Exercise - Implement and Manage Data Quality Constraints with Azure Databricks
Now it's your chance to implement and manage data quality constraints with Azure Databricks. In this lab, you build a Lakeflow Spark Declarative Pipeline for ClearCover Insurance that enforces data quality constraints on raw claims data. You implement nullability and range checks using pipeline expectations, validate data types with col().cast(), and handle schema drift using Auto Loader's rescued data column. You then create and run the pipeline in the Databricks UI and monitor data quality metrics.
Note
To complete this lab, you need an Azure subscription in which you have administrative access.
Launch the exercise and follow the instructions.
