pl.read_json(
+        root / "vegafusion-runtime/tests/util/vegajs_runtime/data/cars.json"
+    )
+
+    # Build a histogram of horsepower
+    chart = (
+        alt.Chart(cars)
+        .mark_bar()
+        .encode(
+            alt.X("Horsepower:Q", bin=True),
+            y="count()",
+        )
+    )
+
+    # Check that the transformed data is a polars DataFrame
+    transformed = chart.transformed_data()
+    assert isinstance(transformed, pl.DataFrame)
+    assert len(transformed["bin_maxbins_10_Horsepower"]) == 10
+
+    # Do a full pre-transform of the spec
+    transformed_spec = chart.to_dict(format="vega")
+    assert isinstance(transformed_spec, dict)
+    assert "data" in transformed_spec
+
+    # Make sure that pandas and pyarrow were not loaded when using polars
+    for mod in ["pandas", "pyarrow", "duckdb"]:
+        if mod in ["pandas", "pyarrow"] and skip_eager_import_check:
+            # Skip pandas/pyarrow check for narwhals >= 1.43.0 as it may import
+            # them eagerly
+            print(
+                f"WARNING: Skipping {mod} lazy import check for narwhals "
+                "{narwhals.__version__}"
+            )
+            continue
+        assert mod not in sys.modules, f"{mod} module should be imported lazily"
diff --git a/vegafusion-python/pyproject.toml b/vegafusion-python/pyproject.toml
index 41e9a0c8e..1abf4ec8f 100644
--- a/vegafusion-python/pyproject.toml
+++ b/vegafusion-python/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
-name = "deepnote_vegafusion"
-version = "2.0.8"
+name = "vegafusion"
+version = "2.0.3"
 description = "Core tools for using VegaFusion from Python"
 readme = "README.md"
 requires-python = ">=3.9"
@@ -14,12 +14,7 @@ classifiers = [
   "License :: OSI Approved :: BSD License",
   "Topic :: Scientific/Engineering :: Visualization",
 ]
-dependencies = [
-  "arro3-core",
-  "packaging",
-  "narwhals>=1.42",
-  "datafusion>=49.0.0,<50.0.0",
-]
+dependencies = ["arro3-core", "packaging", "narwhals>=1.42"]
 [[project.authors]]
 name = "VegaFusion Contributors"
 
@@ -32,7 +27,7 @@ file = "LICENSE"
 
 [project.urls]
 Homepage = "https://vegafusion.io"
-Repository = "https://github.com/deepnote/vegafusion"
+Repository = "https://github.com/hex-inc/vegafusion"
 Documentation = "https://vegafusion.io"
 
 [project.optional-dependencies]
diff --git a/vegafusion-python/src/lib.rs b/vegafusion-python/src/lib.rs
index be08d07b3..525742789 100644
--- a/vegafusion-python/src/lib.rs
+++ b/vegafusion-python/src/lib.rs
@@ -1,7 +1,5 @@
 mod chart_state;
 mod utils;
-mod vendor;
-
 use lazy_static::lazy_static;
 use pyo3::exceptions::PyValueError;
 use pyo3::prelude::*;
@@ -37,7 +35,6 @@ use vegafusion_runtime::task_graph::cache::VegaFusionCache;
 
 use crate::chart_state::PyChartState;
 use crate::utils::{parse_json_spec, process_inline_datasets};
-use crate::vendor::select_executor_for_vendor;
 
 static INIT: Once = Once::new();
 
@@ -102,19 +99,6 @@ impl PyVegaFusionRuntime {
         Self::build_with_executor(max_capacity, memory_limit, worker_threads, None)
     }
 
