diff --git a/README.md b/README.md
index b0cc61c91d..af4df06175 100644
--- a/README.md
+++ b/README.md
@@ -148,7 +148,7 @@ The list below contains the functionality that contributors are planning to deve
   * [x] [Hive (community plugin)](https://github.com/baineng/feast-hive)
   * [x] [Postgres (community plugin)](https://github.com/nossrannug/feast-postgres)
   * [x] [Spark (community plugin)](https://docs.feast.dev/reference/data-sources/spark)
-  * [x] Kafka / Kinesis sources (via [push support into the online store](https://docs.feast.dev/reference/data-sources/push)
+  * [x] Kafka / Kinesis sources (via [push support into the online store](https://docs.feast.dev/reference/data-sources/push))
   * [ ] HTTP source
 * **Offline Stores**
   * [x] [Snowflake](https://docs.feast.dev/reference/offline-stores/snowflake)
diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index e73996665e..c6a3422ecd 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -85,7 +85,6 @@
   * [Go-based feature retrieval](reference/feature-servers/go-feature-retrieval.md)
 * [\[Alpha\] Data quality monitoring](reference/dqm.md)
 * [\[Alpha\] On demand feature view](reference/alpha-on-demand-feature-view.md)
-* [\[Alpha\] Stream ingestion](reference/alpha-stream-ingestion.md)
 * [\[Alpha\] AWS Lambda feature server](reference/alpha-aws-lambda-feature-server.md)
 * [Feast CLI reference](reference/feast-cli-commands.md)
 * [Python API reference](http://rtd.feast.dev)
diff --git a/docs/getting-started/third-party-integrations.md b/docs/getting-started/third-party-integrations.md
index a731fd85dc..0c233d7b69 100644
--- a/docs/getting-started/third-party-integrations.md
+++ b/docs/getting-started/third-party-integrations.md
@@ -21,7 +21,7 @@ Don't see your offline store or online store of choice here? Check out our guide
 * [x] [Hive (community plugin)](https://github.com/baineng/feast-hive)
 * [x] [Postgres (community plugin)](https://github.com/nossrannug/feast-postgres)
 * [x] [Spark (community plugin)](https://docs.feast.dev/reference/data-sources/spark)
-* [x] Kafka / Kinesis sources (via [push support into the online store](https://docs.feast.dev/reference/data-sources/push)
+* [x] Kafka / Kinesis sources (via [push support into the online store](https://docs.feast.dev/reference/data-sources/push))
 * [ ] HTTP source
 
 ### Offline Stores
diff --git a/docs/roadmap.md b/docs/roadmap.md
index 080cf16c02..3eb181c0da 100644
--- a/docs/roadmap.md
+++ b/docs/roadmap.md
@@ -16,7 +16,7 @@ The list below contains the functionality that contributors are planning to deve
   * [x] [Hive (community plugin)](https://github.com/baineng/feast-hive)
   * [x] [Postgres (community plugin)](https://github.com/nossrannug/feast-postgres)
   * [x] [Spark (community plugin)](https://docs.feast.dev/reference/data-sources/spark)
-  * [x] Kafka / Kinesis sources (via [push support into the online store](https://docs.feast.dev/reference/data-sources/push)
+  * [x] Kafka / Kinesis sources (via [push support into the online store](https://docs.feast.dev/reference/data-sources/push))
   * [ ] HTTP source
 * **Offline Stores**
   * [x] [Snowflake](https://docs.feast.dev/reference/offline-stores/snowflake)
diff --git a/examples/quickstart/quickstart.ipynb b/examples/quickstart/quickstart.ipynb
index 3679fcc778..60974d2751 100644
--- a/examples/quickstart/quickstart.ipynb
+++ b/examples/quickstart/quickstart.ipynb
@@ -59,7 +59,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "rXNMAAJKQPG5",
-        "outputId": "b27420ac-c6ba-4d9f-cae8-51a2007b4189"
+        "outputId": "52297709-380b-4200-8e7c-3d0102a82ea4"
       },
       "source": [
         "%%sh\n",
@@ -67,14 +67,14 @@
         "pip install Pygments -q\n",
         "echo \"Please restart your runtime now (Runtime -> Restart runtime). This ensures that the correct dependencies are loaded.\""
       ],
-      "execution_count": null,
+      "execution_count": 1,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Please restart your runtime now (Runtime -> Restart runtime). This ensures that the correct dependencies are loaded.\n"
-          ],
-          "name": "stdout"
+          ]
         }
       ]
     },
@@ -112,22 +112,22 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "IhirSkgUvYau",
-        "outputId": "a2a5631e-1703-4957-b896-9c432851a261"
+        "outputId": "df90af1a-06bd-48a1-94e6-7def19e87d5f"
       },
       "source": [
         "!feast init feature_repo"
       ],
-      "execution_count": null,
+      "execution_count": 1,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Feast is an open source project that collects anonymized error reporting and usage statistics. To opt out or learn more see https://docs.feast.dev/reference/usage\n",
             "\n",
             "Creating a new Feast repository in \u001b[1m\u001b[32m/content/feature_repo\u001b[0m.\n",
             "\n"
-          ],
-          "name": "stdout"
+          ]
