Anomalo Can Now Monitor an Entire Data Warehouse in Minutes With Major Expansion

Anomalo, the complete data quality platform company, announced that it 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. Anomalo will be showcasing this new capability this week at the Data + AI Summit by Databricks and Snowflake Summit.

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“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 launched in 2021 with the industry’s most robust deep data quality monitoring platform. Customers include some of the biggest brands like Block, BuzzFeed, Discover Financial Services, Notion and Substack.

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.

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“While companies need accurate data, they also need to reduce expensive compute workloads wherever possible. Anomalo’s two-tiered approach to data observability helps enterprises go deep where it matters–for example, to check the quality of financial or compliance-sensitive records–but avoid unnecessary processing of lower-tier data,” said Kevin Petrie, vice president of research at Eckerson Group.

“Adding the new functionality of table observability gives our data teams another tool to use to ensure data quality is monitored at Block so our users and customers have trust in our data. Data observability fills a need for the future of our data strategy,” said Tim Ng, engineering lead of data products at Square.

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