Anomalo supports Azure Synapse Analytics and Microsoft SQL Server for data quality monitoring

November 23, 2022

Connect Anomalo to Azure Synapse Analytics or Microsoft SQL Server and get comprehensive, fully-automated data monitoring on the tables you care about within minutes. Anomalo is excited to extend support of Microsoft data products beyond Azure Databricks so organizations can build trust across all of the data they care about.

Today’s modern data-powered organizations are using Microsoft to centralize all of their data and make it easily available for business decision-making and predictive analytics. However dashboards and data-powered products are only as good as the quality of the data that powers them. Enterprises need data quality tools that can help them detect and resolve complicated data issues, before issues affect BI dashboards and reports or downstream ML models.

“With Anomalo, Microsoft customers of Azure and SQL Server now have a comprehensive way to automatically detect data issues and understand their root causes.” said Mahesh Prakriya, Director in Microsoft’s Intelligence Platform. “We are excited to work with Anomalo and help provide Microsoft customers a platform for data observability and data quality monitoring.”

“There is no one size fits all for cloud architectures, especially for the enterprise customers Anomalo serves.” said Amy Reams, Vice President of Business Development at Anomalo. “That’s why we prioritize interoperability, working closely with data warehouses, lake houses, and relational databases like SQL Server-to ensure that an enterprise with any stack can have confidence in their data. “

See how easy it is to connect and start monitoring your data tables with Anomalo with the security and privacy you need in either our SaaS or an in-VPC deployment.

Request a demo and let us show you what Anomalo can do. https://www.anomalo.com/requestademo

Written By
The Anomalo Team
Try Anomalo with your team for free.
Lorem ipsum dolor sit amet, cour adipiscing elit ullam congue.
Data observability might be sufficient if you’re in the early innings of your data journey, but if you’re using data to make decisions or as an input into ML models, as our customers are, then basic checks are not enough to ensure your data is accurate and trustworthy.
Jobin George
Staff Solutions Consultant, Cloud Partner Engineering