Databricks has fully embraced Apache Spark 3, offering it as part of their Databricks Runtime 7.0 and later versions. Spark 3 brought significant advancements, including:

• Enhanced Python and SQL Support: Improved Pandas UDFs, Python-type hints for better performance, and optimized SQL engine.
• Adaptive Query Execution (AQE): A framework that dynamically optimizes query plans during execution for faster results.
• Dynamic Partition Pruning (DPP) Prunes partitions based on filter conditions, reducing the amount of data scanned and improving query performance.
• Performance Improvements: Various optimizations across the board, including faster shuffle operations and better memory management.

How to Get Started with Spark 3 on Databricks

1. Choose a Databricks Runtime: When creating your cluster, select a Databricks Runtime version 7.0 or later. This ensures you have Spark 3 available.
2. Upgrade Your Code (if necessary): If you’re migrating from an older Spark version, you might need to adjust your code to take advantage of Spark 3’s new features or to address any compatibility changes. Databricks provides a migration guide for assistance.
3. Explore New Features: Dive into Spark 3’s enhanced Python and SQL capabilities, experiment with AQE and DPP, and leverage the performance improvements to optimize your data processing and analysis workflows.