Introducing automated data lake optimization in Starburst Galaxy

Starburst Galaxy introduces automated data lake optimization to enhance query performance and storage utilization in data lakes, addressing challenges posed by modern table formats like Apache Iceberg. Unlike traditional databases or data warehouses, data lakes require manual maintenance, often consuming significant time and resources from data teams. The new automated optimization in Starburst Galaxy encompasses four main operations: data compaction, profiling and statistics, vacuuming, and data retention. Data compaction consolidates smaller files for faster querying, while profiling and statistics refresh metrics for optimal query execution. Vacuuming removes orphaned files resulting from failed queries, reducing storage clutter, and the data retention feature allows users to manage snapshot storage by setting retention thresholds, addressing issues of version control and storage costs. These features aim to streamline data maintenance and are expected to be available for private preview in early December.