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

Datasembly Case StudySourced & dated by Case Study Desk
Key facts · TL;DR
Company
Datasembly
Industry
Retail / Pricing Data
Challenge
Undocumented lineage in a complex pricing dataset slowed changes
Headline result
Datasembly cut data-project time by more than 90% and saved over $30k a year in Snowflake costs after adopting Select Star's lineage and documentation.

Key results

90%+
Reduction in data-project time
80 hours to ~6
$30k+
Saved per year in Snowflake costs
estimated $30k-$40k annually
6 hrs
To complete work that took 80 hours

The challenge

Datasembly's pricing dataset relied on engineers' unwritten knowledge of undocumented data lineage. Uncertainty about downstream impact made the team reluctant to implement changes, and project planning was inefficient. Some analyses took up to 80 hours at only a 50% confidence level.

The solution

Datasembly implemented Select Star for detailed lineage visualization, centralized AI-assisted documentation, and an interface for understanding data dependencies.

“It feels like having another engineer on the team.”

JH
Jamie Hollowell
Lead Data Engineer, Datasembly

The results, in context

Work that previously took up to 80 hours can now be completed in about 6 hours with high confidence, a reduction of more than 90%. The company estimates saving $30,000 to $40,000 per year in Snowflake costs.

Products used

Select Star Select StarSelect Star Column-Level LineageSelect Star AI Documentation