-    #[staticmethod]
-    #[pyo3(signature = (max_capacity=None, memory_limit=None, worker_threads=None, vendor=None, executor=None))]
-    pub fn new_embedded_vendor(
-        max_capacity: Option<usize>,
-        memory_limit: Option<usize>,
-        worker_threads: Option<i32>,
-        vendor: Option<String>,
-        executor: Option<PyObject>,
-    ) -> PyResult<Self> {
-        let rust_executor = select_executor_for_vendor(vendor, executor)?;
-        Self::build_with_executor(max_capacity, memory_limit, worker_threads, rust_executor)
-    }
-
     #[staticmethod]
     pub fn new_grpc(url: &str) -> PyResult<Self> {
         let tokio_runtime = Arc::new(
diff --git a/vegafusion-python/src/vendor.rs b/vegafusion-python/src/vendor.rs
deleted file mode 100644
index f3728d669..000000000
--- a/vegafusion-python/src/vendor.rs
+++ /dev/null
@@ -1,120 +0,0 @@
-use std::sync::Arc;
-
-use async_trait::async_trait;
-use pyo3::exceptions::PyValueError;
-use pyo3::prelude::*;
-use pyo3::types::PyString;
-use tokio::sync::Mutex;
-use vegafusion_common::{data::table::VegaFusionTable, datafusion_expr::LogicalPlan};
-use vegafusion_core::runtime::PlanExecutor;
-use vegafusion_runtime::sql::logical_plan_to_spark_sql;
-
-pub struct SparkSqlPlanExecutor {
-    python_executor: PyObject,
-    mutex: Arc<Mutex<()>>,
-}
-
-impl SparkSqlPlanExecutor {
-    pub fn new(python_executor: PyObject) -> Self {
-        Self {
-            python_executor,
-            mutex: Arc::new(Mutex::new(())),
-        }
-    }
-}
-
-pub fn select_executor_for_vendor(
-    vendor: Option<String>,
-    executor: Option<PyObject>,
-) -> PyResult<Option<Arc<dyn PlanExecutor>>> {
-    match vendor.as_deref() {
-        Some("sparksql") => {
-            let py_exec = executor.ok_or_else(|| {
-                PyValueError::new_err(
-                    "'executor' is required for vendor='sparksql' and must be callable or have execute_plan",
-                )
-            })?;
-
-            Python::with_gil(|py| -> PyResult<()> {
-                let obj_ref = py_exec.bind(py);
-                if obj_ref.is_callable() || obj_ref.hasattr("execute_plan")? {
-                    Ok(())
-                } else {
-                    Err(PyValueError::new_err(
-                        "Executor must be callable or have an execute_plan method",
-                    ))
-                }
-            })?;
-
-            Ok(Some(Arc::new(SparkSqlPlanExecutor::new(py_exec))))
-        }
-        Some("datafusion") | Some("") | None => {
-            // For DataFusion we don't support passing custom executor from Python (due to issues with
-            // serialization and deserialization of LogicalPlan). We always use default runtime executor
-            // by passing None
-            if executor.is_some() {
-                return Err(PyValueError::new_err(
-                    "Custom executors are not supported for the default DataFusion runtime. Remove executor parameter or use different vendor.",
-                ));
-            }
-            Ok(None)
-        }
-        Some(other) => Err(PyValueError::new_err(format!(
-            "Unsupported vendor: '{}'. Supported vendors: 'datafusion', 'sparksql'",
-            other
-        ))),
-    }
-}
-
-#[async_trait]
-impl PlanExecutor for SparkSqlPlanExecutor {
-    async fn execute_plan(
-        &self,
-        plan: LogicalPlan,
-    ) -> vegafusion_common::error::Result<VegaFusionTable> {
-        // Acquire mutex lock to ensure only one request runs at a time
-        let _lock = self.mutex.lock().await;
-
-        // Convert logical plan to SparkSQL
-        let spark_sql = logical_plan_to_spark_sql(&plan)?;
-
-        let python_executor = &self.python_executor;
-        let result = tokio::task::spawn_blocking({
-            let python_executor = Python::with_gil(|py| python_executor.clone_ref(py));
-            let spark_sql = spark_sql.clone();
-
-            move || {
-                Python::with_gil(|py| -> PyResult<VegaFusionTable> {
-                    let sql_py = PyString::new(py, &spark_sql);
-
-                    let table_result = if python_executor.bind(py).is_callable() {
-                        python_executor.call1(py, (sql_py,))
-                    } else if python_executor.bind(py).hasattr("execute_plan")? {
-                        let execute_plan_method =
-                            python_executor.bind(py).getattr("execute_plan")?;
-                        execute_plan_method
-                            .call1((sql_py,))
-                            .map(|result| result.into())
-                    } else {
-                        return Err(PyValueError::new_err(
-                            "Executor must be callable or have an execute_plan method",
-                        ));
-                    }?;
-
-                    VegaFusionTable::from_pyarrow(py, &table_result.bind(py))
-                })
-            }
-        })
-        .await;
-
-        match result {
-            Ok(Ok(table)) => Ok(table),
-            Ok(Err(py_err)) => Err(vegafusion_common::error::VegaFusionError::internal(
-                format!("Python executor error: {}", py_err),
-            )),
-            Err(join_err) => Err(vegafusion_common::error::VegaFusionError::internal(
-                format!("Failed to execute Python executor: {}", join_err),
-            )),
-        }
-    }
-}
diff --git a/vegafusion-python/tests/specs/aggregations.json b/vegafusion-python/tests/specs/aggregations.json
deleted file mode 100644
index a4cd6ca09..000000000
--- a/vegafusion-python/tests/specs/aggregations.json
+++ /dev/null
@@ -1,829 +0,0 @@
-{
-    "layer": [
-        {
-            "layer": [
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "line",
-                                "color": "#83AFF6",
-                                "tooltip": false
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": "descending",
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    },
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "average",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "order_total",
-                                    "scale": {
-                                        "range": [
-                                            "#83AFF6"
-                                        ],
-                                        "domain": [
-                                            "order_total"
-                                        ]
-                                    }
-                                },
-                                "tooltip": [
-                                    {
-                                        "type": "quantitative",
-                                        "field": "discount",
-                                        "aggregate": "average"
-                                    }
-                                ],
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_0"
-                                                },
-                                                {
-                                                    "param": "legend_color_0"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "interval_selection",
-                                    "select": {
-                                        "type": "interval",
-                                        "encodings": [
-                                            "x",
-                                            "y"
-                                        ]
-                                    }
-                                }
-                            ]
-                        }
-                    ]
-                },
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "line",
-                                "color": "#2266D3",
-                                "tooltip": false
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": "descending",
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    },
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "median",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "order_total",
-                                    "scale": {
-                                        "range": [
-                                            "#2266D3"
-                                        ],
-                                        "domain": [
-                                            "order_total"
-                                        ]
-                                    }
-                                },
-                                "tooltip": [
-                                    {
-                                        "type": "quantitative",
-                                        "field": "discount",
-                                        "aggregate": "median"
-                                    }
-                                ],
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_1"
-                                                },
-                                                {
-                                                    "param": "legend_color_1"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_1",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_1",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                }
-                            ]
-                        }
-                    ]
-                },
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "line",
-                                "color": "#CC8BFF",
-                                "tooltip": false
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": "descending",
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    },
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "min",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "order_total",
-                                    "scale": {
-                                        "range": [
-                                            "#CC8BFF"
-                                        ],
-                                        "domain": [
-                                            "order_total"
-                                        ]
-                                    }
-                                },
-                                "tooltip": [
-                                    {
-                                        "type": "quantitative",
-                                        "field": "discount",
-                                        "aggregate": "min"
-                                    }
-                                ],
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_2"
-                                                },
-                                                {
-                                                    "param": "legend_color_2"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_2",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_2",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                }
-                            ]
-                        }
-                    ]
-                },
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "line",
-                                "color": "#8018D3",
-                                "tooltip": false
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": "descending",
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    },
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "max",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "order_total",
-                                    "scale": {
-                                        "range": [
-                                            "#8018D3"
-                                        ],
-                                        "domain": [
-                                            "order_total"
-                                        ]
-                                    }
-                                },
-                                "tooltip": [
-                                    {
-                                        "type": "quantitative",
-                                        "field": "discount",
-                                        "aggregate": "max"
-                                    }
-                                ],
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_3"
-                                                },
-                                                {
-                                                    "param": "legend_color_3"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_3",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_3",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                }
-                            ]
-                        }
-                    ]
-                },
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "line",
-                                "color": "#0E9384",
-                                "tooltip": false
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": "descending",
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    },
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "count",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "order_total",
-                                    "scale": {
-                                        "range": [
-                                            "#0E9384"
-                                        ],
-                                        "domain": [
-                                            "order_total"
-                                        ]
-                                    }
-                                },
-                                "tooltip": [
-                                    {
-                                        "axis": {
-                                            "format": {
-                                                "type": "default",
-                                                "decimals": null
-                                            },
-                                            "formatType": "numberFormatFromNumberType"
-                                        },
-                                        "type": "quantitative",
-                                        "field": "order_total",
-                                        "scale": {
-                                            "type": "linear",
-                                            "zero": false
-                                        },
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "aggregate": "count",
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    {
-                                        "type": "quantitative",
-                                        "field": "discount",
-                                        "aggregate": "sum"
-                                    }
-                                ],
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_4"
-                                                },
-                                                {
-                                                    "param": "legend_color_4"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_4",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_4",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                }
-                            ]
-                        }
-                    ]
-                },
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "line",
-                                "color": "#5FD9C3",
-                                "tooltip": false
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": "descending",
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "customer_name",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    },
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "distinct",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "customer_name",
-                                    "scale": {
-                                        "range": [
-                                            "#5FD9C3"
-                                        ],
-                                        "domain": [
-                                            "customer_name"
-                                        ]
-                                    }
-                                },
-                                "tooltip": [
-                                    {
-                                        "type": "quantitative",
-                                        "field": "discount",
-                                        "aggregate": "distinct"
-                                    }
-                                ],
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_5"
-                                                },
-                                                {
-                                                    "param": "legend_color_5"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_5",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_5",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                }
-                            ]
-                        }
-                    ]
-                }
-            ],
-            "resolve": {
-                "scale": {
-                    "color": "independent"
-                }
-            }
-        },
-        {
-            "layer": [
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "line",
-                                "color": "#EEC026",
-                                "tooltip": false
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": "descending",
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear",
-                                        "zero": false
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "title": null
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "zero": false
-                                    },
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "sum",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "order_total",
-                                    "scale": {
-                                        "range": [
-                                            "#EEC026"
-                                        ],
-                                        "domain": [
-                                            "order_total"
-                                        ]
-                                    }
-                                },
-                                "tooltip": [
-                                    {
-                                        "type": "quantitative",
-                                        "field": "discount",
-                                        "aggregate": "sum"
-                                    }
-                                ],
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_6"
-                                                },
-                                                {
-                                                    "param": "legend_color_6"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_6",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_6",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                }
-                            ]
-                        }
-                    ]
-                }
-            ],
-            "resolve": {
-                "scale": {
-                    "color": "independent"
-                }
-            }
-        }
-    ],
-    "title": "",
-    "config": {
-        "legend": {
-            "disable": false
-        },
-        "axisQuantitative": {
-            "tickCount": 5
-        },
-        "area": {
-            "line": true
-        },
-        "mark": {
-            "color": "#2266D3"
-        },
-        "customFormatTypes": true
-    },
-    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
-    "resolve": {
-        "scale": {
-            "y": "independent"
-        }
-    },
-    "encoding": {},
-    "usermeta": {
-        "seriesNames": [
-            "order_total",
-            "order_total",
-            "order_total",
-            "order_total",
-            "order_total",
-            "customer_name",
-            "order_total"
-        ],
-        "seriesOrder": [
-            0,
-            1,
-            2,
-            3,
-            4,
-            6,
-            5
-        ],
-        "specSchemaVersion": 2,
-        "tooltipDefaultMode": false
-    },
-    "height": "container",
-    "width": "container",
-    "autosize": {
-        "type": "fit"
-    },
-    "data": {
-        "url": "vegafusion+dataset://sales_data_1kk"
-    }
-}
diff --git a/vegafusion-python/tests/specs/bin.json b/vegafusion-python/tests/specs/bin.json
deleted file mode 100644
index 55b85f231..000000000
--- a/vegafusion-python/tests/specs/bin.json
+++ /dev/null
@@ -1,165 +0,0 @@
-{
-    "layer": [
-        {
-            "layer": [
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "bar",
-                                "color": "#2266D3",
-                                "tooltip": true
-                            },
-                            "encoding": {
-                                "x": {
-                                    "bin": {
-                                        "step": 10
-                                    },
-                                    "sort": "descending",
-                                    "type": "quantitative",
-                                    "field": "customer_age",
-                                    "scale": {
-                                        "type": "linear"
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "customer_name",
-                                    "scale": {
-                                        "type": "linear"
-                                    },
-                                    "format": {
-                                        "type": "default",
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-                                    "datum": "Orders",
-                                    "scale": {
-                                        "range": [
-                                            "#2266D3"
-                                        ],
-                                        "domain": [
-                                            "Orders"
-                                        ]
-                                    }
-                                },
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_0"
-                                                },
-                                                {
-                                                    "param": "legend_color_0"
-                                                },
-                                                {
-                                                    "param": "interval_selection"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
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-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "interval_selection",
-                                    "select": {
-                                        "type": "interval",
-                                        "encodings": [
-                                            "x",
-                                            "y"
-                                        ]
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-                }
-            ],
-            "resolve": {
-                "scale": {
-                    "color": "independent"
-                }
-            }
-        }
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-    "config": {
-        "legend": {
-            "disable": false
-        },
-        "axisQuantitative": {
-            "tickCount": 5
-        },
-        "area": {
-            "line": true
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-        "mark": {
-            "color": "#2266D3"
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-        "customFormatTypes": true
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-    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
-    "encoding": {},
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-        "aditionalTypeInfo": {
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-        "specSchemaVersion": 2,
-        "tooltipDefaultMode": true
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-    "height": "container",
-    "width": "container",
-    "autosize": {
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-    },
-    "data": {
-        "url": "vegafusion+dataset://sales_data_1kk"
-    }
-}
diff --git a/vegafusion-python/tests/specs/dual_axis.json b/vegafusion-python/tests/specs/dual_axis.json
deleted file mode 100644
index 64b6d67d7..000000000
--- a/vegafusion-python/tests/specs/dual_axis.json
+++ /dev/null
@@ -1,294 +0,0 @@
-{
-    "layer": [
-        {
-            "layer": [
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "bar",
-                                "color": "#2266D3",
-                                "tooltip": false
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": {
-                                        "op": "sum",
-                                        "field": "order_total",
-                                        "order": "descending"
-                                    },
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear"
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "type": "linear"
-                                    },
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "sum",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "order_total",
-                                    "scale": {
-                                        "range": [
-                                            "#2266D3"
-                                        ],
-                                        "domain": [
-                                            "order_total"
-                                        ]
-                                    }
-                                },
-                                "tooltip": [
-                                    {
-                                        "type": "quantitative",
-                                        "field": "discount",
-                                        "aggregate": "sum"
-                                    }
-                                ],
-                                "xOffset": {
-                                    "datum": "series_0"
-                                },
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_0"
-                                                },
-                                                {
-                                                    "param": "legend_color_0"
-                                                },
-                                                {
-                                                    "param": "interval_selection"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "interval_selection",
-                                    "select": {
-                                        "type": "interval",
-                                        "encodings": [
-                                            "x",
-                                            "y"
-                                        ]
-                                    }
-                                }
-                            ]
-                        }
-                    ]
-                }
-            ],
-            "resolve": {
-                "scale": {
-                    "color": "independent"
-                }
-            }
-        },
-        {
-            "layer": [
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "bar",
-                                "color": "#83AFF6",
-                                "tooltip": false
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": {
-                                        "op": "sum",
-                                        "field": "order_total",
-                                        "order": "descending"
-                                    },
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear"
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "title": null
-                                    },
-                                    "type": "quantitative",
-                                    "field": "discount",
-                                    "scale": {},
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "sum",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "discount",
-                                    "scale": {
-                                        "range": [
-                                            "#83AFF6"
-                                        ],
-                                        "domain": [
-                                            "discount"
-                                        ]
-                                    }
-                                },
-                                "tooltip": [
-                                    {
-                                        "type": "quantitative",
-                                        "field": "discount",
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "aggregate": "sum"
-                                    }
-                                ],
-                                "xOffset": {
-                                    "datum": "series_1"
-                                },
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_1"
-                                                },
-                                                {
-                                                    "param": "legend_color_1"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_1",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_1",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                }
-                            ]
-                        }
-                    ]
-                }
-            ],
-            "resolve": {
-                "scale": {
-                    "color": "independent"
-                }
-            }
-        }
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-    "config": {
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-        },
-        "axisQuantitative": {
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-        "mark": {
-            "color": "#2266D3"
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-        "customFormatTypes": true
-    },
-    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
-    "resolve": {
-        "scale": {
-            "y": "independent"
-        }
-    },
-    "encoding": {},
-    "usermeta": {
-        "seriesNames": [
-            "order_total",
-            "discount"
-        ],
-        "seriesOrder": [
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-            1
-        ],
-        "specSchemaVersion": 2,
-        "tooltipDefaultMode": false
-    },
-    "height": "container",
-    "width": "container",
-    "autosize": {
-        "type": "fit"
-    },
-    "data": {
-        "url": "vegafusion+dataset://sales_data_1kk"
-    }
-}
\ No newline at end of file
diff --git a/vegafusion-python/tests/specs/group_by_categorical.json b/vegafusion-python/tests/specs/group_by_categorical.json
deleted file mode 100644
index 8fefc90b1..000000000
--- a/vegafusion-python/tests/specs/group_by_categorical.json
+++ /dev/null
@@ -1,153 +0,0 @@
-{
-    "layer": [
-        {
-            "layer": [
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "bar",
-                                "tooltip": true
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": "descending",
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear"
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "type": "linear"
-                                    },
-                                    "stack": "zero",
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "sum",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "sort": null,
-                                    "type": "nominal",
-                                    "field": "customer_segment",
-                                    "scale": {
-                                        "scheme": "deepnote10"
-                                    }
-                                },
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_0"
-                                                },
-                                                {
-                                                    "param": "legend_color_0"
-                                                },
-                                                {
-                                                    "param": "interval_selection"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "interval_selection",
-                                    "select": {
-                                        "type": "interval",
-                                        "encodings": [
-                                            "x",
-                                            "y"
-                                        ]
-                                    }
-                                }
-                            ]
-                        }
-                    ]
-                }
-            ],
-            "resolve": {
-                "scale": {
-                    "color": "independent"
-                }
-            }
-        }
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-    "config": {
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-            "disable": false
-        },
-        "axisQuantitative": {
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-        },
-        "area": {
-            "line": true
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-        "mark": {
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-        "customFormatTypes": true
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-        "specSchemaVersion": 2,
-        "tooltipDefaultMode": true
-    },
-    "height": "container",
-    "width": "container",
-    "autosize": {
-        "type": "fit"
-    },
-    "data": {
-        "url": "vegafusion+dataset://sales_data_1kk"
-    }
-}
\ No newline at end of file
diff --git a/vegafusion-python/tests/specs/group_by_numerical.json b/vegafusion-python/tests/specs/group_by_numerical.json
deleted file mode 100644
index 69e3acd7b..000000000
--- a/vegafusion-python/tests/specs/group_by_numerical.json
+++ /dev/null
@@ -1,154 +0,0 @@
-{
-    "layer": [
-        {
-            "layer": [
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "bar",
-                                "tooltip": true
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": "descending",
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear"
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "type": "linear"
-                                    },
-                                    "stack": "zero",
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "sum",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "sort": null,
-                                    "type": "quantitative",
-                                    "field": "customer_age",
-                                    "scale": {
-                                        "scheme": "deepnote_blues"
-                                    },
-                                    "aggregate": "average"
-                                },
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_0"
-                                                },
-                                                {
-                                                    "param": "legend_color_0"
-                                                },
-                                                {
-                                                    "param": "interval_selection"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "interval_selection",
-                                    "select": {
-                                        "type": "interval",
-                                        "encodings": [
-                                            "x",
-                                            "y"
-                                        ]
-                                    }
-                                }
-                            ]
-                        }
-                    ]
-                }
-            ],
-            "resolve": {
-                "scale": {
-                    "color": "independent"
-                }
-            }
-        }
-    ],
-    "title": "",
-    "config": {
-        "legend": {
-            "disable": false
-        },
-        "axisQuantitative": {
-            "tickCount": 5
-        },
-        "area": {
-            "line": true
-        },
-        "mark": {
-            "color": "#2266D3"
-        },
-        "customFormatTypes": true
-    },
-    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
-    "encoding": {},
-    "usermeta": {
-        "seriesNames": [
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-        ],
-        "seriesOrder": [
-            0
-        ],
-        "specSchemaVersion": 2,
-        "tooltipDefaultMode": true
-    },
-    "height": "container",
-    "width": "container",
-    "autosize": {
-        "type": "fit"
-    },
-    "data": {
-        "url": "vegafusion+dataset://sales_data_1kk"
-    }
-}
diff --git a/vegafusion-python/tests/specs/sales_data_1kk.parquet b/vegafusion-python/tests/specs/sales_data_1kk.parquet
deleted file mode 100644
index cc359760b..000000000
Binary files a/vegafusion-python/tests/specs/sales_data_1kk.parquet and /dev/null differ
diff --git a/vegafusion-python/tests/specs/simple.json b/vegafusion-python/tests/specs/simple.json
deleted file mode 100644
index 0960782cc..000000000
--- a/vegafusion-python/tests/specs/simple.json
+++ /dev/null
@@ -1,85 +0,0 @@
-{
-    "data": {
-        "url": "vegafusion+dataset://sales_data_1kk"
-    },
-    "layer": [
-      {
-        "layer": [
-          {
-            "layer": [
-              {
-                "mark": {
-                  "clip": true,
-                  "type": "bar",
-                  "color": "#2266D3",
-                  "tooltip": true
-                },
-                "encoding": {
-                  "x": {
-                    "sort": {
-                      "op": "sum",
-                      "field": "order_total",
-                      "order": "descending"
-                    },
-                    "type": "nominal",
-                    "field": "product_category",
-                    "scale": {
-                      "type": "linear"
-                    }
-                  },
-                  "y": {
-                    "axis": {
-                      "format": {
-                        "type": "default",
-                        "decimals": null
-                      },
-                      "formatType": "numberFormatFromNumberType"
-                    },
-                    "type": "quantitative",
-                    "field": "order_total",
-                    "scale": {
-                      "type": "linear"
-                    },
-                    "format": {
-                      "type": "default",
-                      "decimals": null
-                    },
-                    "aggregate": "sum",
-                    "formatType": "numberFormatFromNumberType"
-                  },
-                  "color": {
-                    "type": "nominal",
-                    "datum": "order_total",
-                    "scale": {
-                      "range": [
-                        "#2266D3"
-                      ],
-                      "domain": [
-                        "order_total"
-                      ]
-                    }
-                  },
-                  "xOffset": {
-                    "datum": "series_0"
-                  }
-                },
-                "transform": []
-              }
-            ]
-          }
-        ],
-        "resolve": {
-          "scale": {
-            "color": "independent"
-          }
-        }
-      }
-    ],
-    "config": {
-      "legend": {
-        "disable": false
-      }
-    },
-    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
-    "encoding": {}
-  }
\ No newline at end of file
diff --git a/vegafusion-python/tests/specs/temporal.json b/vegafusion-python/tests/specs/temporal.json
deleted file mode 100644
index 4514cb7bc..000000000
--- a/vegafusion-python/tests/specs/temporal.json
+++ /dev/null
@@ -1,163 +0,0 @@
-{
-    "layer": [
-        {
-            "layer": [
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "bar",
-                                "color": "#2266D3",
-                                "tooltip": true
-                            },
-                            "encoding": {
-                                "x": {
-                                    "axis": {
-                                        "grid": false
-                                    },
-                                    "sort": "descending",
-                                    "type": "temporal",
-                                    "field": "datetime",
-                                    "scale": {
-                                        "type": "linear"
-                                    },
-                                    "timeUnit": "yearmonth",
-                                    "bandPosition": 0
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "type": "linear"
-                                    },
-                                    "stack": "zero",
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "sum",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "Orders",
-                                    "scale": {
-                                        "range": [
-                                            "#2266D3"
-                                        ],
-                                        "domain": [
-                                            "Orders"
-                                        ]
-                                    }
-                                },
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_0"
-                                                },
-                                                {
-                                                    "param": "legend_color_0"
-                                                },
-                                                {
-                                                    "param": "interval_selection"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "interval_selection",
-                                    "select": {
-                                        "type": "interval",
-                                        "encodings": [
-                                            "x",
-                                            "y"
-                                        ]
-                                    }
-                                }
-                            ]
-                        }
-                    ]
-                }
-            ],
-            "resolve": {
-                "scale": {
-                    "color": "independent"
-                }
-            }
-        }
-    ],
-    "title": "",
-    "config": {
-        "legend": {
-            "disable": false
-        },
-        "axisQuantitative": {
-            "tickCount": 5
-        },
-        "area": {
-            "line": true
-        },
-        "mark": {
-            "color": "#2266D3"
-        },
-        "customFormatTypes": true
-    },
-    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
-    "encoding": {},
-    "usermeta": {
-        "seriesNames": [
-            "Orders"
-        ],
-        "seriesOrder": [
-            0
-        ],
-        "specSchemaVersion": 2,
-        "tooltipDefaultMode": true
-    },
-    "height": "container",
-    "width": "container",
-    "autosize": {
-        "type": "fit"
-    },
-    "data": {
-        "url": "vegafusion+dataset://sales_data_1kk"
-    }
-}
\ No newline at end of file
diff --git a/vegafusion-python/tests/specs/tooltip_value_labels.json b/vegafusion-python/tests/specs/tooltip_value_labels.json
deleted file mode 100644
index b574fc9bc..000000000
--- a/vegafusion-python/tests/specs/tooltip_value_labels.json
+++ /dev/null
@@ -1,277 +0,0 @@
-{
-    "layer": [
-        {
-            "layer": [
-                {
-                    "layer": [
-                        {
-                            "mark": {
-                                "clip": true,
-                                "type": "bar",
-                                "color": "#2266D3",
-                                "tooltip": false
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": "descending",
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear"
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "type": "linear"
-                                    },
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "sum",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "Orders",
-                                    "scale": {
-                                        "range": [
-                                            "#2266D3"
-                                        ],
-                                        "domain": [
-                                            "Orders"
-                                        ]
-                                    }
-                                },
-                                "tooltip": [
-                                    {
-                                        "type": "quantitative",
-                                        "field": "discount",
-                                        "aggregate": "sum"
-                                    },
-                                    {
-                                        "type": "quantitative",
-                                        "field": "order_total",
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "aggregate": "sum"
-                                    }
-                                ],
-                                "xOffset": {
-                                    "datum": "series_0"
-                                },
-                                "opacity": {
-                                    "condition": {
-                                        "test": {
-                                            "and": [
-                                                {
-                                                    "param": "legend_size_0"
-                                                },
-                                                {
-                                                    "param": "legend_color_0"
-                                                },
-                                                {
-                                                    "param": "interval_selection"
-                                                }
-                                            ]
-                                        },
-                                        "value": 1
-                                    },
-                                    "value": 0.2
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "interval_selection",
-                                    "select": {
-                                        "type": "interval",
-                                        "encodings": [
-                                            "x",
-                                            "y"
-                                        ]
-                                    }
-                                }
-                            ]
-                        },
-                        {
-                            "mark": {
-                                "dx": 0,
-                                "dy": -1,
-                                "fill": "black",
-                                "type": "text",
-                                "align": "center",
-                                "baseline": "bottom"
-                            },
-                            "encoding": {
-                                "x": {
-                                    "sort": "descending",
-                                    "type": "nominal",
-                                    "field": "product_category",
-                                    "scale": {
-                                        "type": "linear"
-                                    }
-                                },
-                                "y": {
-                                    "axis": {
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "formatType": "numberFormatFromNumberType"
-                                    },
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "scale": {
-                                        "type": "linear"
-                                    },
-                                    "format": {
-                                        "type": "default",
-                                        "decimals": null
-                                    },
-                                    "aggregate": "sum",
-                                    "formatType": "numberFormatFromNumberType"
-                                },
-                                "text": {
-                                    "type": "quantitative",
-                                    "field": "order_total",
-                                    "aggregate": "sum"
-                                },
-                                "color": {
-                                    "type": "nominal",
-                                    "datum": "Orders",
-                                    "scale": {
-                                        "range": [
-                                            "#2266D3"
-                                        ],
-                                        "domain": [
-                                            "Orders"
-                                        ]
-                                    }
-                                },
-                                "tooltip": [
-                                    {
-                                        "type": "quantitative",
-                                        "field": "discount",
-                                        "aggregate": "sum"
-                                    },
-                                    {
-                                        "type": "quantitative",
-                                        "field": "order_total",
-                                        "format": {
-                                            "type": "default",
-                                            "decimals": null
-                                        },
-                                        "aggregate": "sum"
-                                    }
-                                ],
-                                "xOffset": {
-                                    "datum": "series_0"
-                                }
-                            },
-                            "transform": [],
-                            "params": [
-                                {
-                                    "name": "legend_size_0_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "size"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                },
-                                {
-                                    "name": "legend_color_0_0",
-                                    "select": {
-                                        "type": "point",
-                                        "encodings": [
-                                            "color"
-                                        ],
-                                        "toggle": "true"
-                                    },
-                                    "bind": "legend"
-                                }
-                            ]
-                        }
-                    ]
-                }
-            ],
-            "resolve": {
-                "scale": {
-                    "color": "independent"
-                }
-            }
-        }
-    ],
-    "title": "",
-    "config": {
-        "legend": {
-            "disable": false
-        },
-        "axisQuantitative": {
-            "tickCount": 5
-        },
-        "area": {
-            "line": true
-        },
-        "mark": {
-            "color": "#2266D3"
-        },
-        "customFormatTypes": true
-    },
-    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
-    "encoding": {},
-    "usermeta": {
-        "seriesNames": [
-            "Orders"
-        ],
-        "seriesOrder": [
-            0
-        ],
-        "specSchemaVersion": 2,
-        "tooltipDefaultMode": false
-    },
-    "height": "container",
-    "width": "container",
-    "autosize": {
-        "type": "fit"
-    },
-    "data": {
-        "url": "vegafusion+dataset://sales_data_1kk"
-    }
-}
diff --git a/vegafusion-python/tests/test_jupyter_widget.py b/vegafusion-python/tests/test_jupyter_widget.py
index 3c7a9d0bf..009bdc779 100644
--- a/vegafusion-python/tests/test_jupyter_widget.py
+++ b/vegafusion-python/tests/test_jupyter_widget.py
@@ -33,7 +33,7 @@
 altair_default_template = r"""
 ```python
 import altair as alt
-alt.renderers.enable('default', embed_options={'actions': False});
+alt.renderers.enable('default', embed_options={'actions': False, 'renderer': 'canvas'});
 ```
 
 ```python
@@ -50,7 +50,7 @@
 ```python
 import vegafusion
 import altair as alt
-alt.renderers.enable('jupyter', embed_options={'actions': False});
+alt.renderers.enable('jupyter', embed_options={'actions': False, 'renderer': 'canvas'});
 alt.data_transformers.enable('vegafusion');
 ```
 
@@ -332,6 +332,11 @@ def test_vegafusion_widget():
 
     # Display with default altair renderer
     notebook_text_alt = f"""
+```python
+import altair as alt
+alt.renderers.enable('default', embed_options={{'actions': False, 'renderer': 'canvas'}});
+```
+
 ```python
 {altair_chart_str}
 
