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Pieter Noordhuis 50f62692ce
Include a materialized copy of built-in templates (#2146) 
## Changes

Include a materialized copy of built-in templates as reference output.

This updates the output comparison logic to work against an output
directory. The `doComparison` function now always works on real files.
It can now tell apart non-existing files and empty files (e.g., the
`.gitkeep` files in templates).
 		2025-01-17 15:03:59 +00:00
..
.vscode Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00
fixtures Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00
resources Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00
scratch Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00
src Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00
tests Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00
.gitignore Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00
README.md Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00
databricks.yml Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00
pytest.ini Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00
requirements-dev.txt Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00
setup.py Include a materialized copy of built-in templates (#2146) 2025-01-17 15:03:59 +00:00

README.md

my_default_python

The 'my_default_python' project was generated by using the default-python template.

Getting started

  1. Install the Databricks CLI from https://docs.databricks.com/dev-tools/cli/databricks-cli.html

  2. Authenticate to your Databricks workspace, if you have not done so already:

    $ databricks configure

  3. To deploy a development copy of this project, type:

    $ databricks bundle deploy --target dev

    (Note that "dev" is the default target, so the --target parameter is optional here.)

    This deploys everything that's defined for this project. For example, the default template would deploy a job called [dev yourname] my_default_python_job to your workspace. You can find that job by opening your workpace and clicking on Workflows.

  4. Similarly, to deploy a production copy, type:

    $ databricks bundle deploy --target prod

    Note that the default job from the template has a schedule that runs every day (defined in resources/my_default_python.job.yml). The schedule is paused when deploying in development mode (see https://docs.databricks.com/dev-tools/bundles/deployment-modes.html).

  5. To run a job or pipeline, use the "run" command:

    $ databricks bundle run

  6. Optionally, install developer tools such as the Databricks extension for Visual Studio Code from https://docs.databricks.com/dev-tools/vscode-ext.html. Or read the "getting started" documentation for Databricks Connect for instructions on running the included Python code from a different IDE.

  7. For documentation on the Databricks asset bundles format used for this project, and for CI/CD configuration, see https://docs.databricks.com/dev-tools/bundles/index.html.