Your Databricks tables hold valuable insights, but without the right monitoring in place, detecting issues like anomalies or unexpected changes can feel like finding a needle in a haystack.
We’ll show you how to take the guesswork out of data quality monitoring on Databricks, using Soda’s AI-powered anomaly detection and metric monitoring.
What you’ll learn from Tyler Adkins:
- How to set up automated data profiling on your Databricks tables
- How to detect anomalies with AI-powered metric monitoring
- How to keep track of your data health over time with alerts and insights
