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What's Data Quality Studio

Data Quality Studio is Atlan's native data quality module that enables business and data teams to collaborate on defining, monitoring, and enforcing data quality expectations directly within the Atlan platform.

Why it exists​

Data teams often rely on disconnected scripts or tools to define and run quality checks. These are typically siloed, difficult to maintain, and fail to deliver visibility to business users. This leads to:

  • Blind spots in data pipelines
  • Delayed issue detection
  • Lack of trust across the organization

Data Quality Studio bridges these gaps by embedding data quality into your warehouse and surfacing trust signals across Atlan, where your teams already work.

What Data Quality Studio enables​

With Data Quality Studio, you can:

  • Define expectations about your data using familiar SQL logic
  • Execute checks where your data lives, directly in the warehouse
  • Surface trust across Atlan through warnings, trust scores, and notifications

This helps build a proactive, transparent culture of data trust across your organization.

Who is it for​

Data Quality Studio is designed for:

  • Analytics engineers who own data transformation pipelines
  • Data stewards responsible for data quality and governance
  • Business users who need visibility into data they can trust

Each persona benefits from embedded checks, alerts, and transparency across the data lifecycle.

Core mental model​

To understand how Data Quality Studio works, here are some key terms:

  • Rule: A SQL-based expectation about your data
  • Rule set: A group of related rules, typically applied to a table or dataset
  • Check run: Execution of rules in your warehouse
  • Status: The result of a check—passed, failed, or warning
  • Trust signals: Visual indicators and alerts shown in Atlan

These concepts form the foundation of how data quality is evaluated and shared.

How it works​

Data Quality Studio uses a push-down execution model. Rules are defined through Atlan’s interface and executed natively in your data warehouse without needing additional infrastructure.

The flow looks like this:

  1. Define rule sets using SQL logic that reflects your data expectations
  2. Push execution to your warehouse triggers native compute in your environment:
    • Snowflake: Executes rules via Data Metric Functions (DMFs)
    • Databricks: Leverages Delta Live Tables for rule execution
  3. Capture results as check runs with pass, fail, or warning statuses
  4. Surface signals through trust scores, visual indicators, and metadata in Atlan
  5. Notify users using alerts and integrations when checks fail or thresholds are breached

This system ensures quality checks are versioned, repeatable, and integrated into your data stack.

Key capabilities​

These are the core capabilities that power the system:

  • SQL-based rule authoring
  • Versioned execution and history tracking
  • Multi-rule validation per dataset
  • Alerting and integrations with downstream tools
  • Trust scoring and visual feedback in Atlan
  • Query-based diagnostics for failed rules
  • Centralized rule management

These features combine to help teams operationalize trust across every dataset.

Next steps​

To get started, explore:

See also​