Anomalo, the complete data quality platform company, today announced that Anomalo is available on Databricks Partner Connect. Partner Connect helps Databricks customers discover validated data, analytics and AI tools and integrate these tools directly within their Databricks Lakehouse. One of Databricks’ first partners in data quality, Anomalo is providing an exclusive free trial to Databricks customers so that they can start monitoring their tables immediately to detect and root-cause data quality issues if they are not already an Anomalo customer.

“Data quality has become a foundational part of the modern data stack,” said Roger Murff VP, ISV Partners at Databricks. “Businesses shouldn’t have to wait until data quality issues pile up, when they become harder and more costly to fix. With Databricks Partner Connect, we’re excited about the potential to help enterprises be proactive about data quality, allowing them to try out Anomalo with a few clicks and instantly see the value of high-coverage, accessible, automated monitoring.”

Today’s announcement follows the recent news that Anomalo is integrated with Databricks, and makes it even easier for shared customers to use Databricks and Anomalo.

Databricks combines the best of data warehouses and data lakes to offer an open and unified platform for data and AI. This 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.

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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.

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. This is in contrast to legacy approaches to monitoring data quality that require extensive work writing data validation rules or setting limits and thresholds.

With today’s announcement, Databricks customers can now begin monitoring the quality of the data in their Databricks Lakehouse immediately within their Databricks workspace.

“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 Databricks to give their 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 Elliot Shmukler, co-founder and CEO of Anomalo.

Popular Use Cases

• Business intelligence / Analytics: When users track key business metrics, it is critical that they are able to understand why those metrics are moving and detect and resolve any metrics-impacting data quality issues. They need to understand if a movement in the metric was really caused by a change in customer behavior or instead happened because data was missing or got stuck in a downstream pipeline. With Anomalo, users get automatic monitoring of key metrics as well as checks on the underlying data in their lakehouse, so issues that cause unexpected metrics movements are quickly detected.
• Machine Learning: Data quality issues may result in a machine learning model seeing data that looks very different from what it was trained on, leading to erratic and suboptimal model behavior. To avoid this, Anomalo lets users quickly and easily monitor data that’s used as input into machine learning models in order to detect any unusual changes or data drift.

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