• Learning Path
Libraries: This path is only available in the libraries listed. To access this path, purchase a license for the corresponding library.
• Data

This learning path is actively in production. More content will be added to this page as it gets published and becomes available in the library. Planned content includes:

1. Up and Running with Apache Spark on Databricks (video course) 2. Manage Data and Delta Tables with Apache Spark on Databricks (video course) 3. Build Batch Pipelines with Apache Spark on Databricks (video course) 4. Build Streaming Pipelines with Apache Spark on Databricks (video course) 5. Build Declarative Pipelines with Apache Spark on Databricks (video course) 6. Build AI-Powered Pipelines with Apache Spark on Databricks (video course) 7. Run Production Workloads with Apache Spark on Databricks (video course) 8. Optimize Apache Spark on Databricks (video course)

Apache Spark can be used within the Databricks platform to build, manage, and optimize scalable data workloads. In this path, you’ll learn how to work with Delta tables, build batch and streaming pipelines, use declarative pipeline workflows, run production workloads, and improve Spark performance in Databricks. You’ll also explore how modern Databricks capabilities support reliable, governed, and AI-ready data engineering.