Atlan Launches Data Quality Studio for Databricks, Activating Trust for the AI-Native Era

Key takeaways

Data Quality Studio runs business-defined checks natively in Databricks without moving data or adding infrastructure

Business and data teams define quality expectations in plain language or SQL that execute directly in the lakehouse

Real-time trust signals surface in Atlan, Unity Catalog, and BI tools to guide users before they act on data

The integration deepens Databricks + Atlan partnership creating powerful architecture for AI-native enterprises

SAN FRANCISCO, June 10, 2025 — Atlan, the leading metadata platform for modern data and AI governance, today announced the launch of Data Quality Studio for Databricks at the 2025 Data + AI Summit. This new module integrates natively with Databricks, enabling teams to operationalize business-defined quality checks directly in the lakehouse — without moving data or building new infrastructure.

As enterprises race to productionize AI, the lack of trust in data remains one of the biggest blockers. It’s not just about whether pipelines succeed — it’s about whether the data is fit for the specific purpose it’s being used for.

“AI breaks when it runs on untrusted data,” said Prukalpa Sankar, Co-founder of Atlan. “With Data Quality Studio, quality expectations are defined by the people who understand the business need — and run natively in Databricks. Together, Atlan and Databricks are empowering customers to move faster with trusted AI.”

Quality, Defined by the Business, for the Business

Most data quality tools stop at technical checks. But an AI model or dashboard can succeed technically — and still fail to deliver value — if the data it relies on doesn’t meet the expectations of the business team.

Data Quality Studio closes this gap by letting business and data teams define what “good data” means for a specific use case. Whether it’s freshness or null thresholds, expectations can be captured in plain language or SQL, then executed directly inside Databricks.

Built on Databricks, Activated by Atlan

Business-first rule authoring: Quality expectations can be defined using simple, no-code templates or full SQL, within Atlan’s collaborative studio.
Native execution in Databricks: Atlan pushes down checks to run in Databricks — keeping data in-place and avoiding new infrastructure.
Real-time trust signals: Scores, warnings, and trust badges show up directly in Atlan, Unity Catalog, and BI tools to guide users before they act.
Unified trust engine for AI: Atlan aggregates signals from Databricks and tools like Anomalo, Monte Carlo, or Soda, giving teams one pane of glass for trusted data and AI.

The Metadata Lakehouse: Trust for the AI-Native Enterprise

This launch deepens the integration between Databricks’ Lakehouse Platform and Atlan’s Metadata Lakehouse — a unified layer that captures and activates metadata from across the data estate. Together, the two systems form a powerful architecture for AI-native enterprises: Databricks as the engine for data and AI, and Atlan as the metadata control plane to govern it with context, meaning, and trust.

Availability

Data Quality Studio for Databricks is now in private preview. To request access, visit atlan.com/data-quality-studio or contact your Atlan representative.

About Atlan

Atlan is the leading metadata platform for data and AI governance. As a metadata control plane, Atlan unifies context, quality, and trust signals across modern data estates — empowering teams and AI agents to find, understand, and govern data at scale. With deep integrations across the modern data stack, Atlan is trusted by enterprises like General Motors, Nasdaq, Fox, and Unilever to drive AI transformation and data democratization. Learn more at atlan.com or follow us on LinkedIn and X (@AtlanHQ).

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