         }
       ]
     },
@@ -155,25 +155,25 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "9jXuzt4ovzA3",
-        "outputId": "1ef1bf42-2306-4cc0-c959-1ea2d62e3149"
+        "outputId": "bff15f0c-9f8e-4a3c-e605-5ad84be30709"
       },
       "source": [
         "%cd feature_repo\n",
         "!ls -R"
       ],
-      "execution_count": null,
+      "execution_count": 2,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "/content/feature_repo\n",
             ".:\n",
-            "data  example.py  feature_store.yaml\n",
+            "data  example.py  feature_store.yaml  __init__.py\n",
             "\n",
             "./data:\n",
             "driver_stats.parquet\n"
-          ],
-          "name": "stdout"
+          ]
         }
       ]
     },
@@ -192,8 +192,7 @@
         "*   gcp: use BigQuery/Snowflake with Google Cloud Datastore/Redis\n",
         "*   aws: use Redshift/Snowflake with DynamoDB/Redis\n",
         "\n",
-        "Note that there are many other sources Feast works with, including Azure, Hive, Trino, and PostgreSQL via community plugins. See https://docs.feast.dev/getting-started/third-party-integrations for all supported datasources.",
-        "\n",
+        "Note that there are many other sources Feast works with, including Azure, Hive, Trino, and PostgreSQL via community plugins. See https://docs.feast.dev/getting-started/third-party-integrations for all supported datasources.\n",
         "A custom setup can also be made by following https://docs.feast.dev/v/master/how-to-guides/creating-a-custom-provider"
       ]
     },
@@ -204,23 +203,23 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "9_YJ--uYdtcP",
-        "outputId": "8d772619-aa4d-4cb4-e7e0-2ed45bc09a87"
+        "outputId": "89268e31-6be0-43fb-e576-6d335a2c1dd9"
       },
       "source": [
         "!pygmentize feature_store.yaml"
       ],
-      "execution_count": null,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "project: feature_repo\n",
-            "registry: data/registry.db\n",
-            "provider: local\n",
-            "online_store:\n",
-            "    path: data/online_store.db\n"
-          ],
-          "name": "stdout"
+            "\u001b[94mproject\u001b[39;49;00m: feature_repo\n",
+            "\u001b[94mregistry\u001b[39;49;00m: data/registry.db\n",
+            "\u001b[94mprovider\u001b[39;49;00m: local\n",
+            "\u001b[94monline_store\u001b[39;49;00m:\n",
+            "    \u001b[94mpath\u001b[39;49;00m: data/online_store.db\n"
+          ]
         }
       ]
     },
@@ -240,23 +239,55 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 419
+          "height": 424
         },
         "id": "sIF2lO59dwzi",
-        "outputId": "3e7ff19e-1052-49a6-a889-de76cce61714"
+        "outputId": "80e798d5-df21-4ebd-de1c-9bde282bd742"
       },
       "source": [
         "import pandas as pd\n",
         "\n",
         "pd.read_parquet(\"data/driver_stats.parquet\")"
       ],
-      "execution_count": null,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
+            "text/plain": [
+              "               event_timestamp  driver_id  conv_rate  acc_rate  \\\n",
+              "0    2022-03-31 14:00:00+00:00       1005   0.313336  0.231481   \n",
+              "1    2022-03-31 15:00:00+00:00       1005   0.959499  0.942614   \n",
+              "2    2022-03-31 16:00:00+00:00       1005   0.231786  0.313516   \n",
+              "3    2022-03-31 17:00:00+00:00       1005   0.886911  0.531613   \n",
+              "4    2022-03-31 18:00:00+00:00       1005   0.574945  0.718223   \n",
+              "...                        ...        ...        ...       ...   \n",
+              "1802 2022-04-15 12:00:00+00:00       1001   0.521622  0.266667   \n",
+              "1803 2022-04-15 13:00:00+00:00       1001   0.003188  0.535501   \n",
+              "1804 2021-04-12 07:00:00+00:00       1001   0.709081  0.823138   \n",
+              "1805 2022-04-08 02:00:00+00:00       1003   0.033297  0.053268   \n",
+              "1806 2022-04-08 02:00:00+00:00       1003   0.033297  0.053268   \n",
+              "\n",
+              "      avg_daily_trips                 created  \n",
+              "0                 303 2022-04-15 14:34:10.056  \n",
+              "1                 842 2022-04-15 14:34:10.056  \n",
+              "2                 782 2022-04-15 14:34:10.056  \n",
+              "3                 634 2022-04-15 14:34:10.056  \n",
+              "4                 441 2022-04-15 14:34:10.056  \n",
+              "...               ...                     ...  \n",
+              "1802              406 2022-04-15 14:34:10.056  \n",
+              "1803              593 2022-04-15 14:34:10.056  \n",
+              "1804              997 2022-04-15 14:34:10.056  \n",
+              "1805              534 2022-04-15 14:34:10.056  \n",
+              "1806              534 2022-04-15 14:34:10.056  \n",
