Add Dev Containers and JupyterLab docs

README.md

@@ -1,7 +1,5 @@
 # Data Cloud Custom Code SDK
 
-<img src="https://img.shields.io/badge/version-0.1.0-blue" alt="license">
-
 This package provides a development kit for creating custom data transformations in [Data Cloud](https://www.salesforce.com/data/). It allows you to write your own data processing logic in Python while leveraging Data Cloud's infrastructure for data access and running data transformations, mapping execution into Data Cloud data structures like [Data Model Objects](https://help.salesforce.com/s/articleView?id=data.c360_a_data_model_objects.htm&type=5) and [Data Lake Objects](https://help.salesforce.com/s/articleView?id=sf.c360_a_data_lake_objects.htm&language=en_US&type=5).
 
 More specifically, this codebase gives you ability to test code locally before pushing to Data Cloud's remote execution engine, greatly reducing how long it takes to develop.
@@ -84,7 +82,7 @@ Once the Data Transform run is successful, check the DLO your script is writing
 
 ## API
 
-You entry point script will define logic using the `Client` object which wraps data access layers.
+Your entry point script will define logic using the `Client` object which wraps data access layers.
 
 You should only need the following methods:
 * `read_dlo(name)` – Read from a Data Lake Object by name
@@ -174,6 +172,48 @@ Zip a transformation job in preparation to upload to Data Cloud.
 Options:
 - `--path TEXT`: Path to the code directory (default: ".")
 
+## Docker usage
+
+After initializing a project with `datacustomcode init my_package`, you might notice a Dockerfile.  This file isn't used for the
+[Quick Start](#quick-start) approach above, which uses virtual environments, until the `zip` or `deploy` commands are used.  When using dependencies
+that include [native features](https://spark.apache.org/docs/latest/api/python/user_guide/python_packaging.html#using-pyspark-native-features)
+like C++ or C interop, the platform and architecture may be different between your machine and Data Cloud compute.  This is all taken care of
+in the `zip` and `deploy` commands, which utilize the Dockerfile which starts `FROM` an image compatible with Data Cloud.  However, you may
+want to build, run, and test your script on your machine using the same platform and architecture as Data Cloud.  You can use the sections below
+to test your script in this manner.
+
+### VS Code Dev Containers
+
+Within your `init`ed package, you will find a `.devcontainer` folder which allows you to run a docker container while developing inside of it.
+
+Read more about Dev Containers here: https://code.visualstudio.com/docs/devcontainers/containers.
+
+1. Install the VS Code extension "Dev Containers" by microsoft.com.
+1. Open your package folder in VS Code, ensuring that the `.devcontainer` folder is at the root of the File Explorer
+1. Bring up the Command Palette (on mac: Cmd + Shift + P), and select "Dev Containers: Rebuild and Reopen in Container"
+1. Allow the docker image to be built, then you're ready to develop
+1. Now if you open a terminal (within the Dev Container window) and `datacustomcode run ./payload/entrypoint.py`, it will run inside a docker container that more closely resembles Data Cloud compute than your machine
+
+> [!IMPORTANT]
+> Dev Containers get their own tmp file storage, so you'll need to re-run `datacustomcode configure` every time you "Rebuild and Reopen in Container".
+
+### JupyterLab
+
+Within your `init`ed package, you will find a `jupyterlab.sh` file that can open a jupyter notebook for you.  Jupyter notebooks, in
+combination with Data Cloud's [Query Editor](https://help.salesforce.com/s/articleView?id=data.c360_a_add_queries_to_a_query_workspace.htm&type=5)
+and [Data Explorer](https://help.salesforce.com/s/articleView?id=data.c360_a_data_explorer.htm&type=5), can be extremely helpful for data
+exploration.  Instead of running an entire script, one can run one code cell at a time as they discover and experiment with the DLO or DMO data.
+
+You can read more about Jupyter Notebooks here: https://jupyter.org/
+
+1. Within the root project of your package folder, run `./jupyterlab.sh start`
+1. Double-click on "account.ipynb" file, which provides a starting point for a notebook
+1. Use shift+enter to execute each cell within the notebook.  Add/edit/delete cells of code as needed for your data exploration.
+1. Don't forget to run `./jupyterlab.sh stop` to stop the docker container
+
+> [!IMPORTANT]
+> JupyterLab uses its own tmp file storage, so you'll need to re-run `datacustomcode configure` each time you `./jupyterlab.sh start`.
+
 ## Prerequisite details
 
 ### Creating a connected app