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Add default-minimal template (#3885)
## Changes Adds a new `default-minimal` template for advanced users who want a clean slate without sample code: * Added `default-minimal` which inherits from the `default` template, just like `default-python` * Enhanced template renderer to preserve empty directories (necessary to avoid `.gitkeep` files in `src/` and `resources/`) * Fixed `uv run pytest` error shown for projects that don't have a `src/<package>` directory ## Why Advanced customers and non-ETL customers (like @fjakobs) indicated they want an "empty" or "minimal" project template where they don't need to delete sample code. Rather than answering "no" to all questions in `default-python`, this gives them something more intentional. ## Tests * Template acceptance tests * Manual uv run pytest, databricks bundle deploy, databricks bundle run on generated project **Example:** ```bash $ databricks bundle init default-minimal Welcome to the minimal Databricks Asset Bundle template! This template creates a minimal project structure without sample code, ideal for advanced users. (For getting started with Python or SQL code, use the default-python or default-sql templates instead.) Your workspace at https://e2-dogfood.staging.cloud.databricks.com is used for initialization. (See https://docs.databricks.com/dev-tools/cli/profiles.html for how to change your profile.) Unique name for this project [my_project]: empty Default catalog for any tables created by this project [main]: Use a personal schema for each user working on this project. (This is recommended. Your personal schema will be 'main.lennart_kats'.): yes ✨ Your new project has been created in the 'empty' directory! To get started, refer to the project README.md file and the documentation at https://docs.databricks.com/dev-tools/bundles/index.html. ``` ``` $ find empty empty empty/resources empty/pyproject.toml empty/tests empty/tests/conftest.py empty/README.md empty/.gitignore empty/fixtures empty/fixtures/.gitkeep empty/.vscode empty/.vscode/__builtins__.pyi empty/.vscode/settings.json empty/.vscode/extensions.json empty/databricks.yml empty/src ``` --------- Co-authored-by: Claude <[email protected]>
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‎NEXT_CHANGELOG.md‎

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### Dependency updates
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### Bundles
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* Add `default-minimal` template for users who want a clean slate without sample code ([#3885](https://github.com/databricks/cli/pull/3885))
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* Add validation that served_models and served_entities are not used at the same time. Add client side translation logic. ([#3880](https://github.com/databricks/cli/pull/3880))
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### API Changes

‎acceptance/bundle/help/bundle-init/output.txt‎

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TEMPLATE_PATH optionally specifies which template to use. It can be one of the following:
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- default-python: The default Python template for Notebooks and Lakeflow
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- default-sql: The default SQL template for .sql files that run with Databricks SQL
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- default-minimal: The minimal template, for advanced users
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- dbt-sql: The dbt SQL template (databricks.com/blog/delivering-cost-effective-data-real-time-dbt-and-databricks)
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- mlops-stacks: The Databricks MLOps Stacks template (github.com/databricks/mlops-stacks)
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- pydabs: A variant of the 'default-python' template that defines resources in Python instead of YAML

‎acceptance/bundle/templates/default-minimal/input.json‎

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{
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"project_name": "my_default_minimal",
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"include_job": "no",
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"include_pipeline": "no",
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"include_python": "no"
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}

‎acceptance/bundle/templates/default-minimal/out.test.toml‎

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‎acceptance/bundle/templates/default-minimal/output.txt‎

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>>> [CLI] bundle init default-minimal --config-file ./input.json --output-dir output
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Welcome to the minimal Databricks Asset Bundle template!
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This template creates a minimal project structure without sample code, ideal for advanced users.
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(For getting started with Python or SQL code, use the default-python or default-sql templates instead.)
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Your workspace at [DATABRICKS_URL] is used for initialization.
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(See https://docs.databricks.com/dev-tools/cli/profiles.html for how to change your profile.)
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✨ Your new project has been created in the 'my_default_minimal' directory!
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To get started, refer to the project README.md file and the documentation at https://docs.databricks.com/dev-tools/bundles/index.html.
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>>> [CLI] bundle validate -t dev
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Name: my_default_minimal
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Target: dev
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Workspace:
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Host: [DATABRICKS_URL]
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User: [USERNAME]
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Path: /Workspace/Users/[USERNAME]/.bundle/my_default_minimal/dev
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Validation OK!
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>>> [CLI] bundle validate -t prod
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Name: my_default_minimal
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Target: prod
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Workspace:
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Host: [DATABRICKS_URL]
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User: [USERNAME]
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Path: /Workspace/Users/[USERNAME]/.bundle/my_default_minimal/prod
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Validation OK!

