Hands-On Data Engineering: Build Pipelines with Spark, Kafka, and Airflow

Modern businesses generate massive amounts of data daily, but this data is useless without robust pipelines to process, clean, and store it. This text-only course guides you through the core concepts of data engineering, taking you from raw data to production-ready pipelines. You will transition from a beginner to a confident practitioner capable of designing and automating both batch and stream-processing workflows. Through clear written explanations and practical code walkthroughs, you will master the fundamental tools used by modern data teams. What you'll learn: Understand the fundamental architecture of modern data platforms and storage systems; Build automated ETL pipelines using Airflow to schedule and monitor data workflows; Process large-scale datasets efficiently using PySpark and Pandas; Configure real-time streaming data pipelines with Kafka for instant data processing; Containerize your data applications using Docker to ensure consistent environments; Clean, parse, and validate incoming data to maintain high data quality standards. The course begins with foundational definitions of data engineering, databases, and containerization. You will then progress step-by-step through batch processing, stream processing, and pipeline orchestration using real-world scenarios. This course is designed for aspiring data engineers, software developers, and data analysts with a basic understanding of Python. No prior experience with big data tools or DevOps is required. Start reading today to build your first reliable data pipeline.