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databricks-template

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Production-ready PySpark ETL template for Databricks — medallion architecture, DABs, tests, DQX, CI/CD, and agentic development with Claude Code.

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# Databricks PySpark ETL Template — production-ready pipelines, built agentically with Claude Code

![Databricks](https://img.shields.io/badge/platform-Databricks-orange?logo=databricks)
![PySpark](https://img.shields.io/badge/pyspark-4.1+-brightgreen?logo=apache-spark)
![CI/CD](https://img.shields.io/github/actions/workflow/status/andre-salvati/databricks-template/.github/workflows/onpush.yml)
![Claude Code](https://img.shields.io/badge/agentic-Claude%20Code-8A2BE2)
![License](https://img.shields.io/badge/license-Apache%202.0-blue)
![Stars](https://img.shields.io/github/stars/andre-salvati/databricks-template?style=social)

## 🚀 Overview

> Stop spending weeks on boilerplate. This PySpark project template for Databricks gives you medallion architecture, Python packaging, unit + integration + load tests, CI/CD, DQX data quality, service-principal-based production deploys, and much more — all wired together and ready to ship. Whether you're starting a new Databricks ETL project or looking for a reference implementation of production-ready PySpark pipelines, fork this and go.

If this saves you time, a star helps others find it. Let's [connect on LinkedIn](https://www.linkedin.com/in/andresalvati/).

## 🧪 Technologies

- Databricks Free Edition (Serverless)
- Databricks Runtime 18.0 LTS
- Databricks Unity Catalog
- Databricks Declarative Automation Bundles (former Asset Bundles)
- Databricks CLI
- Databricks Python SDK
- Databricks DQX
- Databricks AI Dev Kit
- Databricks Dashboards
- Claude Code
- PySpark 4.1
- Spark Declarative Pipelines (SDP)
- Python 3.12
- GitHub Actions
- Pytest

## 📦 Features

This project template demonstrates how to:

- use agentic development (with [Databricks AI Dev Kit](https://github.com/databricks-solutions/ai-dev-kit) and [Claude Code](https://claude.com/product/claude-code)) in data projects. The template ships with a [`CLAUDE.md`](CLAUDE.md) and a [`specs/`](specs/) folder documenting the project's conventions.
- structure PySpark code inside classes/packages, deploy it as a Python wheel (instead of notebooks), and manage the project with [uv](https://docs.astral.sh/uv/).
- package and deploy code with [Declarative Automation Bundles](https://docs.databricks.com/en/dev-tools/bundles/index.html) to different environments (dev, staging, prod). Use [GitHub Actions](https://docs.github.com/en/actions) to automate CI/CD pipeline. 
- utilize [Databricks Lakeflow Jobs](https://docs.databricks.com/en/workflows/index.html) to execute a DAG - Yes, you don't need Airflow to manage your DAGs here!!!. Generate job definitions to run with environment-specific conditions using [Databricks SDK](https://docs.databricks.com/aws/en/dev-tools/sdk-python#create-a-job-that-uses-serverless-compute).
- isolate "dev" environments / catalogs to avoid concurrency issues between developer tests.
- separate deploy-time config (environment variables, CI secrets) from runtime config (job parameters overridable from the Databricks UI), keeping jobs flexible without coupling them to the build process.
- utilize job tags to track issues, costs, and ownership.
- use the [medallion architecture](https://www.databricks.com/glossary/medallion-architecture) to organize your data.
- use a [Lakeflow Spark Declarative Pipeline](https://docs.databricks.com/aws/en/ldp/) to run the same ETL logic side-by-side with the PySpark job, demonstrating both paradigms from one codebase.
- apply [Delta liquid clustering](https://docs.databricks.com/aws/en/delta/clustering) and incremental load to build more efficient pipelines.
- run unit tests on transformations with the [pytest package](https://pypi.org/project/pytest/). Set up VS Code to run tests on your local machine.
- run integration tests by setting the input data and validating the output data.
- run load tests to exercise both the initial bulk load and incremental daily updates, validating that the pipeline handles production-scale data.
- use [Databricks AI/BI Dashboards](https://docs.databricks.com/aws/en/dashboards) to visualize the gold layer.
- utilize the [coverage package](https://pypi.org/project/coverage/) to generate test coverage reports.
- use structured logging giving you full observability during incidents without a code change.
- lint and format code with [ruff](https://docs.astral.sh/ruff/) and [pre-commit](https://pre-commit.com/).
- use a Makefile to automate repetitive tasks.
- utilize [Databricks DQX](https://databrickslabs.github.io/dqx/) to enforce data quality rules, such as null checks, uniqueness, thresholds, and schema validation, and filter bad data into quarantine tables.
- utilize [service principals](https://docs.databricks.com/aws/en/admin/users-groups/service-principals) to run production code.
- utilize the [Databricks SDK for Python](https://docs.databricks.com/en/dev-tools/sdk-python.html) to manage catalogs, schemas, workspaces, and accounts. Refer to the `scripts` folder for examples.
- utilize [Databricks Unity Catalog](https://www.databricks.com/product/unity-catalog) to manage permissions and get data lineage.
- enforce production guardrails out of the box — identity-locked CI deploys, a health-check task, wheel version pinning, per-task timeouts, schema-drift guards, queued runs, and on-call alerting.
- use a Claude 'project-costs' skill to track project cloud spend in USD across AWS (Cost Explorer) and Databricks — see an [example](.claude/skills/project-costs/examples/2026-07-22.md).
- use a Claude 'sql-diagram' skill to diagram any SQL query with [sqlglot](https://github.com/tobymao/sqlglot) — see an [example](.claude/skills/sql-diagram/examples/job_spend_plan.svg).
- use a Claude 'data-divergence' skill to investigate why two datasets that should agree don't — see an [example](.claude/skills/data-divergence/examples/2026-08-06-prod-batch-vs-sdp.md).
- utilize serverless job clusters on [Databricks Free Edition](https://docs.databricks.com/aws/en/getting-started/free-edition) to deploy your pipelines.

## 📐 Specs

Deep technical detail lives in [`specs/`](specs/) (the README stays a landing page):

- [**Architecture**](specs/architecture.md) — wheel/CLI surface, jobs DAG, job generation, CI/CD, job-level params, deploy-time env vars, logging, production guardrails, folder structure.
- [**Data model**](specs/data-model.md) — catalog/schema isolation, medallion data flow (diagram), table schemas, product-name freeze semantics, the dashboard, liquid clustering, DQX/quarantine, lineage.
- [**Workflow**](specs/workflow.md) — the development lifecycle (plan → branch → PR), PR description standard, production-table impact check, the prod rollback playbook, and the unit / integration / load test plan.
- [**Tooling**](specs/tooling.md) — MCP servers (Databricks, AWS billing/docs, context7), the Databricks CLI, and the bundled skills.

## 🧠 Resources

Agentic development:
- [Claude Code: 5 Essentials for Data Engineering](https://www.youtube.com/watch?v=YnIWW88l0mc)
- [Mastering Claude Code in 30 minutes](https://www.youtube.com/watch?v=6eBSHbLKuN0)
- [Introducing Databricks AI Dev Kit - Skills, MCP server, Builder App](https://www.youtube.com/watch?v=HFSIKrG8bRg)

Debates on the use of notebooks vs. Python packaging:
- [The Rise of The Notebook Engineer](https://dataengineeringcentral.substack.com/p/the-rise-of-the-notebook-engineer)
- [Please don’t make me use Databricks notebooks](https://medium.com/@seade03/please-dont-make-me-use-databricks-notebooks-3d07a4a332ae)
- [this Linkedin thread by Daniel Beach](https://www.linkedin.com/posts/daniel-beach-6ab8b4132_dataengineering-databricks-activity-7171661784997715968-OpRW)
- [this Linkedin thread by Ryan Chynoweth](https://www.linkedin.com/posts/ryan-chynoweth_using-databricks-notebooks-for-production-activity-7170868557621186561-eo3P)
- [this Linkedin thread by Jaco van Gelder](https://www.linkedin.com/posts/jwvangelder_my-honest-opinion-on-notebooks-vs-python-activity-7385955500007534592-xwHa/)