+              "\n",
+              "[1807 rows x 6 columns]"
+            ],
             "text/html": [
-              "<div>\n",
+              "\n",
+              "  <div id=\"df-fdad8041-cb31-4675-af7e-2643a4371f51\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -285,48 +316,48 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>2021-08-08 16:00:00+00:00</td>\n",
+              "      <td>2022-03-31 14:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.293061</td>\n",
-              "      <td>0.001904</td>\n",
-              "      <td>40</td>\n",
-              "      <td>2021-08-23 16:25:16.962</td>\n",
+              "      <td>0.313336</td>\n",
+              "      <td>0.231481</td>\n",
+              "      <td>303</td>\n",
+              "      <td>2022-04-15 14:34:10.056</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>2021-08-08 17:00:00+00:00</td>\n",
+              "      <td>2022-03-31 15:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.411542</td>\n",
-              "      <td>0.893139</td>\n",
-              "      <td>722</td>\n",
-              "      <td>2021-08-23 16:25:16.962</td>\n",
+              "      <td>0.959499</td>\n",
+              "      <td>0.942614</td>\n",
+              "      <td>842</td>\n",
+              "      <td>2022-04-15 14:34:10.056</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>2021-08-08 18:00:00+00:00</td>\n",
+              "      <td>2022-03-31 16:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.495635</td>\n",
-              "      <td>0.202365</td>\n",
-              "      <td>280</td>\n",
-              "      <td>2021-08-23 16:25:16.962</td>\n",
+              "      <td>0.231786</td>\n",
+              "      <td>0.313516</td>\n",
+              "      <td>782</td>\n",
+              "      <td>2022-04-15 14:34:10.056</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>2021-08-08 19:00:00+00:00</td>\n",
+              "      <td>2022-03-31 17:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.890092</td>\n",
-              "      <td>0.771689</td>\n",
-              "      <td>88</td>\n",
-              "      <td>2021-08-23 16:25:16.962</td>\n",
+              "      <td>0.886911</td>\n",
+              "      <td>0.531613</td>\n",
+              "      <td>634</td>\n",
+              "      <td>2022-04-15 14:34:10.056</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
-              "      <td>2021-08-08 20:00:00+00:00</td>\n",
+              "      <td>2022-03-31 18:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.308211</td>\n",
-              "      <td>0.126267</td>\n",
-              "      <td>552</td>\n",
-              "      <td>2021-08-23 16:25:16.962</td>\n",
+              "      <td>0.574945</td>\n",
+              "      <td>0.718223</td>\n",
+              "      <td>441</td>\n",
+              "      <td>2022-04-15 14:34:10.056</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>...</th>\n",
@@ -339,73 +370,133 @@
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1802</th>\n",
-              "      <td>2021-08-23 14:00:00+00:00</td>\n",
+              "      <td>2022-04-15 12:00:00+00:00</td>\n",
               "      <td>1001</td>\n",
-              "      <td>0.251525</td>\n",
-              "      <td>0.245729</td>\n",
-              "      <td>98</td>\n",
-              "      <td>2021-08-23 16:25:16.962</td>\n",
+              "      <td>0.521622</td>\n",
+              "      <td>0.266667</td>\n",
+              "      <td>406</td>\n",
+              "      <td>2022-04-15 14:34:10.056</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1803</th>\n",
-              "      <td>2021-08-23 15:00:00+00:00</td>\n",
+              "      <td>2022-04-15 13:00:00+00:00</td>\n",
               "      <td>1001</td>\n",
-              "      <td>0.469145</td>\n",
-              "      <td>0.138416</td>\n",
-              "      <td>606</td>\n",
-              "      <td>2021-08-23 16:25:16.962</td>\n",
+              "      <td>0.003188</td>\n",
+              "      <td>0.535501</td>\n",
+              "      <td>593</td>\n",
+              "      <td>2022-04-15 14:34:10.056</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1804</th>\n",
               "      <td>2021-04-12 07:00:00+00:00</td>\n",
               "      <td>1001</td>\n",
-              "      <td>0.897222</td>\n",
-              "      <td>0.086379</td>\n",
-              "      <td>314</td>\n",
-              "      <td>2021-08-23 16:25:16.962</td>\n",
+              "      <td>0.709081</td>\n",
+              "      <td>0.823138</td>\n",
+              "      <td>997</td>\n",
+              "      <td>2022-04-15 14:34:10.056</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1805</th>\n",
-              "      <td>2021-08-16 04:00:00+00:00</td>\n",
+              "      <td>2022-04-08 02:00:00+00:00</td>\n",
               "      <td>1003</td>\n",
