Case Study: Fair (Automotive FinTech) achieves near-real-time insights from 40M records and 500 data sources with Snowflake

Achieving Near Real-Time Insights with Snowflake

Fair is a Santa Monica–based automotive FinTech that powers a widely used app for flexible car access, ingesting billions of data points from more than 500 sources to support supply chain, product and marketing decisions. Its legacy data warehouse couldn’t scale to meet growing demand: dealer inventory was imported only once per day, ETL jobs took hours and clusters failed frequently, and parsing nested JSON from microservices required significant engineering effort.

By migrating to Snowflake, Fair consolidated 500 sources into a single source of truth, used Snowflake’s native semi-structured support to parse nested JSON, and enabled near real-time ingestion of 40 million vehicle records per day. ETL runtimes dropped from hours to under five minutes, more than 200 analysts could run concurrent queries, supply‑chain analytics saved the company millions, and teams gained faster, more productive access to data for ML, pricing, product and marketing.

Fair

Brandyn Abrams

Director of Analytics