Snowflake BUILD | Data Pipeline Monitoring With Snowflake Cortex ML-based Functions

What if data engineers could use ML to forecast growth in their data pipelines, then identify outliers and trigger alerts when monitoring the health and performance of those pipelines? Or when monitoring data quality?

The new ML-based Snowflake Cortex functions, Forecasting and Anomaly Detection, empower data engineers to generate accurate forecasts of data volume growth and find outliers that should be investigated. They also help data engineers find unlikely-to-happen-again situations that can be excluded from data pipelines with just a couple of simple SQL commands. No ML expertise required.

Tune into this session from Snowflake BUILD to learn about Forecasting and Anomaly Detection. See an in-action demo on how they generate predictions and detect anomalies for a single time series or multiple time series.

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