Spark and Databricks: Big Data ETL Fundamentals

Managing and transforming massive datasets is a core requirement for today's data professionals. As organizations generate more information than ever, understanding how to build scalable pipelines using Spark and Databricks has become an essential skill for data and machine learning engineers. This foundational text-based course guides you through the core concepts of distributed computing and modern data processing. You will transition from understanding basic big data principles to building a practical ETL (Extract, Transform, Load) pipeline. Through detailed written explanations and practical code snippets, you will gain the confidence to handle real-world data engineering challenges. What you'll learn: • Understand the foundational concepts of distributed data processing and cluster computing. • Navigate the Databricks workspace to write and execute robust data processing scripts. • Process large datasets using modern Spark DataFrame operations and SQL queries. • Build a complete ETL pipeline to extract, transform, and load data reliably. • Apply modern data lakehouse concepts, including a basic introduction to Delta Lake principles. • Practice data cleaning, validation, and transformation techniques through guided written exercises. The course begins with essential big data terminology and foundational definitions before moving into practical coding applications. You will progress step-by-step through reading materials, applying your new knowledge to construct a complete data pipeline from the ground up. Designed specifically for beginners, this course requires no prior experience with distributed systems or big data tools. Start reading today to build your foundational data engineering skills and process big data with confidence.