Case Study: Etam achieves scalable data monitoring and better ROI with Sifflet

Etam scales data monitoring in 3 months with Sifflet

Etam, a French fashion retailer, faced a challenge with its modern data stack built on Snowflake and dbt. Despite having a robust technical setup, their lean data team lacked visibility into data issues, leading to silent errors like broken joins and schema changes that could impact business reports and cause Snowflake costs to spiral. They needed a solution from Sifflet for automated data observability to catch these errors proactively.

Sifflet implemented its intelligent data monitoring platform, layering automated checks for freshness, volume, distribution, and schema drift across Etam's environment in just days. The AI-powered solution provided dynamic alerting and integrated with their existing GitHub workflow. As a result, Etam's team scaled its monitoring capabilities, gained proactive insights into technical issues, and optimized its Snowflake ROI by ensuring the platform delivers trusted, business-ready data.

Etam