Implementing data quality and validation rules with Delta Live Tables in Databricks

One of the key features of Delta Live Tables is the ability to define and enforce data quality rules on your datasets using expectations. You can add optional clauses called expectations to your dataset declarations that check the quality of each record in a query. You can specify what action to take when a record fails an expectation, such as warning, dropping, failing, or quarantining the record.

In this recipe, you will learn how to use Delta Live Tables expectations to implement data quality and validation rules in your data pipelines. You will also learn how to monitor and troubleshoot your pipelines using the Delta Live Tables event log and UI.

How to do it…

1. Create a live table for customer data: We are defining a live table to create a materialized view from the samples.tpch.customer Delta table. We can now define some data expectation rules. The CONSTRAINT valid_customer_key...