How to optimize your Databricks notebooks with AI

An easily missed Databricks AI super power you might not be using... One of the greatest implementations of AI in the Databricks platform is an easily missed little button on the bottom right of your notebook cells...optimize! This is a game-changer, especially for Data Migrations where optimization is required at scale. Having instant insights into performance bottlenecks and inefficient transformation patterns means migrations can be executed more efficiently and predictably. In a nut-shell, this command reads the logic in a notebook cell after it's run, reading the execution plan and profiling data layout, including: 1. Automatically identifying performance bottlenecks that used to require manual Spark UI event log analysis 2. Instantly recognizing data distribution patterns and skew without manual profiling 3. Navigating complex Spark execution plans for you 4. Suggesting efficient code patterns and operations without you having to figure them out yourself This little Optimize button not only identifies inefficient joins and suggests better partition keys, but also eliminates redundant operations you might not even realize are slowing things down. Beyond the time savings, it's also a powerful learning tool. By studying the optimized code versions and tips on data layout, you can improve your Spark knowledge and write better code from the start. #Databricks #DataEngineering #AI #DataMigration