Implement and manage data quality constraints with Azure Databricks

Module
8 Units

Intermediate

Data Engineer

Azure Databricks

This module explores strategies for maintaining high data quality in Azure Databricks. You will learn how to implement validation checks, enforce schemas, manage schema drift, and use pipeline expectations to ensure data integrity throughout your data pipelines.

Learning objectives

By the end of this module, you'll be able to:

Implement validation checks for nullability, cardinality, and range constraints
Implement data type checks using schema enforcement and explicit casting
Enforce schema and manage schema drift using Auto Loader and Delta Lake
Manage data quality using pipeline expectations in Lakeflow Spark Declarative Pipelines

Prerequisites

The following prerequisites should be completed:

Basic understanding of Azure Databricks workspaces and Unity Catalog
Familiarity with SQL and data engineering concepts

Introduction min
Implement validation checks min
Implement data type checks min
Detect and manage schema drift min
Manage data quality with pipeline expectations min
Exercise - Implement and Manage Data Quality Constraints with Azure Databricks min
Module assessment min
Summary min

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