At Anomalo, we believe data quality should always be highly visible and easy to access, no matter what tools your business uses for its data stack. That’s why we prioritize interoperability, working closely with data warehouse, lakehouse, and catalog providers to ensure that our platforms fit together seamlessly.
As part of this goal, we’re thrilled to announce that Anomalo is a founding partner of Alation’s Open Data Quality Initiative. Alation conducted research showing that data quality is the most impactful data governance issue organizations face, affecting 89% of their survey participants. This is why we have prioritized displaying Anomalo’s data quality checks directly in the UX of the Alation Data Catalog.
How Anomalo and Alation are integrated
Anomalo’s data quality checks are integrated with the Alation Data Catalog in two places: 1) in the “Overview” for each table in the catalog, and 2) in a new “Health” section for each table.
1. Table overview
There’s now a custom subsection in Alation’s table overview where Anomalo is able to output its daily data quality checks.
Each Alation table monitored by Anomalo provides a hyperlink back to the table view within Anomalo. If a table fails any data quality checks, Anomalo flags this as a “deprecation” and provides a visual warning. If a table passes its data quality checks, Anomalo “endorses” the table with a visual flag.
2. Health section
With its new Open Data Quality Framework, Alation has created a data health section in the catalog. Each of Anomalo’s data quality checks will be shown here in detail, with an indicator of whether the check passed or failed. Clicking on the link for each check will take you directly to the visualizations and root cause analysis inside Anomalo.
We’re excited to bring this integration to our mutual customers. For instructions on the integration, contact a member of the Alation or Anomalo team.
Want to explore more ways that automated data quality monitoring can fit into your data stack? Learn about how Anomalo works with Snowflake and Databricks.