-              "      <td>0.298156</td>\n",
-              "      <td>0.671153</td>\n",
-              "      <td>162</td>\n",
-              "      <td>2021-08-23 16:25:16.962</td>\n",
+              "      <td>0.033297</td>\n",
+              "      <td>0.053268</td>\n",
+              "      <td>534</td>\n",
+              "      <td>2022-04-15 14:34:10.056</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1806</th>\n",
-              "      <td>2021-08-16 04:00:00+00:00</td>\n",
+              "      <td>2022-04-08 02:00:00+00:00</td>\n",
               "      <td>1003</td>\n",
-              "      <td>0.298156</td>\n",
-              "      <td>0.671153</td>\n",
-              "      <td>162</td>\n",
-              "      <td>2021-08-23 16:25:16.962</td>\n",
+              "      <td>0.033297</td>\n",
+              "      <td>0.053268</td>\n",
+              "      <td>534</td>\n",
+              "      <td>2022-04-15 14:34:10.056</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "<p>1807 rows × 6 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "               event_timestamp  ...                 created\n",
-              "0    2021-08-08 16:00:00+00:00  ... 2021-08-23 16:25:16.962\n",
-              "1    2021-08-08 17:00:00+00:00  ... 2021-08-23 16:25:16.962\n",
-              "2    2021-08-08 18:00:00+00:00  ... 2021-08-23 16:25:16.962\n",
-              "3    2021-08-08 19:00:00+00:00  ... 2021-08-23 16:25:16.962\n",
-              "4    2021-08-08 20:00:00+00:00  ... 2021-08-23 16:25:16.962\n",
-              "...                        ...  ...                     ...\n",
-              "1802 2021-08-23 14:00:00+00:00  ... 2021-08-23 16:25:16.962\n",
-              "1803 2021-08-23 15:00:00+00:00  ... 2021-08-23 16:25:16.962\n",
-              "1804 2021-04-12 07:00:00+00:00  ... 2021-08-23 16:25:16.962\n",
-              "1805 2021-08-16 04:00:00+00:00  ... 2021-08-23 16:25:16.962\n",
-              "1806 2021-08-16 04:00:00+00:00  ... 2021-08-23 16:25:16.962\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdad8041-cb31-4675-af7e-2643a4371f51')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
               "\n",
-              "[1807 rows x 6 columns]"
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-fdad8041-cb31-4675-af7e-2643a4371f51 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-fdad8041-cb31-4675-af7e-2643a4371f51');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
             ]
           },
           "metadata": {},
-          "execution_count": 5
+          "execution_count": 4
         }
       ]
     },
@@ -438,53 +529,54 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "DPqXCoNpL0SX",
-        "outputId": "a252e224-61da-48ee-92b8-1780def99244"
+        "outputId": "be1308b2-0c83-4dd3-eb88-e79ffcbd20d6"
       },
       "source": [
         "!pygmentize -f terminal16m example.py"
       ],
-      "execution_count": null,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "\u001b[38;2;64;128;128m# This is an example feature definition file\u001b[39m\n",
+            "\u001b[38;2;64;128;128;03m# This is an example feature definition file\u001b[39;00m\n",
             "\n",
-            "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mgoogle.protobuf.duration_pb2\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m Duration\n",
+            "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mdatetime\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m timedelta\n",
             "\n",
-            "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mfeast\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m Entity, Feature, FeatureView, FileSource, ValueType\n",
+            "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mfeast\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m Entity, FeatureView, Field, FileSource, ValueType\n",
+            "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mfeast\u001b[39;00m\u001b[38;2;0;0;255;01m.\u001b[39;00m\u001b[38;2;0;0;255;01mtypes\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m Float32, Int64\n",
             "\n",
-            "\u001b[38;2;64;128;128m# Read data from parquet files. Parquet is convenient for local development mode. For\u001b[39m\n",
-            "\u001b[38;2;64;128;128m# production, you can use your favorite DWH, such as BigQuery. See Feast documentation\u001b[39m\n",
-            "\u001b[38;2;64;128;128m# for more info.\u001b[39m\n",
+            "\u001b[38;2;64;128;128;03m# Read data from parquet files. Parquet is convenient for local development mode. For\u001b[39;00m\n",
+            "\u001b[38;2;64;128;128;03m# production, you can use your favorite DWH, such as BigQuery. See Feast documentation\u001b[39;00m\n",
+            "\u001b[38;2;64;128;128;03m# for more info.\u001b[39;00m\n",
             "driver_hourly_stats \u001b[38;2;102;102;102m=\u001b[39m FileSource(\n",
