Data Engineering: Local Stack Setup with Spark and Airflow

Mastering data engineering requires understanding how to combine disparate tools—like data warehouses, processors, and orchestrators—into one cohesive, production-ready pipeline. This course provides the practical, hands-on knowledge necessary to build and manage a functioning data platform from the ground up. By the end of this course, you will master the practical steps of setting up a local cluster environment and implementing the fundamental data layering strategy (RAW, STG, CORE) used by professional data teams. What you'll learn: * Learn the architecture and function of core data engineering tools including Spark, Airflow, Postgres, and Jupyter. * Configure a unified local development environment using containerization concepts for reproducible stack setup. * Apply data transformation logic using Spark to structure data into intermediate (STG) and final (CORE) layers. * Practice orchestrating complex data workflows using Airflow to ensure reliable, scheduled execution. * Understand and implement foundational data quality and governance checks within the pipeline. * Design and populate analytical tables in a relational data warehouse (Postgres). This course begins with foundational concepts and definitions, then guides you step-by-step through setting up the cluster and executing increasingly complex data ingestion and transformation exercises. The material focuses on reading and applying written explanations and code snippets to build your practical skills. This course is perfect for absolute beginners or developers looking to transition into a data engineering role. No prior experience with these specific tools is required. Start building your essential data engineering skills today.