Demos

DQX Core

Import the following notebooks in the Databricks workspace to try DQX Core out:

Use as Library

• DQX Quick Start Demo Notebook - quickstart DQX usage as a library.
• DQX Intermediate Demo Notebook - intermediate-level DQX usage as a library.
• DQX Comprehensive Demo Notebook - comprehensive DQX usage as a library.
• DQX Demo Notebook for Data Quality Summary Metrics - demonstrates how to generate summary-level data quality metrics when validating data with DQX.
• DQX Demo Notebook for Actions and Alerting - demonstrates how to react to data quality problems by firing alerts (driver log, with optional Slack) when summary metrics cross a threshold.
• DQX Demo Notebook for Lineage Collection - demonstrates how CollectLineageAction captures upstream and downstream table lineage across a bronze → silver → gold star schema, alongside a driver-log alert on the injected data-quality failures.
• DQX Demo Notebook for Profiling and Applying Checks at Scale on Multiple Tables - demonstrates how to use DQX as a library at scale to apply checks on multiple tables.
• DQX Demo Notebook for Row Anomaly Detection - comprehensive demo showing how to use DQX Row Anomaly Detection to detect unusual patterns in your data.
• DQX Demo Notebook for AI-assisted checks generation - demonstrates how to generate DQX rules/checks with LLM using natural language.
• DQX Demo Notebook for Data Contract Integration (ODCS) - demonstrates how to generate DQX quality rules from ODCS (Open Data Contract Standard) data contracts, including predefined rules from schema constraints, explicit custom rules, and contract metadata tracking.
• DQX Demo Notebook for Spark Structured Streaming (Native End-to-End Approach) - demonstrates how to use DQX as a library with Spark Structured Streaming, using the built-in end-to-end method to handle both reading and writing.
• DQX Demo Notebook for Spark Structured Streaming (DIY Approach) - demonstrates how to use DQX as a library with Spark Structured Streaming, while handling reading and writing on your own outside DQX using Spark API.
• DQX Demo Notebook for Lakeflow Pipelines (formerly DLT) - demonstrates how to use DQX in a Lakeflow Pipeline, reporting issues as additional columns and computing data quality summary metrics.
• DQX Demo Notebook for Lakeflow Pipelines with Quarantine - demonstrates how to use DQX in a Lakeflow Pipeline to split valid and invalid records into valid and quarantine tables and compute summary metrics.
• DQX Demo Notebook for Lakeflow Pipelines with a foreachBatch sink - demonstrates how to use DQX in a Lakeflow Pipeline foreachBatch sink, reporting issues as additional columns and computing summary metrics incrementally.
• DQX Demo Notebook for Lakeflow Pipelines with a foreachBatch sink and Quarantine - demonstrates how to use DQX in a Lakeflow Pipeline foreachBatch sink to split valid and invalid records into separate tables in the same batch and compute summary metrics incrementally.
• DQX usage with Declarative Automation Bundles - demonstrates how to use DQX as a library with Declarative Automation Bundles (formerly Databricks Asset Bundles).
• DQX usage with dbt - demonstrates how to use DQX as a library with dbt projects.
• DQX Demo on Primary-Key detection - demonstrates how to detect primary keys and generate uniqueness rules.

Use as Workspace Tool

• DQX Demo Notebook - demonstrates how to use DQX as a tool when installed in the workspace.

Industry Accelerators

• DQX for Banking / FSI Industry Accelerator Demo - showcases DQX data quality rules tailored for the Banking and Financial Services industry, focusing on fraud detection, transaction structuring, and regulatory compliance.
• DQX for Fashion & Retail Accelerator Demo - demonstrates DQX rules tailored for the Fashion and Retail industry, including SKU validation, hex color formats, and pricing compliance.
• DQX for Manufacturing Industry Accelerator Demo - demonstrates how to use DQX to check data quality for Manufacturing Industry datasets, specifically for machine maintenance and sensor data.
• DQX for Customer 360 Industry Accelerator Demo - demonstrates DQX data quality rules for Customer 360 datasets, including customer identity, email format, revenue, ticket counts, purchase dates, and active customer flag consistency.

Use Cases

• DQX for PII Detection Notebook - demonstrates how to use DQX to check data for Personally Identifiable Information (PII).

Execution Environment

You don't have to run DQX from a Notebook. DQX can be run from any Python script as long as it runs on Databricks. For example, you can run it from a Databricks job by adding DQX as a dependent library. When DQX is installed in the workspace as a tool, it provides a suite of command-line tools for executing DQX jobs (see the User Guide).

DQX Studio

If you're using DQX Studio, the no-code web app, you can try the app built-in demo so you can explore every feature against realistic data before pointing it at your own tables. It creates a small set of sample tables, a library of reusable rules, monitored tables, collections, and past runs — enough to see scores, trends, failing records, and the approval workflow in action.

An admin deploys the demo from the Rule Marketplace — it's the Deploy demo content action at the top of the page. Deploying it sets up the sample catalog, seeds the rules and monitored tables, and generates a history of runs so the results views have something to show. Once it's in place you can:

• Browse the rules registry and see how rules are reused across tables.
• Open a monitored table and review its results — including failing records you can inspect.
• View a collection such as Customer 360 to see quality across related tables at once.
• Try Ask Genie on the demo's results.

It's the fastest way for a new steward or reviewer to get comfortable — nothing they do to the demo touches production data.

Admin only

Only the Admin role can deploy or remove the demo, since it creates sample data and app content. See Configure DQX Studio.