Data Engineering Foundations with Spark, Databricks, and Delta Lake

Modern businesses rely on robust data pipelines to turn raw data into actionable insights, making data engineering one of the most critical skills today. This course introduces you to the core concepts of the lakehouse architecture, giving you a solid foundation in modern data processing. You will transition from understanding basic data concepts to reading, writing, and executing data pipelines. Through clear written explanations and structured code examples in Python and Scala, you will learn how to process large-scale datasets, manage reliable data tables, and implement industry-standard data workflows. What you'll learn: - Understand the foundational principles of the Databricks Lakehouse architecture and distributed computing with Apache Spark. - Build reliable data pipelines using Spark SQL, DataFrames, and APIs in both Python and Scala. - Manage Delta Tables using advanced features like time travel, version history, and schema evolution. - Optimize query performance using Delta caching, file management, and modern storage layouts. - Configure data governance and file storage basics using Unity Catalog volumes. - Apply data pipeline testing and monitoring practices to ensure data quality and pipeline reliability. The journey begins with essential data engineering terminology and Spark setup before moving systematically through DataFrame transformations, data loading, Delta Lake operations, and performance tuning. You will read through comprehensive code walk-throughs and practice with conceptual exercises designed to reinforce your learning. This course is designed for aspiring data engineers, database administrators, and software developers who are new to big data technologies. No prior experience with Spark or Databricks is required, though a basic familiarity with SQL and general programming concepts is helpful. Start building your data engineering foundation today.