Anomalo, the data quality platform company, has expanded its platform to include metadata-based observability for all tables in an enterprise data warehouse. Enterprises can now do basic monitoring of the entire data warehouse in minutes and at low cost and use that as a pathway into deep data quality monitoring to identify issues with the contents of their data.

“Our customers have always said that they want to monitor every single table in their data warehouse or data lake for data issues. But especially in this environment, applying Anomalo’s full deep monitoring capabilities to every single table is neither necessary nor cost-efficient,” said Elliot Shmukler, co-founder and CEO of Anomalo. “With our new table observability checks, basic monitoring can now be applied cost-efficiently to the entire data warehouse with the flexibility to use Anomalo’s unsupervised data monitoring, metric checks and validation rules only on the most important tables. Thus enterprises now have the flexibility with Anomalo that cover all of their data observability and data quality needs.”

Anomalo’s platform looks inside enterprise data and automatically detects and root-causes data issues, allowing teams to resolve any hiccups with their data before making decisions, running operations or powering models. Anomalo leverages machine learning to rapidly assess a wide range of data sets with minimal human input. If desired, enterprises can fine-tune Anomalo’s monitoring through the low-code configuration of metrics and validation rules.

With the addition of metadata-based monitoring of the entire data warehouse, customers can gain the peace of mind of knowing their entire data warehouse is covered broadly without incurring additional data warehouse costs.

Anomalo will be showcasing this new capability this week at the Data + AI Summit by Databricks and Snowflake Summit.

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