Demos

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

Use as Library

• DQX Quick Start Demo Notebook - quickstart on how to use DQX as a library.
• DQX Demo Notebook - demonstrates how to use DQX as a library.
• 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 as a library with Lakeflow Pipelines.
• DQX Asset Bundles Demo - demonstrates how to use DQX as a library with Databricks Asset Bundles.
• DQX Demo for dbt - demonstrates how to use DQX as a library with dbt projects.

Deploy as Workspace Tool

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

Use Cases

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

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).