SNOWFLAKE DATA QUALITY
Trust the data inside your Snowflake account
digna monitors, validates and tracks Snowflake data directly in your account: anomalies, rows skipped at load, duplicates, delivery times and schema changes, with nothing copied out for checking. AI learns what normal looks like, so you don’t write a rule for every case.
Snowflake can be running perfectly while your data is not
Warehouses can be sized right and queries can be fast. Snowpipe can keep loading and tasks can keep succeeding. And the dashboards built on your reporting marts can still be built on incomplete, duplicated or out-of-date data. digna monitors the data itself, catching quality problems that platform and pipeline monitoring were never built to see. Combine the capabilities below into the Snowflake data quality solution you actually need.
Monitor Snowflake data where it lives
digna runs data quality checks directly in your Snowflake account — nothing is copied into an external platform just to be checked.
Cover what you expect, and what you don’t
Rules still matter. A column shouldn’t be NULL. A key should exist in the reference table. A load should land before 6:00 AM. digna keeps validating all of that — and watches for what no rule describes: rows quietly vanishing, a distribution shifting, a pipeline creeping toward the reporting window.
What breaks quietly in Snowflake
The defects that reach reporting are rarely the ones that raise an error. They pass through loads, transformations and tasks without failing anything, then surface weeks later as numbers nobody can reconcile.
One data quality layer for every Snowflake account
Whether your data sits in raw landing schemas, curated marts or datasets shared with other teams, and whether the account runs on AWS, Azure or Google Cloud, digna provides one consistent data quality layer.
Databases and schemas
Monitor the tables and views your workloads actually depend on.
Reporting marts
Observe large-scale analytical datasets 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.
Prove the data still matches after a migration to Snowflake
Moving a warehouse onto Snowflake, often from Oracle or Teradata, puts every downstream report at risk. digna monitors the source and the target, so you can see dataset by dataset whether what arrived matches what left.
Snowflake data quality questions
Direct answers about how digna runs inside a Snowflake account, what it detects and what it leaves untouched.
