Case Study: Prymat achieves faster SQL Server to BigQuery ETL with Etlworks

Prymat Scales SQL Server to BigQuery ETL with Partitioned Workflows in Etlworks

Prymat, a leading European food manufacturer, needed a way to move and synchronize large volumes of data between on-premise SQL Server databases and Google BigQuery. Its challenge was handling very large, continuously growing tables, slow ETL loads, and the need for bidirectional data flows without adding maintenance overhead.

Etlworks implemented partitioned, parallel ETL workflows with native SQL Server and BigQuery integration, plus reverse ETL to sync curated data back into operational systems. With automated scheduling, monitoring, and recovery built in, Etlworks reduced ETL execution time by over 80% for large tables, improved data availability to near real time, and created a scalable, low-maintenance integration architecture.

Prymat