diff --git a/vegafusion-python/tests/test_spark_e2e.py b/vegafusion-python/tests/test_spark_e2e.py
deleted file mode 100644
index e4a28a0f8..000000000
--- a/vegafusion-python/tests/test_spark_e2e.py
+++ /dev/null
@@ -1,138 +0,0 @@
-from __future__ import annotations
-from pathlib import Path
-import json
-import pytest
-
-import pandas as pd
-from pyspark.sql import SparkSession
-from pyspark.sql.functions import col, to_utc_timestamp, current_timezone
-from pyspark.sql.pandas.types import to_arrow_schema
-import vl_convert as vlc
-import vegafusion as vf
-import pyarrow as pa
-from typing import Optional
-
-
-SPEC_ROOT = (Path(__file__).parent / "specs").resolve()
-SALES_DATA_PATH = SPEC_ROOT / "sales_data_1kk.parquet"
-SALES_DATA_DF = pd.read_parquet(SALES_DATA_PATH)
-
-
-def _discover_spec_files(limit: Optional[int] = None) -> list[Path]:
-    specs_all = SPEC_ROOT.rglob("*.json")
-    specs_filtered = [p for p in specs_all if not p.name.startswith("_")]
-    specs_sorted = sorted(specs_filtered)
-    return specs_sorted[:limit] if limit is not None else specs_sorted
-
-
-@pytest.fixture(scope="session")
-def spark():
-    """Initialise a local SparkSession for the duration of the test session."""
-
-    session: SparkSession = (
-        SparkSession.builder.appName("vegafusion-e2e")
-        .config("spark.sql.execution.arrow.pyspark.fallback.enabled", "true")
-        .config("spark.sql.legacy.parquet.nanosAsLong", "true")
-        .config("spark.sql.execution.arrow.pyspark.enabled", "true")
-        .config("spark.executor.memory", "8g")
-        .config("spark.driver.memory", "8g")
-        .master("local[2]")
-        .getOrCreate()
-    )
-
-    # This is required for properly handling temporal. Due to how Spark handles dates (which doesn't match
-    # proper SQL standart), we have to set timezone to UTC. We mention this is our docs for users.
-    session.sql("SET TIME ZONE 'UTC'")
-
-    sales_data_df = session.read.parquet(str(SALES_DATA_PATH))
-
-    # Convert datetime column from bigint (nanoseconds) to actual timestamp
-    sales_data_df = sales_data_df.withColumn(
-        "datetime",
-        to_utc_timestamp((col("datetime") / 1e9).cast("timestamp"), current_timezone()),
-    )
-
-    sales_data_df.createOrReplaceTempView("sales_data_1kk")
-
-    yield session
-
-    session.stop()
-
-
-_SPEC_FILES = [p for p in _discover_spec_files() if isinstance(p, Path)]
-
-
-@pytest.mark.parametrize("spec_path", _SPEC_FILES, ids=[p.stem for p in _SPEC_FILES])
-def test_spec_against_spark(spec_path: Path, spark: SparkSession):
-    """End-to-end comparison between in-memory evaluation and Spark SQL.
-
-    For every Vega-Lite spec we:
-
-    1. Load the spec JSON and discover the associated datasets.
-    2. Evaluate the spec with the *in-memory* Vegafusion runtime to obtain the
-       expected result.
-    3. Register the datasets as Spark SQL tables.
-    4. Ask Vegafusion to generate the equivalent Spark SQL statements.
-    5. Execute the SQL with Spark and collect the actual result.
-    6. Compare *expected* vs *actual*.
-    """
-
-    print(f"Testing {spec_path.name}")
-
-    vegalite_spec = json.loads(spec_path.read_text("utf8"))
-    vega_spec = vlc.vegalite_to_vega(vegalite_spec)
-
-    print("Generating Pandas-based transformed spec")
-    inmemory_spec, inmemory_datasets, _ = vf.runtime.pre_transform_extract(
-        spec=vega_spec,
-        preserve_interactivity=False,
-        local_tz="UTC",
-        default_input_tz="UTC",
-        inline_datasets={"sales_data_1kk": SALES_DATA_DF},
-        extract_threshold=0,
-        extracted_format="pyarrow",
-    )
-
-    print("Generating Spark-backed results via custom executor")
-
-    def spark_executor(sql_query: str) -> pa.Table:
-        spark_df = spark.sql(sql_query)
-        return pa.Table.from_pandas(spark_df.toPandas())
-
-    vf_spark_runtime = vf.VegaFusionRuntime.new_vendor("sparksql", spark_executor)
-
-    sales_schema = to_arrow_schema(spark.table("sales_data_1kk").schema)
-
-    spark_spec, spark_datasets, _ = vf_spark_runtime.pre_transform_extract(
-        spec=vega_spec,
-        preserve_interactivity=False,
-        local_tz="UTC",
-        default_input_tz="UTC",
-        inline_datasets={"sales_data_1kk": sales_schema},
-        extract_threshold=0,
-        extracted_format="pyarrow",
-    )
-
-    print("Comparing transformed specs (Pandas vs Spark)")
-
-    assert spark_spec == inmemory_spec
-
-    assert len(inmemory_datasets) == len(spark_datasets), (
-        f"Dataset count mismatch: in-memory={len(inmemory_datasets)} "
-        f"vs spark={len(spark_datasets)}"
-    )
-
-    for inmemory_ds, spark_ds in zip(inmemory_datasets, spark_datasets):
-        inmemory_name, _, inmemory_data = inmemory_ds
-        spark_name, _, spark_data = spark_ds
-
-        assert inmemory_name == spark_name
-
-        pd.testing.assert_frame_equal(
-            inmemory_data.to_pandas(),
-            spark_data.to_pandas(),
-            check_dtype=False,
-            check_like=True,
-            atol=1e-6,
-            rtol=1e-6,
-        )
diff --git a/vegafusion-python/vegafusion/runtime.py b/vegafusion-python/vegafusion/runtime.py
index 760561aa5..84034eae7 100644
--- a/vegafusion-python/vegafusion/runtime.py
+++ b/vegafusion-python/vegafusion/runtime.py
@@ -5,9 +5,7 @@
 from typing import (
     TYPE_CHECKING,
     Any,
-    Callable,
     Literal,
-    Protocol,
     TypedDict,
     Union,
     cast,
@@ -37,21 +35,6 @@
     )
 
