Production-Grade AIOps: Automating Databricks with GitHub Actions
Duration: 4 Hours
Format: Hands-on, Lab-driven Workshop
Level: Intermediate
Course Overview
Transitioning data pipelines from development to production requires robust automation, testing, and governance. This intensive, four-hour crash course bridges the gap between Data Engineering and DevOps. You will learn how to treat your Databricks notebooks, libraries, and workflows as code, automating their lifecycle using GitHub Actions.
By the end of this course, you will have built a fully functional CI/CD pipeline that automatically tests data code upon a Pull Request and deploys it across multiple Databricks environments using modern tooling like Databricks Asset Bundles (DABs).
Target Audience
Data Engineers looking to automate their deployment workflows.
DevOps/Platform Engineers tasked with supporting data engineering teams on Databricks.
Data Architects aiming to implement governance and CI/CD best practices in the cloud.
Prerequisites
Basic familiarity with the Databricks workspace (Notebooks, Workflows).
Foundational knowledge of Git concepts (commits, branches, pull requests).
Basic understanding of Python or SQL.
Learning Objectives
Connect Databricks workspaces securely with GitHub.
Implement Continuous Integration (CI) to lint, format, and unit-test your data pipelines.
Configure Databricks Asset Bundles (DABs) to define your infrastructure as code.
Build automated Continuous Deployment (CD) workflows using GitHub Actions to push code safely to Staging and Production workspaces.
Manage secure authentication using GitHub Secrets and Databricks Service Principals.
Course Schedule and Syllabus
Hour 1: Foundational DevOps and Workspace Integration
Concepts Covered: * The DataOps philosophy: Why DevOps for data is different.
Setting up the architecture: Dev, Staging, and Prod workspaces.
Authentication best practices: Service Principals vs. Personal Access Tokens (PATs).
Hands-on Lab: * Connecting Databricks Repos to GitHub.
Configuring GitHub Secrets to securely communicate with the Databricks CLI.
Hour 2: Continuous Integration (CI) for Data Pipelines
Concepts Covered: * Automating code quality checks on Pull Requests.
Static code analysis: Linting Python notebooks and SQL queries.
Unit testing Databricks code using pytest.
Hands-on Lab: * Writing a GitHub Actions workflow triggered by a pull_request event.
Running a local Python environment inside a GitHub runner to test notebook logic before it ever hits Databricks.
Hour 3: Continuous Deployment (CD) with Databricks Asset Bundles (DABs)
Concepts Covered:
Introduction to Databricks Asset Bundles (DABs)—the modern standard for deploying Databricks projects.
Defining Databricks resources (Notebooks, Delta Live Tables, and Jobs) in a single databricks.yml configuration file.
Hands-on Lab:
Structuring a DABs project locally.
Writing a CD pipeline in GitHub Actions that automatically deploys assets to a target workspace upon merging to the main branch.
Hour 4: Automated Testing, Workflows, and Monitoring
Concepts Covered:
Integration testing: Running automated Databricks jobs via CI/CD.
Strategies for promotion: Moving code from Staging to Production safely.
Rollback strategies when deployments fail.
Hands-on Lab:
Configuring a GitHub Actions pipeline to deploy a workflow, trigger a test run of a Databricks Job, and verify the data output.
Final wrap-up and Q&A.
Tech Stack and Tools Used
Databricks Runtime & Workspaces
Databricks Asset Bundles (DABs) & Databricks CLI v2
GitHub & GitHub Actions
Python, PyTest, and Black/Flake8 (for linting)
What Students Need to Bring: Access to a GitHub account and a Databricks workspace (Community Edition is not recommended for this course due to API limitations; a standard/premium cloud trial tier is ideal).