Case Study: YipitData achieves 90% faster data processing and nearly 60% lower database costs with Databricks

YipitData turns to its data team to transform financial market information overload into insight

YipitData provides alternative-data research and web-scraped intelligence to help hedge funds, banks, and asset managers make better investment and risk decisions. Faced with billions of monthly requests from hundreds of sites, siloed teams, and a legacy data warehouse that produced severe performance bottlenecks (very large queries could take up to six hours), the company struggled to scale ETL, analytics, and cross-team collaboration.

By moving to Databricks on AWS and adopting autoscaling clusters and notebook-based ETL, YipitData empowered 40+ analysts to operate as hybrid engineers and manage the full analytics workflow. The change sped pipelines by up to 90% (some queries dropped from ~6 hours to ~7 seconds), increased reporting scale 4–5x, cut database costs by about 60% and trimmed annual AWS spend by roughly $2.5M, while simplifying operations and improving collaboration.

YipitData

Steve Pulec

Chief Technology Officer