 
-class PlanExecutorProtocol(Protocol):
-    """Protocol for objects with execute_plan method."""
-
-    def execute_plan(self, logical_plan_json: str) -> pa.Table:
-        """Execute a logical plan and return an Arrow table."""
-        ...
-
-
-# Type alias for plan executors
-PlanExecutor = Union[
-    # Callable that takes logical plan JSON and returns Arrow table
-    Callable[[str], "pa.Table"],
-    PlanExecutorProtocol,  # Object with execute_plan method
-]
-
 # This type isn't defined in the grpcio package, so let's at least name it
 UnaryUnaryMultiCallable = Any
 
@@ -246,32 +229,6 @@ def runtime(self) -> PyVegaFusionRuntime:
             )
         return self._runtime
 
-    @classmethod
-    def new_vendor(
-        cls,
-        vendor: Literal["datafusion", "sparksql"],
-        executor: PlanExecutor | None = None,
-        cache_capacity: int = 64,
-        memory_limit: int | None = None,
-        worker_threads: int | None = None,
-    ) -> VegaFusionRuntime:
-        from vegafusion._vegafusion import PyVegaFusionRuntime
-
-        inst = cls(cache_capacity, memory_limit, worker_threads)
-        if inst.memory_limit is None:
-            inst.memory_limit = get_virtual_memory() // 2
-        if inst.worker_threads is None:
-            inst.worker_threads = get_cpu_count()
-
-        inst._runtime = PyVegaFusionRuntime.new_embedded_vendor(
-            inst.cache_capacity,
-            inst.memory_limit,
-            inst.worker_threads,
-            vendor,
-            executor,
-        )
-        return inst
-
     def grpc_connect(self, url: str) -> None:
         """
         Connect to a VegaFusion server over gRPC at the provided gRPC url
@@ -324,8 +281,6 @@ def _import_inline_datasets(
             if (pa is not None and isinstance(value, pa.Schema)) or hasattr(
                 value, "__arrow_c_schema__"
             ):
-                # Handle PyArrow Schema - convert to arro3 Schema
-                # This allows for planning without requiring actual data
                 imported_inline_datasets[name] = Schema.from_arrow(value)
             elif pd is not None and pa is not None and isinstance(value, pd.DataFrame):
                 # rename to help mypy
diff --git a/vegafusion-runtime/Cargo.toml b/vegafusion-runtime/Cargo.toml
index 2cf548655..6e2731186 100644
--- a/vegafusion-runtime/Cargo.toml
+++ b/vegafusion-runtime/Cargo.toml
@@ -6,7 +6,7 @@ harness = false
 name = "vegafusion-runtime"
 license = "BSD-3-Clause"
 edition = "2021"
-version = "2.0.8"
+version = "2.0.3"
 description = "VegaFusion Runtime"
 
 [features]
@@ -115,12 +115,12 @@ workspace = true
 [dependencies.vegafusion-common]
 path = "../vegafusion-common"
 features = ["json", "sqlparser", "prettyprint", "object_store", "url"]
-version = "2.0.8"
+version = "2.0.3"
 
 [dependencies.vegafusion-core]
 path = "../vegafusion-core"
 features = ["sqlparser"]
-version = "2.0.8"
+version = "2.0.3"
 
 [dependencies.serde]
 workspace = true
@@ -154,10 +154,6 @@ workspace = true
 [dependencies.datafusion-functions-window]
 workspace = true
 
