My main use case is that we deployed Sifflet to solve a critical lack of visibility into the data health of a retail client's AWS-based data lake: S3, Glue, Redshift. The implementation focused on Sifflet's ML-driven anomaly detection to monitor over 1,500 tables and 10 million hourly records. By integrating via AWS Marketplace, we moved from manual SQL validation to automated monitoring of metadata and query logs. This allowed us to detect silent failures, such as partial loading or subtle schema drift, that were previously invisible to the engineering team.
My main use case is that we deployed Sifflet to solve a critical lack of visibility into the data health of a retail client's AWS-based data lake: S3, Glue, Redshift. The implementation focused on Sifflet's ML-driven anomaly detection to monitor over 1,500 tables and 10 million hourly records. By integrating via AWS Marketplace, we moved from manual SQL validation to automated monitoring of metadata and query logs. This allowed us to detect silent failures, such as partial loading or subtle schema drift, that were previously invisible to the engineering team.