Data Modeling and Ingestion with Azure Databricks

Transitioning to a modern lakehouse architecture requires a solid understanding of how data is structured, stored, and managed. This comprehensive written course guides you through the core principles of data modeling within Azure Databricks, helping you build high-performance data platforms. You will progress from understanding foundational data lakehouse concepts to designing efficient storage structures. By reading through clear explanations and practical code examples, you will learn how to optimize query performance, manage data schemas, and ensure robust data governance. What you'll learn: 1. Understand the fundamentals of lakehouse architecture, Delta Lake tables, and Unity Catalog governance. 2. Design efficient table structures using partitioning, liquid clustering, and indexing strategies. 3. Implement data ingestion patterns to load batch and streaming data smoothly. 4. Manage slowly changing dimensions to track historical data changes over time. 5. Apply data optimization techniques like file compaction and z-ordering for faster queries. 6. Configure security and access controls using modern data governance practices. The course begins with essential terminology and the structural foundations of modern data lakes. You will then explore step-by-step written guides on schema design, performance tuning, and data lifecycle management. This course is designed for beginner data engineers, database administrators, and analysts looking to transition to modern cloud data platforms. No prior experience with Databricks is required, though a basic familiarity with SQL concepts is helpful. Start reading today to master the essentials of cloud data modeling and build faster, more reliable data pipelines.