-[dependencies.datafusion-sql]
-workspace = true
-features = ["unparser"]
-
 [dependencies.datafusion-proto]
 workspace = true
 optional = true
diff --git a/vegafusion-runtime/src/lib.rs b/vegafusion-runtime/src/lib.rs
index 4e5bafa10..8333dab58 100644
--- a/vegafusion-runtime/src/lib.rs
+++ b/vegafusion-runtime/src/lib.rs
@@ -7,7 +7,6 @@ pub mod datafusion;
 pub mod expression;
 pub mod plan_executor;
 pub mod signal;
-pub mod sql;
 pub mod task_graph;
 pub mod tokio_runtime;
 pub mod transform;
diff --git a/vegafusion-runtime/src/sql/mod.rs b/vegafusion-runtime/src/sql/mod.rs
deleted file mode 100644
index bc46ddaff..000000000
--- a/vegafusion-runtime/src/sql/mod.rs
+++ /dev/null
@@ -1,3 +0,0 @@
-pub mod spark;
-
-pub use spark::logical_plan_to_spark_sql;
diff --git a/vegafusion-runtime/src/sql/spark.rs b/vegafusion-runtime/src/sql/spark.rs
deleted file mode 100644
index b3be3d603..000000000
--- a/vegafusion-runtime/src/sql/spark.rs
+++ /dev/null
@@ -1,684 +0,0 @@
-use datafusion::sql::unparser::dialect::CustomDialectBuilder;
-use datafusion::sql::unparser::Unparser;
-use datafusion_common::tree_node::{Transformed, TreeNode};
-use datafusion_common::{Column, ScalarValue};
-use datafusion_expr::{expr::ScalarFunction, Expr, LogicalPlan};
-use lazy_static::lazy_static;
-use regex;
-use sqlparser::ast::{self, visit_expressions_mut};
-use std::collections::HashMap;
-use std::ops::ControlFlow;
-use vegafusion_common::error::{Result, VegaFusionError};
-
-/// This method converts a logical plan, which we get from DataFusion, into a SQL query
-/// which is compatible with Spark.
-// The SQL generated from the DataFusion plan is not compatible with Spark by default.
-// To make it work, we apply changes to both the logical plan itself and to the
-// abstract syntax tree generated from this logical plan before converting
-// it into an SQL string. This allows us to rewrite parts of the plan or syntax tree to
-// be compatible with Spark.
-pub fn logical_plan_to_spark_sql(plan: &LogicalPlan) -> Result<String> {
-    let plan = plan.clone();
-    let processed_plan = rewrite_subquery_column_identifiers(plan)?;
-    let processed_plan = rewrite_datetime_formatting(processed_plan)?;
-
-    let dialect = CustomDialectBuilder::new().build();
-    let unparser = Unparser::new(&dialect).with_pretty(true);
-    let mut statement = unparser.plan_to_sql(&processed_plan).map_err(|e| {
-        VegaFusionError::vendor(format!(
-            "Failed to generate SQL AST from logical plan: {}",
-            e
-        ))
-    })?;
-
-    rewrite_row_number(&mut statement);
-    rewrite_inf_and_nan(&mut statement);
-    rewrite_date_format(&mut statement);
-    rewrite_timestamps(&mut statement);
-    rewrite_intervals(&mut statement);
-    rewrite_nested_is_null(&mut statement);
-    rewrite_column_identifiers(&mut statement);
-
-    let spark_sql = statement.to_string();
-
-    Ok(spark_sql)
-}
-
-/// When adding row_number() DataFusion generates SQL like this:
-/// ```sql
-/// row_number() ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
-/// ```
-/// Which is not compatible with Spark. For Spark we rewrite AST to be
-/// ```sql
-/// row_number() OVER (ORDER BY monotonically_increasing_id())
-/// ```
-fn rewrite_row_number(statement: &mut ast::Statement) {
-    let _ = visit_expressions_mut(statement, |expr: &mut ast::Expr| {
-        if let ast::Expr::Function(func) = expr {
-            if func.name.to_string().to_lowercase() == "row_number" {
-                if let Some(ast::WindowType::WindowSpec(ref mut window_spec)) = &mut func.over {
-                    window_spec.window_frame = None;
-                    window_spec.order_by = vec![ast::OrderByExpr {
-                        expr: ast::Expr::Function(ast::Function {
-                            name: ast::ObjectName::from(vec![ast::Ident::new(
-                                "monotonically_increasing_id",
-                            )]),
-                            args: ast::FunctionArguments::List(ast::FunctionArgumentList {
-                                duplicate_treatment: None,
-                                args: vec![],
-                                clauses: vec![],
-                            }),
-                            filter: None,
-                            null_treatment: None,
-                            over: None,
-                            within_group: vec![],
-                            uses_odbc_syntax: false,
-                            parameters: ast::FunctionArguments::None,
-                        }),
-                        options: ast::OrderByOptions {
-                            asc: None,
-                            nulls_first: None,
-                        },
-                        with_fill: None,
-                    }];
-                }
-            }
-        }
-        ControlFlow::<()>::Continue(())
-    });
-}
-
-/// When DataFusion generates SQL, NaN and infinity values are presented as
-/// literals, while Spark requires them to be `float('NaN')`, `float('inf')`, etc.
-fn rewrite_inf_and_nan(statement: &mut ast::Statement) {
-    const SPECIAL_VALUES: &[&str] = &[
-        "nan",
-        "inf",
-        "infinity",
-        "+inf",
-        "+infinity",
-        "-inf",
-        "-infinity",
-    ];
-
-    let _ = visit_expressions_mut(statement, |expr: &mut ast::Expr| {
-        if let ast::Expr::Value(value) = expr {
-            if let ast::Value::Number(num_str, _) = &value.value {
-                if SPECIAL_VALUES.contains(&num_str.to_lowercase().as_str()) {
-                    *expr = ast::Expr::Function(ast::Function {
-                        name: ast::ObjectName::from(vec![ast::Ident::new("float")]),
-                        args: ast::FunctionArguments::List(ast::FunctionArgumentList {
-                            duplicate_treatment: None,
-                            args: vec![ast::FunctionArg::Unnamed(ast::FunctionArgExpr::Expr(
-                                ast::Expr::Value(ast::ValueWithSpan {
-                                    value: ast::Value::SingleQuotedString(num_str.clone()),
-                                    span: value.span.clone(),
-                                }),
-                            ))],
-                            clauses: vec![],
-                        }),
-                        filter: None,
-                        null_treatment: None,
-                        over: None,
-                        within_group: vec![],
-                        uses_odbc_syntax: false,
-                        parameters: ast::FunctionArguments::None,
-                    });
-                }
-            }
-        }
-        ControlFlow::<()>::Continue(())
-    });
-}
-
-/// Rename `to_char` function calls to `date_format` for Spark compatibility
-/// Spark <4 doesn't support formatting dates with `to_char` function
-fn rewrite_date_format(statement: &mut ast::Statement) {
-    let _ = visit_expressions_mut(statement, |expr: &mut ast::Expr| {
-        if let ast::Expr::Function(func) = expr {
-            if func.name.to_string().to_lowercase() == "to_char" {
-                func.name = ast::ObjectName::from(vec![ast::Ident::new("date_format")]);
-            }
-        }
-        ControlFlow::<()>::Continue(())
-    });
-}
-
-/// Timestamp is weird in Spark.
-/// First of all, TIMESTAMP type is SQL in "naive", it doesn't have associated timezone. But in Spark it actually has.
-/// And Spark doesn't support TIMESTAMP WITH TIME ZONE type, so we rewrite it to just TIMESTAMP.
-/// Because of this we also rewrite calls to make_timestamptz into make_timestamp, dropping milliseconds argument,
-/// as it's not supported by Spark.
-fn rewrite_timestamps(statement: &mut ast::Statement) {
-    let _ = visit_expressions_mut(statement, |expr: &mut ast::Expr| {
-        if let ast::Expr::Function(func) = expr {
-            let func_name = func.name.to_string().to_lowercase();
-            if func_name == "make_timestamptz" {
-                func.name = ast::ObjectName::from(vec![ast::Ident::new("make_timestamp")]);
-
-                // Remove milliseconds (not supported by Spark)
-                if let ast::FunctionArguments::List(ref mut arg_list) = &mut func.args {
-                    if arg_list.args.len() >= 7 {
-                        arg_list.args.remove(6);
-                    }
-                }
-            } else if func_name.starts_with("to_timestamp") {
-                // Spark only has to_timestamp function, no to_timestamp_nanos, etc
-                if func_name != "to_timestamp" {
-                    func.name = ast::ObjectName::from(vec![ast::Ident::new("to_timestamp")]);
-                }
-
-                // Spark's `to_timestamp` supports passing only format, while DataFusion allows to
-                // match list of Chrono patterns. So we remove ALL patterns from this func call
-                if let ast::FunctionArguments::List(ref mut arg_list) = &mut func.args {
-                    if arg_list.args.len() > 1 {
-                        arg_list.args.truncate(1);
-                    }
-                }
-            }
-        } else if let ast::Expr::Cast { data_type, .. } = expr {
-            // Rewrite TIMESTAMP WITH TIME ZONE to just TIMESTAMP
-            if let ast::DataType::Timestamp(_, ast::TimezoneInfo::WithTimeZone) = data_type {
-                *data_type = ast::DataType::Timestamp(None, ast::TimezoneInfo::None);
-            }
-        }
-        ControlFlow::<()>::Continue(())
-    });
-}
-
-/// Rewrite interval expressions to use full names instead of abbreviations for Spark compatibility
-/// e.g. "1 MONS" -> "1 MONTHS", "2 MINS" -> "2 MINUTES"
-fn rewrite_intervals(statement: &mut ast::Statement) {
-    let _ = visit_expressions_mut(statement, |expr: &mut ast::Expr| {
-        if let ast::Expr::Interval(interval) = expr {
-            if let ast::Expr::Value(value_with_span) = interval.value.as_ref() {
-                if let ast::Value::SingleQuotedString(interval_str) = &value_with_span.value {
-                    *interval.value = ast::Expr::Value(ast::ValueWithSpan {
-                        value: ast::Value::SingleQuotedString(expand_interval_abbreviations(
-                            interval_str,
-                        )),
-                        span: value_with_span.span.clone(),
-                    });
-                }
-            }
-        }
-        ControlFlow::<()>::Continue(())
-    });
-}
-
-lazy_static! {
-    /// Precompiled regexes for expanding interval abbreviations
-    static ref INTERVAL_ABBREV_REGEXES: Vec<(regex::Regex, &'static str)> = vec![
-        (regex::Regex::new(r"\b(\d+)\s+MONS\b").unwrap(), "${1} MONTHS"),
-        (regex::Regex::new(r"\b(\d+)\s+MON\b").unwrap(), "${1} MONTH"),
-        (regex::Regex::new(r"\b(\d+)\s+MINS\b").unwrap(), "${1} MINUTES"),
-        (regex::Regex::new(r"\b(\d+)\s+MIN\b").unwrap(), "${1} MINUTE"),
-        (regex::Regex::new(r"\b(\d+)\s+SECS\b").unwrap(), "${1} SECONDS"),
-        (regex::Regex::new(r"\b(\d+)\s+SEC\b").unwrap(), "${1} SECOND"),
-        (regex::Regex::new(r"\b(\d+)\s+HRS\b").unwrap(), "${1} HOURS"),
-        (regex::Regex::new(r"\b(\d+)\s+HR\b").unwrap(), "${1} HOUR"),
-        (regex::Regex::new(r"\b(\d+)\s+YRS\b").unwrap(), "${1} YEARS"),
-        (regex::Regex::new(r"\b(\d+)\s+YR\b").unwrap(), "${1} YEAR"),
-    ];
-}
-
-/// Ensure nested IS NULL/IS NOT NULL predicates are parenthesized for Spark compatibility
-/// e.g. `col IS NULL IS NULL` -> `(col IS NULL) IS NULL`
-fn rewrite_nested_is_null(statement: &mut ast::Statement) {
-    let _ = visit_expressions_mut(statement, |expr: &mut ast::Expr| {
-        if let ast::Expr::IsNull(inner) | ast::Expr::IsNotNull(inner) = expr {
-            if matches!(
-                inner.as_ref(),
-                ast::Expr::IsNull(_) | ast::Expr::IsNotNull(_)
-            ) {
-                let inner_clone = inner.as_ref().clone();
-                *inner = Box::new(ast::Expr::Nested(Box::new(inner_clone)));
-            }
-        }
-        ControlFlow::<()>::Continue(())
-    });
-}
-
-/// Expand interval abbreviations to full names for Spark compatibility
-fn expand_interval_abbreviations(interval_str: &str) -> String {
-    let mut result = interval_str.to_string();
-    for (re, replacement) in INTERVAL_ABBREV_REGEXES.iter() {
-        result = re.replace_all(&result, *replacement).to_string();
-    }
-    result
-}
-
-/// DataFusion logical plan which uses compound names when selecting from subquery:
-/// ```sql
-/// SELECT orders.customer_name, orders.customer_age FROM (SELECT orders.customer_name, orders.customer_age FROM orders)
-/// ```
-/// This is not a valid SQL, as `orders` isn't available once we get out of first query.
-/// So we rewrite logical plan to replace compound names with just the column names in projections
-/// that select data from another projection
-fn rewrite_subquery_column_identifiers(plan: LogicalPlan) -> Result<LogicalPlan> {
-    let processed_plan = plan
-        .transform_up_with_subqueries(|p| {
-            if let LogicalPlan::Projection(projection) = &p {
-                // only touch projections that read from another projection
-                if matches!(*projection.input, LogicalPlan::Projection { .. }) {
-                    let rewritten_exprs = projection
-                        .expr
-                        .iter()
-                        .map(|e| {
-                            e.clone()
-                                .transform_up(|mut ex| {
-                                    if let Expr::Column(c) = &mut ex {
-                                        *c = Column::from_name(c.name.clone());
-                                        Ok(Transformed::yes(ex))
-                                    } else {
-                                        Ok(Transformed::no(ex))
-                                    }
-                                })
-                                .map(|t| t.data)
-                        })
-                        .collect::<std::result::Result<_, _>>()?;
-                    let new_plan_node =
-                        p.with_new_exprs(rewritten_exprs, vec![(*projection.input).clone()])?;
-                    return Ok(Transformed::yes(new_plan_node));
-                }
-            }
-
-            Ok(Transformed::no(p))
-        })
-        .map_err(|e| {
-            VegaFusionError::vendor(format!(
-                "Failed to rewrite subquery column identifiers: {}",
-                e
-            ))
-        })?
-        .data;
-
-    Ok(processed_plan)
-}
-
-/// Rewrite datetime formatting expressions to be compatible with Spark
-fn rewrite_datetime_formatting(plan: LogicalPlan) -> Result<LogicalPlan> {
-    let processed_plan = plan
-        .transform_up_with_subqueries(|p| {
-            let p = p
-                .map_expressions(|expr| {
-                    expr.transform(&|e| {
-                        if let Expr::ScalarFunction(sf) = &e {
-                            if sf.name().eq_ignore_ascii_case("to_char") {
-                                let mut new_args = sf.args.clone();
-                                if new_args.len() > 1 {
-                                    if let Expr::Literal(ScalarValue::Utf8(Some(format_str)), _) =
-                                        &new_args[1]
-                                    {
-                                        let spark_format =
-                                            chrono_to_spark(format_str).map_err(|e| {
-                                                datafusion_common::DataFusionError::External(
-                                                    Box::new(e),
-                                                )
-                                            })?;
-                                        new_args[1] = Expr::Literal(
-                                            ScalarValue::Utf8(Some(spark_format)),
