Anomalo to Speak About How Unsupervised Machine Learning Can Scale Data Quality Monitoring in Databricks at DATA+AI Summit 2022

Anomalo, the complete data quality platform company, announced that it has a session titled “How Unsupervised Machine Learning Can Scale Data Quality Monitoring in Databricks” at the DATA+AI Summit 2022.

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Technologies like Databricks Delta Lake and Databricks SQL enable enterprises to store and query their data. But existing, manually-configured rule and metric approaches to monitoring the quality of this data are tedious to set up and maintain, fail to catch unexpected issues and generate false positive alerts that lead to alert fatigue.

On Thursday at 11:30 a.m., Jeremy Stanley, co-founder and CTO of Anomalo, will describe a set of unsupervised machine learning algorithms for monitoring data quality at scale in Databricks. He will cover how the algorithms work, their strengths and weaknesses relative to a rules-based approach and how they are tested and calibrated. Attendees will gain an understanding of unsupervised data quality monitoring and how to begin monitoring data using Anomalo through Databricks. Anomalo is providing a f********* exclusively to Databricks customers so that they can start monitoring their tables immediately to detect and root-cause data quality issues.

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Anomalo will showcase its data quality platform in booth #305 at the DATA+AI Summit 2022.

“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.

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