Databricks DevOps & CI/CD for Data Pipelines

Module 1: DevOps Principles for Data Teams

• DataOps vs DevOps: applying software engineering discipline to data pipelines
• Version control for notebooks: Git integration and Databricks Repos setup
• Environment strategy: dev, staging, and prod workspace architecture
• Infrastructure as Code overview: managing Databricks resources declaratively

Module 2: Databricks CLI Mastery

• CLI installation and authentication: PAT tokens and OAuth setup on Linux/Mac/Windows
• Managing clusters: creating, starting, stopping, and resizing via CLI commands
• Workspace operations: importing, exporting, and syncing notebooks from terminal
• DBFS management: uploading datasets and libraries from CI/CD pipelines

Module 3: Databricks REST API

• REST API fundamentals: authentication, pagination, and error handling patterns
• Jobs API: programmatically creating and triggering Databricks Workflow jobs
• Clusters API: dynamic cluster provisioning for cost-optimised pipelines
• Secrets API: injecting database credentials and API keys without hardcoding

Module 4: Testing Data Pipelines

• Unit testing transformations: pytest with in-memory Spark fixtures
• Integration testing: validating end-to-end pipeline outputs against expected datasets
• Nutter framework: running notebook-based tests in CI/CD pipelines
• Data contract testing: schema and quality assertions between producer and consumer

Module 5: CI/CD Pipeline Design

• GitHub Actions workflows: lint, test, and deploy Databricks notebooks automatically
• Azure DevOps pipelines: YAML-based CI/CD for Microsoft-aligned Indian enterprises
• Artifact management: packaging notebooks, wheels, and configs for deployment
• Rollback strategies: safely reverting failed pipeline deployments

Module 6: Infrastructure as Code with Terraform

• Terraform Databricks provider: provisioning workspaces, clusters, and jobs
• State management: remote Terraform state in S3 or Azure Blob for team use
• Module design: reusable Terraform modules for standard Databricks patterns
• Drift detection: identifying manual changes that conflict with IaC definitions

Module 7: Secrets, Security & Compliance

• Databricks Secrets Scopes: integrating with Azure Key Vault and AWS Secrets Manager
• PAT token rotation: automated credential refresh for long-running pipelines
• Audit log automation: streaming CLI activity logs to a SIEM for compliance
• Role-based deployment: restricting production deployments to approved service principals

Module 8: Capstone -- Full DevOps Pipeline

• Capstone: building a complete CI/CD pipeline for a Chennai fintech data product
• End-to-end demo: code commit triggers test -> staging deploy -> prod promotion
• Runbook creation: operational documentation for on-call data engineering teams
• Exam prep: DevOps-related questions in Databricks Professional certificatio

Experience in the Industry Gain expertise from Azure-certified data engineers who have built cloud data platforms using Azure Databricks, ADLS Gen2, Azure Data Factory, and Azure Synapse for enterprises on the Microsoft Azure ecosystem.

Backgrounds at the Top Our Azure Databricks trainers have delivered cloud data engineering projects at financial institutions, retail companies, and healthcare organisations leveraging the full Microsoft Azure data platform stack.

Clear & Effective Teaching Azure Databricks workspace setup, ADLS Gen2 integration, Delta Lake, ADF orchestration, Structured Streaming, Azure ML integration, and Unity Catalog governance are taught with real Azure data platform examples.

Hands-On Learning Focus Students build Azure data pipelines, work with Delta Lake on ADLS Gen2, orchestrate notebooks with ADF, stream from Event Hubs, and configure Unity Catalog through structured hands-on lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Azure Databricks releases, Microsoft Fabric integration considerations, Unity Catalog on Azure enhancements, and evolving Azure data platform best practices.