Case Study: Datasembly saves 90% of data engineering time with Select Star

How Datasembly Saves 90% of Data Engineering Time with Select Star

Datasembly, a provider of real-time hyperlocal market intelligence data, faced significant challenges managing the complex and largely undocumented lineage of its massive pricing dataset. This lack of visibility, with dependencies locked in engineers' unwritten knowledge, led to data inconsistency across teams, inefficient project planning, and a reluctance to implement changes for fear of breaking downstream client deliverables. To solve this, they implemented the Select Star data discovery platform.

The Select Star solution provided comprehensive data lineage visualization and AI-assisted documentation, giving the team a complete map of their data ecosystem. This enabled Datasembly to cut the time for data projects by 90%, reducing an 80-hour process to just 6 hours with high confidence. The implementation also saved over $30,000 annually in Snowflake costs while improving data consistency and fostering more confident change management.

Datasembly

Jamie Hollowell

Lead Data Engineer