Databricks Alerts for Data Mismatches

Have you ever expected data downstream only to find it missing, even though pipelines show no failures? These elusive "transient errors" often cause source-to-target data mismatches that get discovered too late. In this video, learn how to proactively detect and alert on such data mismatches using simple SQL count checks and Databricks alerts. Watch as I create source and target demo tables, run quick data quality comparisons, and build alert logic to notify you when mismatches occur—before the business is impacted. I also show how to simulate data load mismatches with synthetic data using Python’s Faker library, demonstrating real-world scenarios of data syncing issues. Finally, see how to schedule, customize, and scale these alerts within your pipelines, giving your data team more confidence and control over data quality. Whether you’re building medallion architectures or managing complex data pipelines, this solution helps you get ahead of data issues early.