-                                            None,
-                                        );
-                                        let new_sf = ScalarFunction {
-                                            func: sf.func.clone(),
-                                            args: new_args,
-                                        };
-                                        return Ok(Transformed::yes(Expr::ScalarFunction(new_sf)));
-                                    }
-                                }
-                            }
-                        }
-                        Ok(Transformed::no(e))
-                    })
-                })?
-                .data;
-            Ok(Transformed::yes(p))
-        })
-        .map_err(|e: datafusion_common::DataFusionError| {
-            VegaFusionError::vendor(format!("Failed to rewrite datetime formatting: {}", e))
-        })?
-        .data;
-
-    Ok(processed_plan)
-}
-
-lazy_static! {
-    /// chrono-strftime → SparkSQL pattern map
-    static ref CHRONO_SPARK_MAP: HashMap<&'static str, &'static str> = {
-        HashMap::from([
-            // year
-            ("Y", "yyyy"), ("y", "yy"),
-            // month
-            ("m", "MM"), ("b", "MMM"), ("h", "MMM"), ("B", "MMMM"),
-            // day
-            ("d", "dd"), ("e", "d"), ("j", "DDD"),
-            // hour / minute / second
-            ("H", "HH"), ("I", "hh"), ("k", "H"), ("l", "h"),
-            ("M", "mm"), ("S", "ss"),
-            // week
-            ("U", "ww"), ("W", "ww"), ("V", "ww"),
-            // weekday names
-            ("a", "EEE"), ("A", "EEEE"),
-            // AM / PM
-            ("p", "a"), ("P", "a"),
-            // timezone
-            ("z", "Z"), ("Z", "z"),
-        ])
-    };
-}
-
-/// Convert a chrono `strftime` pattern (e.g. "%Y-%m-%d %H:%M:%S")
-/// to a Spark-SQL `date_format` pattern (e.g. "yyyy-MM-dd HH:mm:ss").
-fn chrono_to_spark(fmt: &str) -> Result<String> {
-    let mut out = String::with_capacity(fmt.len() * 2);
-    let mut chars = fmt.chars().peekable();
-
-    while let Some(c) = chars.next() {
-        if c != '%' {
-            // Check if this character needs to be quoted
-            // Common separators like -, :, space don't need quotes
-            // Letters and other special characters do need quotes
-            if !matches!(c, '-' | ':' | ' ' | '/' | ',' | '.') {
-                // Collect consecutive literal characters that need quoting
-                let mut literal = String::new();
-                literal.push(c);
-
-                // Continue collecting non-% characters that need quoting
-                while let Some(&next_c) = chars.peek() {
-                    if next_c == '%' || !matches!(next_c, '-' | ':' | ' ' | '/' | ',' | '.') {
-                        break;
-                    }
-                    literal.push(chars.next().unwrap());
-                }
-
-                // Wrap the literal string in single quotes
-                out.push_str(&format!("\\'{}\\'", &literal));
-            } else {
-                // Characters that don't need quoting (like -, :, space)
-                out.push(c);
-            }
-            continue;
-        }
-
-        // literal %%
-        if chars.peek() == Some(&'%') {
-            out.push('%');
-            chars.next();
-            continue;
-        }
-
-        // collect every char up to and incl. the terminating alpha
-        let mut modifier = String::new(); // '.', ':', '#' …
-        let mut digits = String::new(); // width like 3 in %3f
-        let mut letter = '\0';
-
-        while let Some(&ch) = chars.peek() {
-            chars.next();
-            if ch.is_ascii_alphabetic() {
-                letter = ch;
-                break;
-            } else if ch.is_ascii_digit() {
-                digits.push(ch);
-            } else {
-                modifier.push(ch);
-            }
-        }
-
-        match letter {
-            // -------- fractional seconds --------
-            'f' => {
-                // width: %f        -> 9  (nanoseconds)
-                //        %3f       -> 3  (fixed)
-                //        %.f       -> 9  (leading dot)
-                //        %.3f      -> 3  (leading dot, fixed)
-                let width: usize = digits.parse::<usize>().unwrap_or(9).clamp(1, 9);
-                if modifier.contains('.') {
-                    out.push('.');
-                }
-                out.push_str(&"S".repeat(width)); // S, SS, … SSSSSSSSS
-            }
-
-            // -------- time-zone offsets --------
-            'z' if modifier == ":" => out.push_str("XXX"), // %:z -> +09:30
-            'z' if modifier == "::" => out.push_str("XXXXX"), // %::z -> +09:30:00
-            'z' if modifier == ":::" => out.push_str("X"), // %:::z -> +09
-            'z' => out.push_str("Z"),                      // %z  -> +0930
-
-            // -------- everything else that has a direct map --------
-            _ => {
-                let key = &format!("{}{}", modifier, letter); // e.g. ""+"Y", ".f", ":z"
-                match CHRONO_SPARK_MAP.get(key.as_str()) {
-                    Some(rep) => out.push_str(rep),
-                    None => {
-                        return Err(VegaFusionError::vendor(format!(
-                            "unsupported specifier %{}",
-                            key
-                        )))
-                    }
-                }
-            }
-        }
-    }
-    Ok(out)
-}
-
-/// Rewrite column identifiers to properly quote column names with spaces or special characters
-/// Spark SQL requires column names with spaces or special characters to be quoted with backticks
-fn rewrite_column_identifiers(statement: &mut ast::Statement) {
-    // Helper function to quote an identifier if needed
-    let quote_if_needed = |ident: &mut ast::Ident| {
-        if needs_quoting(&ident.value) {
-            ident.quote_style = Some('`');
-        }
-    };
-
-    // First handle expressions (column references)
-    let _ = visit_expressions_mut(statement, |expr: &mut ast::Expr| {
-        match expr {
-            ast::Expr::Identifier(ident) => {
-                quote_if_needed(ident);
-            }
-            ast::Expr::CompoundIdentifier(identifiers) => {
-                for ident in identifiers {
-                    quote_if_needed(ident);
-                }
-            }
-            _ => {}
-        }
-        ControlFlow::<()>::Continue(())
-    });
-
-    // Then handle aliases in all SELECT projections (including nested subqueries)
-    quote_aliases_recursively(statement, &quote_if_needed);
-}
-
-/// Recursively traverse the AST to quote aliases in all SELECT statements
-fn quote_aliases_recursively(
-    statement: &mut ast::Statement,
-    quote_if_needed: &impl Fn(&mut ast::Ident),
-) {
-    if let ast::Statement::Query(query) = statement {
-        quote_aliases_in_query(query, quote_if_needed);
-    }
-}
-
-/// Quote aliases in a query and all its nested subqueries
-fn quote_aliases_in_query(query: &mut ast::Query, quote_if_needed: &impl Fn(&mut ast::Ident)) {
-    quote_aliases_in_set_expr(&mut query.body, quote_if_needed);
-}
-
-/// Quote aliases in a set expression (SELECT, UNION, etc.)
-fn quote_aliases_in_set_expr(
-    set_expr: &mut ast::SetExpr,
-    quote_if_needed: &impl Fn(&mut ast::Ident),
-) {
-    match set_expr {
-        ast::SetExpr::Select(select) => {
-            quote_aliases_in_select(select, quote_if_needed);
-        }
-        ast::SetExpr::Query(query) => {
-            quote_aliases_in_query(query, quote_if_needed);
-        }
-        ast::SetExpr::SetOperation { left, right, .. } => {
-            quote_aliases_in_set_expr(left, quote_if_needed);
-            quote_aliases_in_set_expr(right, quote_if_needed);
-        }
-        _ => {
-            // Other set expression types don't have aliases to quote
-        }
-    }
-}
-
-/// Quote aliases in a SELECT statement and handle nested subqueries
-fn quote_aliases_in_select(select: &mut ast::Select, quote_if_needed: &impl Fn(&mut ast::Ident)) {
-    // Quote aliases in the projection list
-    for projection in &mut select.projection {
-        if let ast::SelectItem::ExprWithAlias { alias, .. } = projection {
-            quote_if_needed(alias);
-        }
-    }
-
-    // Recursively handle subqueries in the FROM clause
-    for table_with_joins in &mut select.from {
-        quote_aliases_in_table_with_joins(table_with_joins, quote_if_needed);
-    }
-}
-
-/// Quote aliases in table references and their joins
-fn quote_aliases_in_table_with_joins(
-    table_with_joins: &mut ast::TableWithJoins,
-    quote_if_needed: &impl Fn(&mut ast::Ident),
-) {
-    quote_aliases_in_table_factor(&mut table_with_joins.relation, quote_if_needed);
-
-    for join in &mut table_with_joins.joins {
-        quote_aliases_in_table_factor(&mut join.relation, quote_if_needed);
-    }
-}
-
-/// Quote aliases in table factors (tables, subqueries, etc.)
-fn quote_aliases_in_table_factor(
-    table_factor: &mut ast::TableFactor,
-    quote_if_needed: &impl Fn(&mut ast::Ident),
-) {
-    match table_factor {
-        ast::TableFactor::Derived { subquery, .. } => {
-            quote_aliases_in_query(subquery, quote_if_needed);
-        }
-        ast::TableFactor::NestedJoin {
-            table_with_joins, ..
-        } => {
-            quote_aliases_in_table_with_joins(table_with_joins, quote_if_needed);
-        }
-        _ => {
-            // Other table factor types don't have nested queries to process
-        }
-    }
-}
-
-/// Check if a column name needs to be quoted in Spark SQL
-fn needs_quoting(name: &str) -> bool {
-    // Don't quote function calls (contain parentheses)
-    if name.contains('(') || name.contains(')') {
-        return false;
-    }
-
-    // Check if the name contains spaces, hyphens, or starts with a digit
-    name.contains(' ')
-        || name.contains('-')
-        || name.chars().next().map_or(false, |c| c.is_ascii_digit())
-        || has_special_chars_needing_quotes(name)
-        || is_sql_reserved_word(name)
-}
-
-/// Check if a name contains special characters that require quoting
-/// This is more restrictive than the previous version to avoid quoting function calls
-fn has_special_chars_needing_quotes(name: &str) -> bool {
-    // Only quote for specific problematic characters, not all non-alphanumeric
-    name.chars().any(|c| {
-        matches!(
-            c,
-            '!' | '@'
-                | '#'
-                | '$'
-                | '%'
-                | '^'
-                | '&'
-                | '*'
-                | '+'
-                | '='
-                | '['
-                | ']'
-                | '{'
-                | '}'
-                | '|'
-                | '\\'
-                | ':'
-                | ';'
-                | '"'
-                | '\''
-                | '<'
-                | '>'
-                | ','
-                | '.'
-                | '?'
-                | '/'
-                | '~'
-                | '`'
-        )
-    })
-}
-
-lazy_static! {
-    /// SQL reserved words that need quoting in Spark SQL
-    /// This includes both ANSI SQL and Spark SQL specific reserved words
-    static ref RESERVED_WORDS: std::collections::HashSet<&'static str> = {
-        std::collections::HashSet::from([
-            // ANSI SQL reserved words
-            "SELECT", "FROM", "WHERE", "GROUP", "BY", "HAVING", "ORDER", "LIMIT",
-            "INSERT", "UPDATE", "DELETE", "CREATE", "DROP", "ALTER", "TABLE", "INDEX",
-            "VIEW", "DATABASE", "SCHEMA", "COLUMN", "CONSTRAINT", "PRIMARY", "KEY",
-            "FOREIGN", "REFERENCES", "UNIQUE", "NOT", "NULL", "DEFAULT", "CHECK",
-            "UNION", "INTERSECT", "EXCEPT", "JOIN", "INNER", "LEFT", "RIGHT", "FULL",
-            "OUTER", "CROSS", "ON", "USING", "AS", "DISTINCT", "ALL", "ANY", "SOME",
-            "EXISTS", "IN", "BETWEEN", "LIKE", "IS", "AND", "OR", "CASE", "WHEN",
-            "THEN", "ELSE", "END", "IF", "CAST", "CONVERT", "SUBSTRING", "TRIM",
-            "UPPER", "LOWER", "LENGTH", "CHAR", "VARCHAR", "TEXT", "INTEGER", "INT",
-            "BIGINT", "SMALLINT", "DECIMAL", "NUMERIC", "FLOAT", "DOUBLE", "REAL",
-            "BOOLEAN", "DATE", "TIME", "TIMESTAMP", "INTERVAL", "YEAR", "MONTH",
-            "DAY", "HOUR", "MINUTE", "SECOND", "TRUE", "FALSE", "UNKNOWN",
-
-            // Spark SQL specific reserved words
-            "ARRAY", "MAP", "STRUCT", "BINARY", "TINYINT", "STRING", "PARTITION",
-            "PARTITIONS", "CLUSTER", "DISTRIBUTE", "SORT", "TABLESAMPLE", "LATERAL",
-            "WINDOW", "OVER", "ROW", "ROWS", "RANGE", "PRECEDING", "FOLLOWING",
-            "UNBOUNDED", "CURRENT", "FIRST", "LAST", "IGNORE", "RESPECT", "NULLS",
-            "ROLLUP", "CUBE", "GROUPING", "SETS", "PIVOT", "UNPIVOT", "TRANSFORM",
-            "REDUCE", "AGGREGATE", "FILTER", "WITHIN", "COLLECT_LIST", "COLLECT_SET",
-            "EXPLODE", "INLINE", "POSEXPLODE", "STACK", "JSON_TUPLE", "GET_JSON_OBJECT",
-            "REGEXP_EXTRACT", "REGEXP_REPLACE", "SPLIT", "SIZE", "SORT_ARRAY",
-            "ARRAY_CONTAINS", "MAP_KEYS", "MAP_VALUES", "NAMED_STRUCT", "STRUCT",
-            "DESCRIBE", "DESC", "EXPLAIN", "ANALYZE", "CACHE", "UNCACHE", "REFRESH",
-            "SHOW", "MSCK", "REPAIR", "RECOVER", "EXPORT", "IMPORT", "LOAD", "UNLOAD",
-            "SET", "RESET", "ADD", "LIST", "WITH", "RECURSIVE", "TEMPORARY", "TEMP",
-            "GLOBAL", "LOCAL", "LOCATION", "COMMENT", "TBLPROPERTIES", "SERDEPROPERTIES",
-            "STORED", "INPUTFORMAT", "OUTPUTFORMAT", "SERDE", "DELIMITED", "FIELDS",
-            "TERMINATED", "ESCAPED", "COLLECTION", "ITEMS", "KEYS", "LINES", "DEFINED",
-            "RECORDREADER", "RECORDWRITER", "ROW_FORMAT", "STORED_AS", "CLUSTERED",
-            "SORTED", "INTO", "BUCKETS", "SKEWED", "DIRECTORIES", "PURGE", "ARCHIVE",
-            "UNARCHIVE", "TOUCH", "COMPACT", "CONCATENATE", "CHANGE", "REPLACE",
-            "COLUMNS", "RLIKE", "REGEXP", "FUNCTION", "MACRO", "AGGREGATE", "RETURNS",
-            "LANGUAGE", "DETERMINISTIC", "SQL", "CONTAINS", "READS", "MODIFIES",
-            "NO", "CALLED", "INPUT", "SPECIFIC", "EXTERNAL", "SECURITY", "DEFINER",
-            "INVOKER", "PATH", "ISOLATION", "LEVEL", "READ", "WRITE", "ONLY",
-            "COMMITTED", "UNCOMMITTED", "REPEATABLE", "SERIALIZABLE", "WORK",
-            "TRANSACTION", "START", "COMMIT", "ROLLBACK", "SAVEPOINT", "RELEASE"
-        ])
-    };
-}
-
-/// Check if a name is a SQL reserved word that needs quoting in Spark SQL
-fn is_sql_reserved_word(name: &str) -> bool {
-    RESERVED_WORDS.contains(name.to_uppercase().as_str())
-}
diff --git a/vegafusion-runtime/src/task_graph/runtime.rs b/vegafusion-runtime/src/task_graph/runtime.rs
index 02ad6e7f9..9e047a1c4 100644
--- a/vegafusion-runtime/src/task_graph/runtime.rs
+++ b/vegafusion-runtime/src/task_graph/runtime.rs
@@ -57,11 +57,10 @@ impl VegaFusionRuntime {
         task_graph: Arc<TaskGraph>,
         node_value_index: &NodeValueIndex,
         inline_datasets: HashMap<String, VegaFusionDataset>,
-        plan_executor: Option<Arc<dyn PlanExecutor>>,
     ) -> Result<TaskValue> {
         // We shouldn't panic inside get_or_compute_node_value, but since this may be used
         // in a server context, wrap in catch_unwind just in case.
-        let executor = plan_executor.unwrap_or_else(|| self.plan_executor());
+        let executor = self.plan_executor();
         let node_value = AssertUnwindSafe(get_or_compute_node_value(
             task_graph,
             node_value_index.node_index as usize,
@@ -136,17 +135,11 @@ impl VegaFusionRuntimeTrait for VegaFusionRuntime {
                 // Clone task_graph and task_graph_runtime for use in closure
                 let task_graph_runtime = task_graph_runtime.clone();
                 let task_graph = task_graph.clone();
-                let plan_executor_clone = self.plan_executor();
 