             "    path\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33m/content/feature_repo/data/driver_stats.parquet\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m,\n",
-            "    event_timestamp_column\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mevent_timestamp\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m,\n",
+            "    timestamp_field\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mevent_timestamp\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m,\n",
             "    created_timestamp_column\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mcreated\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m,\n",
             ")\n",
             "\n",
-            "\u001b[38;2;64;128;128m# Define an entity for the driver. You can think of entity as a primary key used to\u001b[39m\n",
-            "\u001b[38;2;64;128;128m# fetch features.\u001b[39m\n",
-            "driver \u001b[38;2;102;102;102m=\u001b[39m Entity(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver_id\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, value_type\u001b[38;2;102;102;102m=\u001b[39mValueType\u001b[38;2;102;102;102m.\u001b[39mINT64, description\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver id\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m,)\n",
+            "\u001b[38;2;64;128;128;03m# Define an entity for the driver. You can think of entity as a primary key used to\u001b[39;00m\n",
+            "\u001b[38;2;64;128;128;03m# fetch features.\u001b[39;00m\n",
+            "driver \u001b[38;2;102;102;102m=\u001b[39m Entity(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, value_type\u001b[38;2;102;102;102m=\u001b[39mValueType\u001b[38;2;102;102;102m.\u001b[39mINT64, join_key\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver_id\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m,)\n",
             "\n",
-            "\u001b[38;2;64;128;128m# Our parquet files contain sample data that includes a driver_id column, timestamps and\u001b[39m\n",
-            "\u001b[38;2;64;128;128m# three feature column. Here we define a Feature View that will allow us to serve this\u001b[39m\n",
-            "\u001b[38;2;64;128;128m# data to our model online.\u001b[39m\n",
+            "\u001b[38;2;64;128;128;03m# Our parquet files contain sample data that includes a driver_id column, timestamps and\u001b[39;00m\n",
+            "\u001b[38;2;64;128;128;03m# three feature column. Here we define a Feature View that will allow us to serve this\u001b[39;00m\n",
+            "\u001b[38;2;64;128;128;03m# data to our model online.\u001b[39;00m\n",
             "driver_hourly_stats_view \u001b[38;2;102;102;102m=\u001b[39m FeatureView(\n",
             "    name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver_hourly_stats\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m,\n",
-            "    entities\u001b[38;2;102;102;102m=\u001b[39m[\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver_id\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m],\n",
-            "    ttl\u001b[38;2;102;102;102m=\u001b[39mDuration(seconds\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;102;102;102m86400\u001b[39m \u001b[38;2;102;102;102m*\u001b[39m \u001b[38;2;102;102;102m1\u001b[39m),\n",
-            "    features\u001b[38;2;102;102;102m=\u001b[39m[\n",
-            "        Feature(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mconv_rate\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, dtype\u001b[38;2;102;102;102m=\u001b[39mValueType\u001b[38;2;102;102;102m.\u001b[39mFLOAT),\n",
-            "        Feature(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33macc_rate\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, dtype\u001b[38;2;102;102;102m=\u001b[39mValueType\u001b[38;2;102;102;102m.\u001b[39mFLOAT),\n",
-            "        Feature(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mavg_daily_trips\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, dtype\u001b[38;2;102;102;102m=\u001b[39mValueType\u001b[38;2;102;102;102m.\u001b[39mINT64),\n",
+            "    entities\u001b[38;2;102;102;102m=\u001b[39m[\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m],\n",
+            "    ttl\u001b[38;2;102;102;102m=\u001b[39mtimedelta(days\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;102;102;102m1\u001b[39m),\n",
+            "    schema\u001b[38;2;102;102;102m=\u001b[39m[\n",
+            "        Field(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mconv_rate\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, dtype\u001b[38;2;102;102;102m=\u001b[39mFloat32),\n",
+            "        Field(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33macc_rate\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, dtype\u001b[38;2;102;102;102m=\u001b[39mFloat32),\n",
