TM
TMTM
This learning module focuses on Databricks Connect and Databricks Asset Bundles through conversational scenarios and real-world examples. You'll explore how these tools solve critical challenges data engineers face when integrating Databricks with modern software engineering practices. The goal is to help you understand when and why to use these tools, and how they enable professional development workflows for production data pipelines.
Through these scenario-based conversations, you'll strengthen your understanding of key integration areas:
Databricks Connect architecture — understanding how local IDE development connects to remote Databricks clusters, where Python code runs versus Spark operations, and how data transfers between environments.
Professional development practices — integrating pytest for automated testing, enabling CI/CD pipelines with GitHub Actions, using IDE debugging with breakpoints, and distinguishing when to use notebooks versus Databricks Connect.
Infrastructure as Code with Databricks Asset Bundles — defining Databricks jobs and resources in YAML configuration files, managing environment-specific settings with targets and variables, and maintaining consistency across Dev, Staging, and Production.
Deployment automation — automating Databricks resource deployment through CI/CD pipelines, version controlling job configurations in Git, organizing DAB project structures, and rolling back changes when needed.
By the end, you'll understand how Databricks Connect and Databricks Asset Bundles bridge the gap between notebook-based development and production-grade software engineering practices—enabling testable code, automated deployments, and consistent configurations across environments.