The combination of Snowflake and Telmai enables you to move from legacy Hadoop and data warehouse environments that were bolted on with custom data quality rules to a modern data infrastructure with end-to-end data observability, automated data quality, and ML-driven anomaly detection.
Telmai works as the one platform that can monitor your data quality across all formats – structured and semi-structured. With this centralized observability, you not only get to monitor data in staging and production tables, but also data ingested into Snowflake in a semi-structured form such as OBJECT, ARRAY, or VARIANT.
Telmai monitors the data itself, its content, and values. This is complementary to the operational observability metrics in Snowflake such as job statuses, start times, and duration. Additionally, Telmai monitors not only the metadata such as schema changes, uniqueness, or completeness but also data quality, accuracy, drifts, value anomalies, and patterns changes.
Telmai’s architecture has its own compute layer, which is decoupled from your data warehouse. This helps you eliminate overloading Snowflake with data validation queries and provides you with monitoring capabilities without slowing down your operational data or increasing your Snowflake costs.
We have shared customers and have gone through technical evaluation with Snowflake to ensure our integration is designed for the best performance, architecture, and scale.