Sessions on Databricks Declarative Automation Bundles, CI/CD, and Software Development Life Cycle at Data + AI Summit 2025:
- [CI/CD for Databricks: Advanced Asset Bundles and GitHub Actions](https://www.youtube.com/watch?v=XumUXF1e6RI)
- [Deploying Databricks Asset Bundles (DABs) at Scale](https://www.youtube.com/watch?v=mMwprgB-sIU)
- [A Prescription for Success: Leveraging DABs for Faster Deployment and Better Patient Outcomes](https://www.youtube.com/watch?v=01JHTM2UP-U)

Other resources:
- [Goodbye Pip and Poetry. Why UV Might Be All You Need](https://codecut.ai/why-uv-might-all-you-need/)
- [The Spark Revolution You Didn’t See Coming: How Apache Spark 4.0 in Databricks Just Changed Everything](https://medium.com/@matiasmaquieira96/the-spark-revolution-you-didnt-see-coming-how-apache-spark-4-0-2a6422144f67)

## Instructions

1) (Optional) Install [Databricks AI Dev Kit](https://github.com/databricks-solutions/ai-dev-kit) and [Claude Code](https://code.claude.com/docs/en/vs-code).

2) Create a [Databricks Free Edition](https://docs.databricks.com/aws/en/getting-started/free-edition) workspace.

3) Install and configure the Databricks CLI on your local machine. Check the current version in `databricks.yml`. Follow the instructions [here](https://docs.databricks.com/en/dev-tools/cli/install.html).

4) Set up the Python environment and run unit tests on your local machine.

make sync && make unit-test
        
5) Initialize the workspace. Create an external location in Databricks and update the `storage-root` parameter in the Makefile. This step will create the catalogs, schemas, service principal, and the required grants. For more details, see [Overview of external locations](https://docs.databricks.com/aws/en/connect/unity-catalog/cloud-storage#external-locations). Then 
run:

make init

6) Generate a secret for the service principal. In Databricks, go to: Workspace -> Settings -> Identity and access -> Service principals -> Secrets. Generate a new secret for your service principal and update the corresponding profiles in your .databrickscfg file. Your configuration should look similar to this:

[dev]
        host          = https://xxxx.cloud.databricks.com/
        token         = bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb
                        
        [staging]
        host          = https://xxxx.cloud.databricks.com/
        client_id     = yyyyyyyy-yyyy-yyyy-yyyy-yyyyyyyyyyyy
        client_secret = aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa

[prod]
        host          = https://xxxx.cloud.databricks.com/
        client_id     = yyyyyyyy-yyyy-yyyy-yyyy-yyyyyyyyyyyy
        client_secret = aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa

7) Deploy and execute on the dev workspace.

make deploy env=dev

8) Configure CI/CD automation with the service principal ID and secret. Configure [GitHub Actions repository secrets](https://docs.github.com/en/actions/security-guides/using-secrets-in-github-actions): `DATABRICKS_HOST`, `DATABRICKS_CLIENT_ID`, `DATABRICKS_CLIENT_SECRET`, and `TEMPLATE_ALERT_EMAILS` (comma-separated on-call addresses).

9) (Optional) You can also execute unit tests from your preferred IDE. Here's a screenshot from [VS Code](https://code.visualstudio.com/) with [Microsoft's Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) installed.

- <img src="assets/vscode.png">

## License

Licensed under the [Apache License 2.0](LICENSE). See [`LICENSE`](LICENSE) and [`NOTICE`](NOTICE).

## Star History

Generated from the GitHub API by `make star-history` — see [`scripts/star_history.py`](scripts/star_history.py).

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