Announcing Anomalo’s Integration with Microsoft Purview: Data Quality Where You Work

Whether you’re powering insights with Azure Databricks, enabling analytics in Azure Synapse Analytics, or managing data assets on Microsoft Fabric, ensuring your data is clean, accurate, and trustworthy is non-negotiable. Yet all too often, data quality checks are siloed, requiring additional tools and disrupting your workflows. 

That’s why we’re thrilled to announce Anomalo’s integration with Microsoft Purview, designed to embed Anomalo’s rich data quality insights directly into the Purview UI. With this integration, teams can see the health of their monitored assets at a glance, right where you already work–whether that’s in Purview itself or integrated tools like PowerBI or Microsoft 365 Compliance–without switching tools or context. 

Anomalo Works Where Your Data Team Works

Imagine exploring a dataset in Purview and needing to assess its quality before making a decision. Instead of toggling between tools or relying on external reports, the insights you need are now available at your fingertips. This integration was built for data teams working across the Microsoft Azure ecosystem, so you can:

• Quickly Trust Your Data: See which assets are monitored and whether they pass critical checks like data freshness, anomalies, or availability—all within Purview.
• Save Time and Stay Focused: Avoid the disruptions of logging into another tool. Everything you need is embedded directly in Purview.
• Streamline Your Governance Processes: Confidently certify tables and views that meet your data quality standards, making governance more transparent and efficient.

Key Capabilities that will Enhance Your Azure Analytics Workflows

1. Data Quality at a Glance

Every monitored asset in Purview displays a clear pass/fail summary for six core data quality checks, such as freshness, anomalies, and completeness. These summaries provide an instant snapshot of data health:

See Anomalo’s automated data quality checks right inside Microsoft Purview. Here, we examine the quality of an Azure Databricks table.

Say you’re an analytics leader selecting datasets for a dashboard. You can now prioritize tables with passing scores, ensuring the underlying data is sound at-a-glance, saving time during asset discovery and selection.

2. Deep Links for Visual Context and Faster Resolution

When working in Purview, gaining deeper insights about a specific table or view often requires additional steps and tools. With Anomalo’s integration, each monitored asset now includes a visual analysis of table content and a full column-level data profile, plus a deep link to the Anomalo UI, allowing you to seamlessly dive into the details of data quality checks, historical performance trends, and root cause analyses.

To explore the underlying check and investigate root cause, simply click through to Anomalo to explore anomalies detected in the table’s recent updates.

Say you’re a data engineer reviewing a table with failing data quality checks in Purview. You can now quickly understand why an asset passed or failed a data quality check without ever leaving your primary workspace. To triage and resolve the situation, simply click through to Anomalo to explore and resolve anomalies detected in the table’s recent updates.

Once inside Anomalo, out-of-the-box column-level visualizations and data profiles give users deeper insights into dataset contents for easy triage and investigation. A data analyst reviewing a table can quickly see column distributions, null values, and data types, making it easier to assess data and AI-readiness for their specific analysis or data project.

3. Asset Labels for Easy Search and Certification

Purview users can now filter and search for assets using custom labels like “monitored,” “unmonitored,” “passing,” or “failing.” These labels make it simple to identify data quality issues or confirm which datasets are actively being checked.

Say you’re a data governance lead auditing data compliance. You can now use the “failing” label to create a targeted action plan for problematic tables, quickly locate assets that meet specific criteria, and enable faster problem detection and governance.

Moreover, certified tables and views give teams confidence that these assets are safe to use in analytics, reporting, and decision-making. A data steward certifies a dataset as “ready for use” in a compliance report, ensuring stakeholders only use approved, quality data–improving trust and transparency across teams by designating high-quality datasets.

Quickly search or filter among your Azure data tables that have passed or failed data quality checks.

4. Automate Managed Attribute Creation for Easy Deployment

Finally, for teams using Azure Databricks, Anomalo automatically creates managed attributes inside Purview for you. This ensures integration set up is quick and accurate. Your Azure Databricks tables and views will always display the latest quality check results, keeping your data governance processes up-to-date.

Say you’re a data engineer integrating Anomalo with Microsoft Purview to democratize data quality on your Azure Databricks tables. Our integration automatically registers and adds definitions for managed attributes so you don’t have to configure them through the Purview UI or ask a Data Map administrator for help.

Built with Flexibility in Mind

Anomalo’s Purview integration is powered by a Python script that synchronizes Anomalo and Purview using Microsoft APIs. To support diverse deployment scenarios, the script is hosted in your environment—whether on a VM or a serverless function—and runs on a schedule you control (typically every 6–24 hours). This approach keeps your workflows highly secure and adaptable to your enterprise’s unique operational needs.

We also understand the critical role that services like Microsoft Purview, Fabric, Azure Databricks, and Azure Synapse Analytics play in your data strategy. That’s why we built this integration to fit seamlessly into your existing workflows, helping you drive faster insights, maintain compliance, and reduce time-to-resolution for data quality issues.

Start Scaling Data Quality with Anomalo on Azure

Ready to see how Anomalo’s bidirectional Azure Purview integration can transform the way you discover and manage your data on Microsoft Azure? Request a demo of Anomalo’s integration with Microsoft Azure today.

With Anomalo in Purview, data teams are empowered to trust their data and make better decisions—right where it happens.