DATABRICKS DATA QUALITY
Trust the data in your Databricks lakehouse
digna monitors, validates and tracks Databricks data directly in your workspace: anomalies, rescued records, duplicates, schema drift and delivery times, with nothing copied out for checking. AI learns what normal looks like, so you don’t write an expectation for every case.
Your Databricks jobs can be green while your data is not
Workflows can complete. Delta tables can commit. And the gold-layer dashboards your business reads can still be built on incomplete, duplicated or out-of-date data. digna monitors the data itself, catching quality problems that job and pipeline monitoring were never built to see. Combine the capabilities below into the Databricks data quality solution you actually need.
Monitor Databricks data where it lives
digna runs data quality checks directly in your Databricks workspace — nothing is copied into an external platform just to be checked.
Cover what you expect, and what you don’t
Expectations still matter. A column shouldn’t be NULL. A key should exist in the reference table. A job should land before 6:00 AM. digna keeps validating all of that — and watches for what no expectation describes: a table quietly losing rows, a distribution shifting, a job finishing later every night.
What breaks quietly in a lakehouse
The defects that reach your dashboards are rarely the ones that fail a job. They pass through ingestion, transformations and commits without raising anything, then surface weeks later as numbers nobody can reconcile.
One data quality layer for every Databricks deployment
Whether your workspaces run on AWS, Azure or Google Cloud, and whether the data sits in bronze, silver or gold, digna provides one consistent data quality layer.
Delta Lake tables
Monitor the tables and datasets your workloads actually depend on.
Unity Catalog workspaces
Observe data across catalogs and schemas and catch unexpected changes.
Critical business data
Protect financial, customer, regulatory and operational data continuously.
AWS, Azure or Google Cloud
Stay within your existing infrastructure and security model.
Choose where to start
Start with the problem you need to solve. Combine capabilities as your requirements grow.
A defect in bronze does not stay in bronze
Medallion pipelines carry whatever they inherit. A bad batch landing in bronze is transformed into silver and aggregated into gold, and by the time a number looks wrong on a dashboard it has usually been wrong for several layers.
Databricks data quality questions
Direct answers about how digna runs inside a Databricks workspace, what it detects and what it leaves untouched.