                 Ok(async move {
                     let value = task_graph_runtime
                         .clone()
-                        .get_node_value(
-                            task_graph,
-                            node_value_index,
-                            inline_datasets.clone(),
-                            Some(plan_executor_clone),
-                        )
+                        .get_node_value(task_graph, node_value_index, inline_datasets.clone())
                         .await?;
 
                     Ok::<_, VegaFusionError>(NamedTaskValue {
diff --git a/vegafusion-runtime/tests/test_image_comparison.rs b/vegafusion-runtime/tests/test_image_comparison.rs
index d8d11ea58..332654201 100644
--- a/vegafusion-runtime/tests/test_image_comparison.rs
+++ b/vegafusion-runtime/tests/test_image_comparison.rs
@@ -1466,12 +1466,7 @@ async fn check_spec_sequence(
     for var in &spec_plan.comm_plan.server_to_client {
         let node_index = task_graph_mapping.get(var).unwrap();
         let value = runtime
-            .get_node_value(
-                Arc::new(task_graph.clone()),
-                node_index,
-                Default::default(),
-                None,
-            )
+            .get_node_value(Arc::new(task_graph.clone()), node_index, Default::default())
             .await
             .expect("Failed to get node value");
 
@@ -1543,12 +1538,7 @@ async fn check_spec_sequence(
         let mut server_to_client_value_batch = HashMap::new();
         for (var, node_index) in watch_vars.iter().zip(&watch_indices) {
             let value = runtime
-                .get_node_value(
-                    Arc::new(task_graph.clone()),
-                    node_index,
-                    Default::default(),
-                    None,
-                )
+                .get_node_value(Arc::new(task_graph.clone()), node_index, Default::default())
                 .await
                 .unwrap();
 
