Partitioning limitations for data lake analytics

Data lake analytics are increasingly popular among data-driven companies, but managing the vast volume of data to optimize performance and cost-effectiveness remains a challenge. While partitioning strategies, such as z-ordering and clustering, can reduce data scanning, they often fall short due to the dynamic nature of query patterns and the need to filter across multiple columns. On average, 80% of compute resources are spent on ScanFilter operations, indicating that current partitioning methods are inadequate. Additionally, excessive partitioning can lead to data skew, long query response times, and degraded performance. To address these issues, Starburst offers a smart indexing solution called Warp Speed, which uses nanoblock indexing to dynamically create efficient, multi-dimensional indices without altering existing data layouts. This approach allows companies to maintain their current partitioning strategies while significantly improving query performance across diverse workloads. Warp Speed can be easily implemented through the Starburst Galaxy platform, providing an accessible way for organizations to enhance their data lake analytics.