Data Engineering with Databricks and Delta Lake

Modern organizations require robust data infrastructure to turn raw data into actionable insights, but traditional data warehouses and data lakes often create silos. This course teaches you how to bridge that gap using the unified lakehouse paradigm, equipping you with the practical skills needed to design, build, and maintain scalable data systems. You will transition from understanding core data storage concepts to constructing fully operational, automated data pipelines. What you will learn: Understand the fundamentals of lakehouse architecture and how Databricks unifies data warehousing and data lakes; Write efficient data transformations using Apache Spark and structured APIs; Configure Delta Lake tables to ensure ACID transactions, schema enforcement, and reliable data versioning; Design robust ETL pipelines that ingest, clean, and refine raw data into structured gold-standard datasets; Apply modern data engineering practices including pipeline orchestration and basic performance optimization. This text-based course guides you step-by-step through foundational concepts, hands-on code examples, and architectural patterns. It is designed specifically for beginners, data analysts, and software developers looking to transition into data engineering, with no prior Databricks experience required. Start reading today to build a strong foundation in modern data architecture.