+            "        Field(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mavg_daily_trips\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, dtype\u001b[38;2;102;102;102m=\u001b[39mInt64),\n",
             "    ],\n",
-            "    online\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;0;128;0mTrue\u001b[39m,\n",
-            "    batch_source\u001b[38;2;102;102;102m=\u001b[39mdriver_hourly_stats,\n",
+            "    online\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;0;128;0;01mTrue\u001b[39;00m,\n",
+            "    source\u001b[38;2;102;102;102m=\u001b[39mdriver_hourly_stats,\n",
             "    tags\u001b[38;2;102;102;102m=\u001b[39m{},\n",
             ")\n"
-          ],
-          "name": "stdout"
+          ]
         }
       ]
     },
@@ -505,21 +597,23 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "RYKCKKrcxYZG",
-        "outputId": "d36b3fb2-9292-4b43-f26a-5441c301c92d"
+        "outputId": "9745d7eb-b4b8-4a43-bf47-189bbf07ae09"
       },
       "source": [
         "!feast apply"
       ],
-      "execution_count": null,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "Registered entity \u001b[1m\u001b[32mdriver_id\u001b[0m\n",
-            "Registered feature view \u001b[1m\u001b[32mdriver_hourly_stats\u001b[0m\n",
-            "Deploying infrastructure for \u001b[1m\u001b[32mdriver_hourly_stats\u001b[0m\n"
-          ],
-          "name": "stdout"
+            "Created entity \u001b[1m\u001b[32mdriver_id\u001b[0m\n",
+            "Created feature view \u001b[1m\u001b[32mdriver_hourly_stats\u001b[0m\n",
+            "\n",
+            "Created sqlite table \u001b[1m\u001b[32mfeature_repo_driver_hourly_stats\u001b[0m\n",
+            "\n"
+          ]
         }
       ]
     },
@@ -544,7 +638,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "C6Fzia7YwBzz",
-        "outputId": "250e9be1-2283-4d74-cf48-297b8ae0d23a"
+        "outputId": "b99aedae-9c47-4b9f-acdd-cd02e2e091b7"
       },
       "source": [
         "from datetime import datetime, timedelta\n",
@@ -583,21 +677,22 @@
         "print(\"----- Example features -----\\n\")\n",
         "print(training_df.head())"
       ],
-      "execution_count": null,
+      "execution_count": 7,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "----- Feature schema -----\n",
             "\n",
             "<class 'pandas.core.frame.DataFrame'>\n",
-            "Int64Index: 3 entries, 0 to 2\n",
+            "Int64Index: 3 entries, 720 to 1081\n",
             "Data columns (total 6 columns):\n",
             " #   Column                              Non-Null Count  Dtype              \n",
             "---  ------                              --------------  -----              \n",
-            " 0   event_timestamp                     3 non-null      datetime64[ns, UTC]\n",
-            " 1   driver_id                           3 non-null      int64              \n",
-            " 2   label_driver_reported_satisfaction  3 non-null      int64              \n",
+            " 0   driver_id                           3 non-null      int64              \n",
+            " 1   label_driver_reported_satisfaction  3 non-null      int64              \n",
+            " 2   event_timestamp                     3 non-null      datetime64[ns, UTC]\n",
             " 3   conv_rate                           3 non-null      float32            \n",
             " 4   acc_rate                            3 non-null      float32            \n",
             " 5   avg_daily_trips                     3 non-null      int32              \n",
@@ -607,14 +702,16 @@
             "\n",
             "----- Example features -----\n",
             "\n",
-            "                   event_timestamp  driver_id  ...  acc_rate  avg_daily_trips\n",
-            "0 2021-08-23 15:12:55.489091+00:00       1003  ...  0.120588              938\n",
-            "1 2021-08-23 15:49:55.489089+00:00       1002  ...  0.504881              635\n",
-            "2 2021-08-23 16:14:55.489075+00:00       1001  ...  0.138416              606\n",
+            "      driver_id  label_driver_reported_satisfaction  \\\n",
+            "720        1002                                   5   \n",
+            "359        1001                                   1   \n",
+            "1081       1003                                   3   \n",
             "\n",
-            "[3 rows x 6 columns]\n"
-          ],
-          "name": "stdout"
+            "                      event_timestamp  conv_rate  acc_rate  avg_daily_trips  \n",
+            "720  2022-04-15 13:58:30.900257+00:00   0.368052  0.417923              346  \n",
+            "359  2022-04-15 14:23:30.900240+00:00   0.003188  0.535501              593  \n",
+            "1081 2022-04-15 13:21:30.900260+00:00   0.214944  0.788695              904  \n"
+          ]
         }
       ]
     },
@@ -645,23 +742,23 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "efbb493a-89a2-41ce-b3b4-d0d05131a8ff"