diff --git a/vegafusion-runtime/tests/test_planning.rs b/vegafusion-runtime/tests/test_planning.rs
index 1f332ed73..8219ee8c0 100644
--- a/vegafusion-runtime/tests/test_planning.rs
+++ b/vegafusion-runtime/tests/test_planning.rs
@@ -69,7 +69,6 @@ async fn test_extract_server_data() {
                 .get(&(Variable::new_data("data_3"), Vec::new()))
                 .unwrap(),
             Default::default(),
-            None,
         )
         .await
         .unwrap();
@@ -86,7 +85,6 @@ async fn test_extract_server_data() {
                 ))
                 .unwrap(),
             Default::default(),
-            None,
         )
         .await
         .unwrap();
diff --git a/vegafusion-runtime/tests/test_spark_sql.rs b/vegafusion-runtime/tests/test_spark_sql.rs
deleted file mode 100644
index 068be069c..000000000
--- a/vegafusion-runtime/tests/test_spark_sql.rs
+++ /dev/null
@@ -1,334 +0,0 @@
-use datafusion::datasource::{provider_as_source, MemTable};
-use datafusion::prelude::{DataFrame, SessionContext};
-use datafusion_expr::Expr;
-use datafusion_expr::{col, lit, LogicalPlanBuilder};
-use datafusion_functions::expr_fn::{to_char, to_timestamp_seconds};
-use std::sync::Arc;
-use vegafusion_common::arrow::array::RecordBatch;
-use vegafusion_common::arrow::datatypes::{DataType, Field, Schema, TimeUnit};
-use vegafusion_common::column::flat_col;
-use vegafusion_runtime::data::util::DataFrameUtils;
-use vegafusion_runtime::datafusion::udfs::datetime::make_timestamptz::make_timestamptz;
-use vegafusion_runtime::expression::compiler::utils::ExprHelpers;
-use vegafusion_runtime::sql::logical_plan_to_spark_sql;
-
-async fn create_test_dataframe(
-    schema_fields: Vec<Field>,
-) -> Result<DataFrame, Box<dyn std::error::Error>> {
-    let ctx = SessionContext::new();
-
-    let schema = Arc::new(Schema::new(schema_fields));
-
-    let empty_batch = RecordBatch::new_empty(schema.clone());
-    let mem_table = MemTable::try_new(schema.clone(), vec![vec![empty_batch]])?;
-
-    let base_plan =
-        LogicalPlanBuilder::scan("test_table", provider_as_source(Arc::new(mem_table)), None)?
-            .build()?;
-
-    Ok(DataFrame::new(ctx.state(), base_plan))
-}
-
-#[tokio::test]
-async fn test_logical_plan_to_spark_sql_rewrites_row_number(
-) -> Result<(), Box<dyn std::error::Error>> {
-    let schema_fields = vec![
-        Field::new("id", DataType::Int32, false),
-        Field::new("name", DataType::Utf8, false),
-        Field::new("value", DataType::Float64, false),
-    ];
-
-    let df = create_test_dataframe(schema_fields).await?;
-
-    let indexed_df = df.filter(col("value").gt(lit(0.0)))?.with_index()?;
-
-    let plan = indexed_df.logical_plan().clone();
-    let spark_sql = logical_plan_to_spark_sql(&plan)?;
-
-    let expected_sql = "SELECT row_number() OVER (ORDER BY monotonically_increasing_id()) AS _vf_order, test_table.id, test_table.name, test_table.value FROM test_table WHERE test_table.value > 0.0";
-
-    assert_eq!(
-        spark_sql.trim(),
-        expected_sql,
-        "Generated SQL should use ORDER BY monotonically_increasing_id() as window for row_number()"
-    );
-
-    Ok(())
-}
-
-#[tokio::test]
-async fn test_logical_plan_to_spark_sql_rewrites_inf_and_nan(
-) -> Result<(), Box<dyn std::error::Error>> {
-    let schema_fields = vec![
-        Field::new("id", DataType::Int32, false),
-        Field::new("value", DataType::Float64, false),
-    ];
-
-    let df = create_test_dataframe(schema_fields).await?;
-
-    // Create a query that will generate NaN and infinity literals in the logical plan
-    let filtered_df = df
-        .filter(col("value").gt(lit(f64::NAN)))?
-        .filter(col("value").lt(lit(f64::INFINITY)))?
-        .filter(col("value").gt(lit(f64::NEG_INFINITY)))?;
-
-    let plan = filtered_df.logical_plan().clone();
-    let spark_sql = logical_plan_to_spark_sql(&plan)?;
-
-    let expected_sql = "SELECT * FROM test_table WHERE test_table.value > float('-inf') AND test_table.value < float('inf') AND test_table.value > float('NaN')";
-
-    assert_eq!(
-        spark_sql, expected_sql,
-        "Should wrap NaN and Infinity literals in float()"
-    );
-    Ok(())
-}
-
-#[tokio::test]
-async fn test_logical_plan_to_spark_sql_rewrites_subquery_column_identifiers(
-) -> Result<(), Box<dyn std::error::Error>> {
-    let schema_fields = vec![
-        Field::new("customer_name", DataType::Utf8, false),
-        Field::new("customer_age", DataType::Float32, false),
-    ];
-
-    let df = create_test_dataframe(schema_fields).await?;
-
-    // Create nested projections that would generate compound column names
-    let nested_df = df
-        .select(vec![flat_col("customer_name"), flat_col("customer_age")])?
-        .select(vec![flat_col("customer_name"), flat_col("customer_age")])?;
-
-    let plan = nested_df.logical_plan().clone();
-    let spark_sql = logical_plan_to_spark_sql(&plan)?;
-
-    let expected_sql = "SELECT customer_name, customer_age FROM (SELECT test_table.customer_name, test_table.customer_age FROM test_table)";
-
-    assert_eq!(
-        spark_sql.trim(),
-        expected_sql,
-        "Generated SQL should rewrite subquery column identifiers correctly"
-    );
-
-    Ok(())
-}
-
-#[tokio::test]
-async fn test_logical_plan_to_spark_sql_chrono_formatting() -> Result<(), Box<dyn std::error::Error>>
-{
-    let schema_fields = vec![Field::new(
-        "timestamp_col",
-        DataType::Timestamp(TimeUnit::Millisecond, Some("UTC".into())),
-        false,
-    )];
-
-    let df = create_test_dataframe(schema_fields).await?;
-
-    // Test basic datetime format conversion: %Y-%m-%d %H:%M:%S -> yyyy-MM-dd HH:mm:ss
-    let df_basic = df.clone().select(vec![to_char(
-        col("timestamp_col"),
-        lit("%Y-%m-%d %H:%M:%S"),
-    )])?;
-
-    let plan_basic = df_basic.logical_plan().clone();
-    let spark_sql_basic = logical_plan_to_spark_sql(&plan_basic)?;
-
-    assert!(
-        spark_sql_basic.contains("yyyy-MM-dd HH:mm:ss"),
-        "Basic datetime format should be converted to Spark format. Got: {}",
-        spark_sql_basic
-    );
-
-    // Test fractional seconds: %.3f -> .SSS
-    let df_frac = df
-        .clone()
-        .select(vec![to_char(col("timestamp_col"), lit("%.3f"))])?;
-
-    let plan_frac = df_frac.logical_plan().clone();
-    let spark_sql_frac = logical_plan_to_spark_sql(&plan_frac)?;
-
-    assert!(
-        spark_sql_frac.contains(".SSS"),
-        "Fractional seconds format should be converted to Spark format. Got: {}",
-        spark_sql_frac
-    );
-
-    // Test full ISO format: %Y-%m-%dT%H:%M:%S%.f%:z -> yyyy-MM-dd'T'HH:mm:ss.SSSSSSSSSXXX
-    let df_iso = df.clone().select(vec![to_char(
-        col("timestamp_col"),
-        lit("%Y-%m-%dT%H:%M:%S%.f%:z"),
-    )])?;
-
-    let plan_iso = df_iso.logical_plan().clone();
-    let spark_sql_iso = logical_plan_to_spark_sql(&plan_iso)?;
-
-    assert!(
-        // Double single quote because it will be inside SQL string and has to be escaped
-        spark_sql_iso.contains("yyyy-MM-dd\\'T\\'HH:mm:ss.SSSSSSSSSXXX"),
-        "ISO datetime format should be converted to Spark format. Got: {}",
-        spark_sql_iso
-    );
-
-    Ok(())
-}
-
-#[tokio::test]
-async fn test_logical_plan_to_spark_sql_rewrites_timestamps(
-) -> Result<(), Box<dyn std::error::Error>> {
-    let schema_fields = vec![Field::new(
-        "order_date",
-        DataType::Timestamp(TimeUnit::Millisecond, None),
-        false,
-    )];
-
-    let df = create_test_dataframe(schema_fields).await?;
-    let df_schema = df.schema().clone();
-
-    // Create operations that will generate make_timestamptz function calls, TIMESTAMP WITH TIME ZONE casts, and to_timestamp_seconds calls
-    let timestamp_df = df.select(vec![
-        // This should create a cast to TIMESTAMP WITH TIME ZONE, which should be rewritten to TIMESTAMP
-        col("order_date")
-            .try_cast_to(
-                &DataType::Timestamp(
-                    TimeUnit::Millisecond,
-                    Some("America/Los_Angeles".to_string().into()),
-                ),
-                &df_schema,
-            )?
-            .alias("order_date_tz"),
-        // This should create a make_timestamptz call, which should be rewritten to make_timestamp with 7th arg dropped
-        make_timestamptz(
-            lit(2023),
-            lit(12),
-            lit(25),
-            lit(10),
-            lit(30),
-            lit(45),
-            lit(123), // This 7th argument (milliseconds) should be dropped
-            "UTC",
-        )
-        .alias("made_timestamp")
-        .into(),
-        // This should create a to_timestamp_seconds call, which should be rewritten to to_timestamp
-        to_timestamp_seconds(vec![lit("2023-12-25 10:30:45")]).alias("parsed_timestamp"),
-    ])?;
-
-    let plan = timestamp_df.logical_plan().clone();
-    let spark_sql = logical_plan_to_spark_sql(&plan)?;
-
-    // Check that make_timestamptz was rewritten to make_timestamp and to_timestamp_seconds was rewritten to to_timestamp
-    assert_eq!(
-        spark_sql,
-        "SELECT TRY_CAST(test_table.order_date AS TIMESTAMP) AS order_date_tz, make_timestamp(2023, 12, 25, 10, 30, 45, 'UTC') AS made_timestamp, to_timestamp('2023-12-25 10:30:45') AS parsed_timestamp FROM test_table",
-        "Generated SQL should rewrite timestamp functions for Spark compatibility"
-    );
-
-    Ok(())
-}
-
-#[tokio::test]
-async fn test_logical_plan_to_spark_sql_rewrites_intervals(
-) -> Result<(), Box<dyn std::error::Error>> {
-    let schema_fields = vec![Field::new(
-        "timestamp_col",
-        DataType::Timestamp(TimeUnit::Millisecond, None),
-        false,
-    )];
-
-    let df = create_test_dataframe(schema_fields).await?;
-
-    // Create a query that will generate interval expressions with abbreviated names
-    let interval_df = df.select(vec![
-        col("timestamp_col") + lit(datafusion_common::ScalarValue::IntervalYearMonth(Some(1))),
-    ])?;
-
-    let plan = interval_df.logical_plan().clone();
-    let spark_sql = logical_plan_to_spark_sql(&plan)?;
-
-    let expected_sql =
-        "SELECT test_table.timestamp_col + INTERVAL '0 YEARS 1 MONTHS' FROM test_table";
-
-    assert_eq!(
-        spark_sql, expected_sql,
-        "Generated SQL should expand interval abbreviations to full names"
-    );
-
-    Ok(())
-}
-
-#[tokio::test]
-async fn test_logical_plan_to_spark_sql_quotes_column_identifiers(
-) -> Result<(), Box<dyn std::error::Error>> {
-    let schema_fields = vec![
-        Field::new("customer name", DataType::Utf8, false), // space in name
-        Field::new("customer-email", DataType::Utf8, false), // hyphen in name
-        Field::new("select", DataType::Utf8, false),        // SQL reserved word
-        Field::new("from", DataType::Utf8, false),          // SQL reserved word
-        Field::new("Total Amount", DataType::Float64, false), // space and capital letters
-        Field::new("123field", DataType::Int32, false),     // starts with digit
-        Field::new("normal_field", DataType::Int32, false), // normal field (shouldn't be quoted)
-    ];
-
-    let df = create_test_dataframe(schema_fields).await?;
-
-    // Create a nested query structure to test recursive alias quoting
-    // This creates: SELECT ... FROM (SELECT ... AS "nested alias" FROM (SELECT ... FROM test_table))
-    let inner_df = df.select(vec![
-        flat_col("customer name").alias("inner customer name"), // nested alias with space
-        flat_col("select").alias("inner select"),               // nested alias with reserved word
-        flat_col("normal_field").alias("inner_normal"),         // normal nested alias
-    ])?;
-
-    let middle_df = inner_df.select(vec![
-        flat_col("inner customer name").alias("middle customer name"), // another level of nesting
-        flat_col("inner select").alias("middle from"),                 // reserved word alias
-        flat_col("inner_normal"),                                      // pass through
-    ])?;
-
-    let result_df = middle_df.select(vec![
-        flat_col("middle customer name").alias("final name"), // final alias with space
-        flat_col("middle from").alias("final location"),      // final alias with space
-        flat_col("inner_normal").alias("final_normal"),
-    ])?;
-
-    let plan = result_df.logical_plan().clone();
-    let spark_sql = logical_plan_to_spark_sql(&plan)?;
-
-    // The expected SQL should have quoted aliases at all levels of nesting
-    let expected_sql = "SELECT `middle customer name` AS `final name`, `middle from` AS `final location`, inner_normal AS final_normal FROM (SELECT `inner customer name` AS `middle customer name`, `inner select` AS `middle from`, inner_normal FROM (SELECT test_table.`customer name` AS `inner customer name`, test_table.`select` AS `inner select`, test_table.normal_field AS inner_normal FROM test_table))";
-
-    assert_eq!(
-        spark_sql.trim(),
-        expected_sql,
-        "Generated SQL should properly quote column names and aliases at all nesting levels"
-    );
-
-    Ok(())
-}
-
-#[tokio::test]
-async fn test_logical_plan_to_spark_sql_parenthesizes_nested_is_null(
-) -> Result<(), Box<dyn std::error::Error>> {
-    let schema_fields = vec![Field::new("IMDB Rating", DataType::Utf8, false)];
-
-    let df = create_test_dataframe(schema_fields).await?;
-
-    // Build nested IS NULL: (test_table.`IMDB Rating` IS NULL) IS NULL
-    let nested_is_null = Expr::IsNull(Box::new(Expr::IsNull(Box::new(col("IMDB Rating")))));
-    let expr = nested_is_null.alias("check_null");
-
-    let df = df.select(vec![expr])?;
-    let plan = df.logical_plan().clone();
-    let spark_sql = logical_plan_to_spark_sql(&plan)?;
-
-    let expected_sql =
-        "SELECT (test_table.`IMDB Rating` IS NULL) IS NULL AS check_null FROM test_table";
-
-    assert_eq!(
-        spark_sql.trim(),
-        expected_sql,
-        "Generated SQL should parenthesize nested IS NULL for Spark compatibility"
-    );
-
-    Ok(())
-}
diff --git a/vegafusion-runtime/tests/test_task_graph_runtime.rs b/vegafusion-runtime/tests/test_task_graph_runtime.rs
index 39c5a5f37..7e0023c0a 100644
--- a/vegafusion-runtime/tests/test_task_graph_runtime.rs
+++ b/vegafusion-runtime/tests/test_task_graph_runtime.rs
@@ -83,12 +83,7 @@ async fn try_it() {
     let graph_runtime = VegaFusionRuntime::default();
     // let result = graph_runtime.get_node_value(graph, 2, None).await.unwrap();
     let result = graph_runtime
-        .get_node_value(
-            graph,
-            &NodeValueIndex::new(2, Some(0)),
-            Default::default(),
-            None,
-        )
+        .get_node_value(graph, &NodeValueIndex::new(2, Some(0)), Default::default())
         .await
         .unwrap();
 
@@ -145,12 +140,7 @@ async fn try_it_from_spec() {
 
     let graph_runtime = VegaFusionRuntime::default();
     let result = graph_runtime
-        .get_node_value(
-            graph,
-            &NodeValueIndex::new(2, Some(0)),
-            Default::default(),
-            None,
-        )
+        .get_node_value(graph, &NodeValueIndex::new(2, Some(0)), Default::default())
         .await
         .unwrap();
     println!("result: {result:?}");
diff --git a/vegafusion-server/Cargo.toml b/vegafusion-server/Cargo.toml
index d5c10dc2e..0346f01ad 100644
--- a/vegafusion-server/Cargo.toml
+++ b/vegafusion-server/Cargo.toml
@@ -5,7 +5,7 @@ path = "src/main.rs"
 [package]
 name = "vegafusion-server"
 license = "BSD-3-Clause"
-version = "2.0.8"
+version = "2.0.3"
 edition = "2021"
 description = "VegaFusion Server"
 repository = "https://github.com/vega/vegafusion"
@@ -34,16 +34,16 @@ workspace = true
 
 [dependencies.vegafusion-common]
 path = "../vegafusion-common"
-version = "2.0.8"
+version = "2.0.3"
 
 [dependencies.vegafusion-core]
 path = "../vegafusion-core"
 features = ["tonic_support"]
-version = "2.0.8"
+version = "2.0.3"
 
 [dependencies.vegafusion-runtime]
 path = "../vegafusion-runtime"
-version = "2.0.8"
+version = "2.0.3"
 
 [dependencies.tokio]
 workspace = true
diff --git a/vegafusion-wasm/Cargo.toml b/vegafusion-wasm/Cargo.toml
index 9d7db6279..b569484cb 100644
--- a/vegafusion-wasm/Cargo.toml
+++ b/vegafusion-wasm/Cargo.toml
@@ -1,7 +1,7 @@
 [package]
 name = "vegafusion-wasm"
 license = "BSD-3-Clause"
-version = "2.0.8"
+version = "2.0.3"
 edition = "2021"
 description = "VegaFusion WASM package for embedding Vega charts in the browser with a connection to a VegaFusion Runtime"
 
@@ -41,15 +41,15 @@ workspace = true
 [dependencies.vegafusion-common]
 path = "../vegafusion-common"
 features = ["json"]
-version = "2.0.8"
+version = "2.0.3"
 
 [dependencies.vegafusion-core]
 path = "../vegafusion-core"
-version = "2.0.8"
+version = "2.0.3"
 
 [dependencies.vegafusion-runtime]
 path = "../vegafusion-runtime"
-version = "2.0.8"
+version = "2.0.3"
 default-features = false
 features = ["http-wasm"]
 
diff --git a/vegafusion-wasm/package-lock.json b/vegafusion-wasm/package-lock.json
index 69fe23e55..1e5ea54c6 100644
--- a/vegafusion-wasm/package-lock.json
+++ b/vegafusion-wasm/package-lock.json
@@ -1,6 +1,6 @@
 {
   "name": "vegafusion-wasm",
-  "version": "2.0.8",
+  "version": "2.0.3",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
diff --git a/vegafusion-wasm/package.json b/vegafusion-wasm/package.json
index 581d05791..2784d02c5 100644
--- a/vegafusion-wasm/package.json
+++ b/vegafusion-wasm/package.json
@@ -1,6 +1,6 @@
 {
   "name": "vegafusion-wasm",
-  "version": "2.0.8",
+  "version": "2.0.3",
   "author": "Jon Mease <jon@vegafusion.io> (https://jonmmease.dev)",
   "license": "BSD-3-Clause",
   "description": "Wasm library for interfacing with VegaFusion",