Building Data Pipelines with Shell, Airflow, and Kafka

In today's data-driven world, the ability to move and transform data efficiently is a core skill for any developer or data professional. This text-based course guides you through the foundational concepts of ETL and ELT, showing you how to build reliable data pipelines from scratch. You will progress from understanding basic data integration theories to constructing functional pipelines. You will learn how to automate tasks with Shell scripting, orchestrate complex workflows using Airflow (including modern TaskFlow API patterns), and handle real-time data streams with Kafka. What you'll learn: - Understand the core differences between ETL and ELT architectures and when to apply each. - Write Shell scripts to automate basic data extraction, transformation, and loading tasks. - Orchestrate multi-step data workflows using Airflow DAGs and modern TaskFlow API conventions. - Configure Kafka to ingest, process, and stream real-time event data. - Apply modern pipeline practices, including containerized execution and basic data validation. The course begins with essential terminology and architectural concepts before moving into step-by-step written guides and configuration exercises. You will explore practical scenarios for batch processing and real-time streaming to build a well-rounded skill set. This course is designed for beginners to data engineering, requiring only basic programming familiarity and no prior pipeline experience. Start reading today to master the tools that power modern data infrastructure.