+        "outputId": "d38a0e0a-2802-4408-ab92-a26baf82752e"
       },
       "source": [
         "from datetime import datetime\n",
         "!feast materialize-incremental {datetime.now().isoformat()}"
       ],
-      "execution_count": null,
+      "execution_count": 8,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "Materializing \u001b[1m\u001b[32m1\u001b[0m feature views to \u001b[1m\u001b[32m2021-08-23 16:25:46+00:00\u001b[0m into the \u001b[1m\u001b[32msqlite\u001b[0m online store.\n",
+            "Materializing \u001b[1m\u001b[32m1\u001b[0m feature views to \u001b[1m\u001b[32m2022-04-15 14:34:37+00:00\u001b[0m into the \u001b[1m\u001b[32msqlite\u001b[0m online store.\n",
             "\n",
-            "\u001b[1m\u001b[32mdriver_hourly_stats\u001b[0m from \u001b[1m\u001b[32m2021-08-22 16:25:47+00:00\u001b[0m to \u001b[1m\u001b[32m2021-08-23 16:25:46+00:00\u001b[0m:\n",
-            "\r  0%|                                                                         | 0/5 [00:00<?, ?it/s]\r100%|████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 592.05it/s]\n"
-          ],
-          "name": "stdout"
+            "\u001b[1m\u001b[32mdriver_hourly_stats\u001b[0m from \u001b[1m\u001b[32m2022-04-14 14:34:38+00:00\u001b[0m to \u001b[1m\u001b[32m2022-04-15 14:34:37+00:00\u001b[0m:\n",
+            "100%|████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 434.17it/s]\n"
+          ]
         }
       ]
     },
@@ -683,7 +780,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "aVIgSYhI4cvR",
-        "outputId": "c91be624-3646-413d-a9df-8d29e8c85bb1"
+        "outputId": "eada224a-bd86-4933-f0ce-5e66870918cc"
       },
       "source": [
         "print(\"--- Data directory ---\")\n",
@@ -698,18 +795,18 @@
         "        \"SELECT * FROM feature_repo_driver_hourly_stats\", con).columns.tolist())\n",
         "con.close()"
       ],
-      "execution_count": null,
+      "execution_count": 9,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "--- Data directory ---\n",
             "driver_stats.parquet  online_store.db  registry.db\n",
             "\n",
             "--- Schema of online store ---\n",
             "['entity_key', 'feature_name', 'value', 'event_ts', 'created_ts']\n"
-          ],
-          "name": "stdout"
+          ]
         }
       ]
     },
@@ -750,7 +847,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "a-PUsUWUxoH9",
-        "outputId": "78b12b00-4718-41c6-a33a-2d56f47bd05c"
+        "outputId": "7acecf34-8e5a-4a87-caa8-c2bff39d1376"
       },
       "source": [
         "from pprint import pprint\n",
@@ -772,17 +869,17 @@
         "\n",
         "pprint(feature_vector)"
       ],
-      "execution_count": null,
+      "execution_count": 10,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "{'acc_rate': [0.5732735991477966, 0.7828438878059387],\n",
-            " 'avg_daily_trips': [33, 984],\n",
-            " 'conv_rate': [0.15498852729797363, 0.6263588070869446],\n",
+            "{'acc_rate': [0.29913440346717834, 0.4562472701072693],\n",
+            " 'avg_daily_trips': [474, 261],\n",
+            " 'conv_rate': [0.20237785577774048, 0.30597227811813354],\n",
             " 'driver_id': [1004, 1005]}\n"
-          ],
-          "name": "stdout"
+          ]
         }
       ]
     },
diff --git a/sdk/python/feast/templates/aws/driver_repo.py b/sdk/python/feast/templates/aws/driver_repo.py
index 19ba44807b..04c3ea3b9c 100644
--- a/sdk/python/feast/templates/aws/driver_repo.py
+++ b/sdk/python/feast/templates/aws/driver_repo.py
@@ -8,7 +8,7 @@
 # construction of feature vectors
 driver = Entity(
     # Name of the entity. Must be unique within a project
-    name="driver_id",
+    name="driver",
     # The join key of an entity describes the storage level field/column on which
     # features can be looked up. The join key is also used to join feature
     # tables/views when building feature vectors
@@ -41,7 +41,7 @@
     # The list of entities specifies the keys required for joining or looking
     # up features from this feature view. The reference provided in this field
     # correspond to the name of a defined entity (or entities)
-    entities=["driver_id"],
+    entities=["driver"],
     # The timedelta is the maximum age that each feature value may have
     # relative to its lookup time. For historical features (used in training),
     # TTL is relative to each timestamp provided in the entity dataframe.
@@ -60,7 +60,7 @@
     # Batch sources are used to find feature values. In the case of this feature
     # view we will query a source table on Redshift for driver statistics
     # features
-    batch_source=driver_stats_source,
+    source=driver_stats_source,
     # Tags are user defined key/value pairs that are attached to each
     # feature view
     tags={"team": "driver_performance"},
diff --git a/sdk/python/feast/templates/gcp/driver_repo.py b/sdk/python/feast/templates/gcp/driver_repo.py
