Matching the Right Engine with the Right Workload in Cloud Data Warehouses and Lakehouses

Are you starting to feel growing pains with your cloud data warehouse?  A study conducted by our user research team found that 46% percent of teams surveyed were looking to adopt a data lake to:

• Offload existing workloads in their cloud data warehouse due to rising data costs, 
• Increase flexibility in using the best SQL engine for the job
• Reduce the risk of new data applications not scaling with desired economics.

How do you fix these challenges without being forced to start over?  At Starburst, we’ve seen firsthand how pairing your data warehouse with a data lakehouse architecture enables optimized price and performance, promotes increased flexibility, and inspires increased success across the entire organization. By incorporating modern table formats like Apache Iceberg alongside Starburst’s analytics engine based on Trino, you can get the most out of low-cost object storage while still applying warehouse-like principles to the data lake.