Data Engineering Fundamentals: Building ETL Pipelines with Hadoop

In today's data-driven world, organizations rely on clean, accessible, and structured data to make critical decisions. This text-based course guides you through the foundational concepts of data engineering, showing you how to manage and move data at scale. You will transition from understanding basic data concepts to designing and executing your own Extract, Transform, Load (ETL) pipelines. By mastering core big data frameworks and modern storage practices, you will gain the skills needed to process massive datasets and prepare them for downstream analysis. What you'll learn: Understand core data engineering terminology, including batch versus real-time processing and pipeline architecture; Extract raw data from diverse sources and clean it using modern transformation techniques; Load structured data efficiently into data warehouses, data lakes, and distributed storage systems; Configure Hadoop and MapReduce concepts to process large-scale datasets across distributed environments; Apply modern storage formats like Parquet and Delta Lake to optimize pipeline performance; Design automated pipeline workflows to ensure consistent data delivery and quality control. The course starts with essential definitions and architecture principles before moving into step-by-step written tutorials on data extraction, transformation, and loading. You will then explore distributed computing with Hadoop and modern storage strategies through practical written exercises. This course is designed for beginners, aspiring data engineers, and analysts looking to understand the backend of data systems. No prior data engineering experience or complex programming background is required. Start your journey into the world of big data and build your first data pipeline today.