index e494e021f2..7c686cdb6f 100644
--- a/sdk/python/feast/templates/gcp/driver_repo.py
+++ b/sdk/python/feast/templates/gcp/driver_repo.py
@@ -8,7 +8,7 @@
 # construction of feature vectors
 driver = Entity(
     # Name of the entity. Must be unique within a project
-    name="driver_id",
+    name="driver",
     # The join key of an entity describes the storage level field/column on which
     # features can be looked up. The join key is also used to join feature
     # tables/views when building feature vectors
@@ -39,7 +39,7 @@
     # The list of entities specifies the keys required for joining or looking
     # up features from this feature view. The reference provided in this field
     # correspond to the name of a defined entity (or entities)
-    entities=["driver_id"],
+    entities=["driver"],
     # The timedelta is the maximum age that each feature value may have
     # relative to its lookup time. For historical features (used in training),
     # TTL is relative to each timestamp provided in the entity dataframe.
@@ -58,7 +58,7 @@
     # Batch sources are used to find feature values. In the case of this feature
     # view we will query a source table on BigQuery for driver statistics
     # features
-    batch_source=driver_stats_source,
+    source=driver_stats_source,
     # Tags are user defined key/value pairs that are attached to each
     # feature view
     tags={"team": "driver_performance"},
diff --git a/sdk/python/feast/templates/local/example.py b/sdk/python/feast/templates/local/example.py
index 076a331f91..56d24e60a5 100644
--- a/sdk/python/feast/templates/local/example.py
+++ b/sdk/python/feast/templates/local/example.py
@@ -16,14 +16,14 @@
 
 # Define an entity for the driver. You can think of entity as a primary key used to
 # fetch features.
-driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)
+driver = Entity(name="driver", join_key="driver_id", value_type=ValueType.INT64,)
 
 # Our parquet files contain sample data that includes a driver_id column, timestamps and
 # three feature column. Here we define a Feature View that will allow us to serve this
 # data to our model online.
 driver_hourly_stats_view = FeatureView(
     name="driver_hourly_stats",
-    entities=["driver_id"],
+    entities=["driver"],
     ttl=timedelta(days=1),
     schema=[
         Field(name="conv_rate", dtype=Float32),
@@ -31,6 +31,6 @@
         Field(name="avg_daily_trips", dtype=Int64),
     ],
     online=True,
-    batch_source=driver_hourly_stats,
+    source=driver_hourly_stats,
     tags={},
 )
diff --git a/sdk/python/feast/templates/snowflake/driver_repo.py b/sdk/python/feast/templates/snowflake/driver_repo.py
index c14e4c38cc..55a25d445c 100644
--- a/sdk/python/feast/templates/snowflake/driver_repo.py
+++ b/sdk/python/feast/templates/snowflake/driver_repo.py
@@ -10,7 +10,7 @@
 # construction of feature vectors
 driver = Entity(
     # Name of the entity. Must be unique within a project
-    name="driver_id",
+    name="driver",
     # The join key of an entity describes the storage level field/column on which
     # features can be looked up. The join key is also used to join feature
     # tables/views when building feature vectors
@@ -43,7 +43,7 @@
     # The list of entities specifies the keys required for joining or looking
     # up features from this feature view. The reference provided in this field
     # correspond to the name of a defined entity (or entities)
-    entities=["driver_id"],
+    entities=["driver"],
     # The timedelta is the maximum age that each feature value may have
     # relative to its lookup time. For historical features (used in training),
     # TTL is relative to each timestamp provided in the entity dataframe.
diff --git a/sdk/python/feast/templates/spark/example.py b/sdk/python/feast/templates/spark/example.py
index c8c1c1257e..58f3df740f 100644
--- a/sdk/python/feast/templates/spark/example.py
+++ b/sdk/python/feast/templates/spark/example.py
@@ -16,9 +16,9 @@
 
 
 # Entity definitions
-driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)
+driver = Entity(name="driver", value_type=ValueType.INT64, description="driver id",)
 customer = Entity(
-    name="customer_id", value_type=ValueType.INT64, description="customer id",
+    name="customer", value_type=ValueType.INT64, description="customer id",
 )
 
 # Sources
@@ -40,7 +40,7 @@
 # Feature Views
 driver_hourly_stats_view = FeatureView(
     name="driver_hourly_stats",
-    entities=["driver_id"],
+    entities=["driver"],
     ttl=timedelta(days=7),
     schema=[
         Field(name="conv_rate", dtype=Float32),
@@ -48,12 +48,12 @@
         Field(name="avg_daily_trips", dtype=Int64),
     ],
     online=True,
-    batch_source=driver_hourly_stats,
+    source=driver_hourly_stats,
     tags={},
 )
 customer_daily_profile_view = FeatureView(
     name="customer_daily_profile",
-    entities=["customer_id"],
+    entities=["customer"],
     ttl=timedelta(days=7),
     schema=[
         Field(name="current_balance", dtype=Float32),
@@ -61,6 +61,6 @@
         Field(name="lifetime_trip_count", dtype=Int64),
     ],
     online=True,
-    batch_source=customer_daily_profile,
+    source=customer_daily_profile,
     tags={},
 )
diff --git a/sdk/python/tests/utils/online_write_benchmark.py b/sdk/python/tests/utils/online_write_benchmark.py
index 6d6b73d5da..82ffc8e98b 100644
--- a/sdk/python/tests/utils/online_write_benchmark.py
+++ b/sdk/python/tests/utils/online_write_benchmark.py
@@ -29,7 +29,7 @@ def create_driver_hourly_stats_feature_view(source):
             Field(name="acc_rate", dtype=Float32),
             Field(name="avg_daily_trips", dtype=Int32),
         ],
-        batch_source=source,
+        source=source,
         ttl=timedelta(hours=2),
     )
     return driver_stats_feature_view