‎acceptance/bundle/templates/default-minimal/output/my_default_minimal/.vscode/__builtins__.pyi‎

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# Typings for Pylance in Visual Studio Code
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# see https://github.com/microsoft/pyright/blob/main/docs/builtins.md
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from databricks.sdk.runtime import *

‎acceptance/bundle/templates/default-minimal/output/my_default_minimal/.vscode/extensions.json‎

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{
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"recommendations": [
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"databricks.databricks",
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"redhat.vscode-yaml",
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"ms-python.black-formatter"
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]
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}

‎acceptance/bundle/templates/default-minimal/output/my_default_minimal/.vscode/settings.json‎

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{
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"jupyter.interactiveWindow.cellMarker.codeRegex": "^# COMMAND ----------|^# Databricks notebook source|^(#\\s*%%|#\\s*\\<codecell\\>|#\\s*In\\[\\d*?\\]|#\\s*In\\[ \\])",
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"jupyter.interactiveWindow.cellMarker.default": "# COMMAND ----------",
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"python.testing.pytestArgs": [
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"."
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],
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"files.exclude": {
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"**/*.egg-info": true,
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"**/__pycache__": true,
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".pytest_cache": true,
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"dist": true,
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},
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"files.associations": {
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"**/.gitkeep": "markdown"
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},
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// Pylance settings (VS Code)
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// Set typeCheckingMode to "basic" to enable type checking!
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"python.analysis.typeCheckingMode": "off",
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"python.analysis.extraPaths": ["src", "lib", "resources"],
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"python.analysis.diagnosticMode": "workspace",
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"python.analysis.stubPath": ".vscode",
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// Pyright settings (Cursor)
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// Set typeCheckingMode to "basic" to enable type checking!
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"cursorpyright.analysis.typeCheckingMode": "off",
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"cursorpyright.analysis.extraPaths": ["src", "lib", "resources"],
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"cursorpyright.analysis.diagnosticMode": "workspace",
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"cursorpyright.analysis.stubPath": ".vscode",
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// General Python settings
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"python.defaultInterpreterPath": "./.venv/bin/python",
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"python.testing.unittestEnabled": false,
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"python.testing.pytestEnabled": true,
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"[python]": {
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"editor.defaultFormatter": "ms-python.black-formatter",
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"editor.formatOnSave": true,
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},
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}

‎acceptance/bundle/templates/default-minimal/output/my_default_minimal/README.md‎

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# my_default_minimal
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The 'my_default_minimal' project was generated by using the default template.
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* `src/`: Python source code for this project.
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* `resources/`: Resource configurations (jobs, pipelines, etc.)
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* `tests/`: Unit tests for the shared Python code.
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* `fixtures/`: Fixtures for data sets (primarily used for testing).
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## Getting started
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Choose how you want to work on this project:
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(a) Directly in your Databricks workspace, see
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https://docs.databricks.com/dev-tools/bundles/workspace.
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(b) Locally with an IDE like Cursor or VS Code, see
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https://docs.databricks.com/dev-tools/vscode-ext.html.
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(c) With command line tools, see https://docs.databricks.com/dev-tools/cli/databricks-cli.html
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If you're developing with an IDE, dependencies for this project should be installed using uv:
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* Make sure you have the UV package manager installed.
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It's an alternative to tools like pip: https://docs.astral.sh/uv/getting-started/installation/.
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* Run `uv sync --dev` to install the project's dependencies.
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# Using this project using the CLI
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The Databricks workspace and IDE extensions provide a graphical interface for working
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with this project. It's also possible to interact with it directly using the CLI:
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1. Authenticate to your Databricks workspace, if you have not done so already:
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```
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$ databricks configure
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```
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2. To deploy a development copy of this project, type:
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```
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$ databricks bundle deploy --target dev
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```
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(Note that "dev" is the default target, so the `--target` parameter
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is optional here.)
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This deploys everything that's defined for this project.
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3. Similarly, to deploy a production copy, type:
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```
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$ databricks bundle deploy --target prod
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```
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4. To run a job or pipeline, use the "run" command:
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```
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$ databricks bundle run
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```
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5. Finally, to run tests locally, use `pytest`:
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```
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$ uv run pytest
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```

‎acceptance/bundle/templates/default-minimal/output/my_default_minimal/databricks.yml‎

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# This is a Databricks asset bundle definition for my_default_minimal.
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# See https://docs.databricks.com/dev-tools/bundles/index.html for documentation.
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bundle:
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name: my_default_minimal
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uuid: [UUID]
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include:
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- resources/*.yml
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- resources/*/*.yml
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# Variable declarations. These variables are assigned in the dev/prod targets below.
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variables:
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catalog:
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description: The catalog to use
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schema:
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description: The schema to use
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targets:
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dev:
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# The default target uses 'mode: development' to create a development copy.
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# - Deployed resources get prefixed with '[dev my_user_name]'
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# - Any job schedules and triggers are paused by default.
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# See also https://docs.databricks.com/dev-tools/bundles/deployment-modes.html.
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mode: development
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default: true
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workspace:
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host: [DATABRICKS_URL]
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variables:
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catalog: hive_metastore
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schema: ${workspace.current_user.short_name}
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prod:
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mode: production
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workspace:
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host: [DATABRICKS_URL]
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# We explicitly deploy to /Workspace/Users/[USERNAME] to make sure we only have a single copy.
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root_path: /Workspace/Users/[USERNAME]/.bundle/${bundle.name}/${bundle.target}
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variables:
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catalog: hive_metastore
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schema: prod
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permissions:
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- user_name: [USERNAME]
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level: CAN_MANAGE

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