Case Study: a large regional bank achieves 10× faster analytics and lower Redshift costs with Dremio and Apache Iceberg

a leading regional bank speeds analytics 10x with Dremio

A leading regional bank faced challenges with its mission-critical Customer Contact Center executive dashboards. Their complex data pipeline, which moved data from an Amazon S3 data lake to Amazon Redshift, created significant latency, with queries taking over a minute to run. This performance issue frustrated executives who needed real-time insights, while the architecture also incurred high costs and management overhead. The bank turned to Dremio to modernize their data platform and address these limitations.

Dremio implemented a solution using Apache Iceberg tables on the bank's S3 data lake, eliminating the need for Redshift and simplifying the entire data pipeline. This resulted in a 10x performance improvement, reducing query times from 80 seconds to just 10-12 seconds. The solution provided immediate cost savings by removing Redshift licensing fees and significantly reduced operational overhead. Dremio enabled the bank to decommission Redshift for this use case while maintaining all advanced analytical capabilities.