DQX by Databricks Labs

Simplified Data Quality checking at Scale for PySpark Workloads on streaming and standard DataFrames.

✨ Key capabilities

• Rule-based quality checks — 80+ built-in checks (null, range, regex, referential, aggregate, geo, PII, and more) for row-level and column/dataset-level validation, with support for custom check functions.
• Code or config checks — define checks programmatically as code or declaratively as YAML/JSON configuration.
• Check levels — mark failed checks as warning or error.
• Custom reactions to failed checks — drop, mark, or quarantine invalid data flexibly.
• Detailed failure info — get detailed insights into why each check failed.
• AI-assisted rule generation — LLM-driven rule suggestions from business descriptions, powered by DSPy and Databricks Model Serving.
• Data profiling & rule generation — automatic statistics collection and quality rule candidate generation from existing data.
• ML row anomaly detection — Isolation Forest–based row anomaly detection with SHAP explanations and AI-generated narratives.
• Data contracts — generate quality rules from ODCS data contracts, including schema validation.
• Summary metrics & quality dashboard — built-in and custom aggregate metrics (input/error/warning/valid row counts, per-check breakdowns) persisted to Delta tables, with a Lakeview quality dashboard for tracking and identifying data quality issues.
• Actions and alerting — automatically send Slack, Microsoft Teams, or generic webhook alerts and/or fail the pipeline when summary metrics cross a threshold.
• Flexible checks storage — save and load quality rules from YAML/JSON files, Unity Catalog tables, Volumes, or Lakebase (PostgreSQL).
• DQX Studio — browser-based no-code UI for authoring, reviewing, running, and monitoring quality rules, deployed as a Databricks App.
• MCP server — Model Context Protocol server that exposes DQX as tools for AI agents (Claude, Genie, Cursor, and others), deployed as a Databricks App with on-behalf-of authentication so data access follows Unity Catalog permissions.
• Data format agnostic & streaming support — works with PySpark DataFrames and applies checks to both batch and Spark Structured Streaming (including Lakeflow Pipelines / DLT) using the same API.

📖 Documentation

The complete documentation is available at: https://databrickslabs.github.io/dqx/

🛠️ Contribution

Please see the contribution guidance here on how to contribute to the project (build, test, and submit a PR).

💬 Project Support

Please note that this project is provided for your exploration only and is not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS, and we do not make any guarantees. Please do not submit a support ticket relating to any issues arising from the use of this project.

Any issues discovered through the use of this project should be filed as GitHub Issues on this repository. They will be reviewed as time permits, but no formal SLAs for support exist.