Anomalo Integrates With Databricks to Help Enterprises Build Confidence in Their Data

Anomalo, the complete data quality platform company, announced support for Databricks, the Data and AI Company, to help customers build confidence in the data they use to make decisions and build products. Anomalo customers can now connect to their Databricks Lakehouse and start monitoring the quality of the data in any table, without writing code, configuring rules or setting thresholds.

A leader in the data warehousing and machine learning space, Databricks helps organizations streamline their data ingestion and management and make that data available for everything from business decision-making to predictive analytics and machine learning.

However, dashboards and data-powered products are only as good as the quality of the data that powers them. When scaling their data efforts, many companies quickly encounter one unfortunate fact: much of their data is missing, stale, corrupt or prone to unexpected and unwelcome changes. As a result, companies spend time dealing with issues in their data rather than unlocking that data’s value and are at risk of silent failures in the data that might go undetected for months or more.

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Anomalo addresses the data quality problem by monitoring enterprise data and automatically detecting and root-causing data issues, allowing teams to resolve any hiccups with their data before making decisions, running operations or powering models. Anomalo leverages machine learning to uncover a wide range of data failures with minimal human input. If desired, enterprises can fine-tune Anomalo’s monitoring through the no-code configuration of metrics and validation rules. This is in contrast to legacy approaches to monitoring data quality that require extensive work writing data validation rules or setting limits and thresholds.

Databricks customers can now begin monitoring the quality of their data with Anomalo in under five minutes. They simply connect Anomalo’s data quality platform to their Databricks account and select the tables they wish to monitor. No further configuration or code is required.

“The Databricks Lakehouse Platform allows teams to unify their data engineering, analytics, and machine learning use cases all on a single platform. That makes Databricks a perfect partner in Anomalo’s vision of providing automated data quality monitoring for the entire enterprise,” said Elliot Shmukler, co-founder and CEO of Anomalo.

“Whether you’re using your Databricks Lakehouse for analytics or machine learning and AI, your results are only as good as the quality of the underlying data. So, we’re excited to partner with Anomalo to give Databricks customers a great tool for automatically detecting and understanding the root-causes of data issues – thus preventing such issues from leading to incorrect BI dashboards or broken machine learning models,” said Roger Murff, VP, ISV Partners at Databricks.

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