Implementing Data Quality Constraints in Azure Databricks

In modern data engineering, poor data quality can quickly ruin downstream analytics and machine learning models. This course shows you how to build robust guardrails directly into your data lakehouse using Azure Databricks. You will transition from manually checking data to automatically enforcing strict quality standards. Through clear written explanations and practical code examples, you will learn to configure schema enforcement, apply validation rules, and monitor data health across your pipelines. What you'll learn: Understand foundational data quality concepts and the role of Unity Catalog in data governance; Configure schema enforcement and evolution to prevent corrupt data from entering your tables; Implement table constraints and validation rules to maintain high-integrity datasets; Define pipeline expectations within Delta Live Tables for real-time quality monitoring; Apply modern observability patterns to track and alert on data quality metrics. The course begins with essential terminology and setup basics before guiding you through hands-on configuration of constraints, validation rules, and automated pipeline expectations. This training is designed for beginner data engineers, analysts, and database administrators looking to secure their data pipelines, with no prior Databricks administration experience required. Start building reliable, self-validating data pipelines today.