B2B E-commerceSourced

Case Study: Faire Slashes Data Pipeline Costs by 70% with Snowflake and Select Star

Faire Case StudySourced & dated by Case Study Desk

Key facts · TL;DR

Company

Faire

Industry

B2B E-commerce

Challenge

A data explosion from rapid growth caused confusion across hundreds of columns

Headline result

As Faire scaled to 100,000 brands and 700,000 retailers, Select Star helped cut core data-pipeline costs by 70% and analytics-engineering debugging hours by 80%.

Key results

70%

Reduction in core data-pipeline costs

80%

Decrease in analytics-engineering debugging hours

20%

Boost in user engagement

75%

Reduction in average BI query runtime

from the Snowflake migration

The challenge

Faire's rapid growth to 100,000 brands and 700,000 retailers caused a data explosion its previous warehouse could not handle, leading to cluster halts and downtime. Hundreds of columns across tables left users unsure which metrics to use, and they often selected the wrong values.

The solution

Faire migrated to Snowflake for scalability and implemented Select Star as a centralized data catalog, using popularity rankings, column-level lineage, and common-join aggregation to identify the columns actually in use.

“engage users early with exactly how each upstream change impacts their downstream workflows”
BT
Ben Thompson
Staff Analytics Engineer, Faire

The results, in context

Faire reported a 70% overall reduction in core data-pipeline costs and an 80% decrease in debugging hours for analytics engineering, alongside a 20% boost in user engagement. The Snowflake migration also reduced average BI query runtime by 75%.

Products used

Select Star Select StarSelect Star Column-Level LineageSelect Star Popularity Rankings