Data Engineering Fundamentals: Batch and Streaming Pipelines

The volume of data generated today requires robust systems to process it efficiently. Modern data engineering bridges the gap between raw data and actionable insights, making it a critical skill for any data professional. In this course, you will learn how to build complete data pipelines from scratch. You will explore foundational concepts, modern orchestration techniques, and the fundamental differences between batch and streaming data processing. By following written explanations and practical code snippets, you will develop the skills to move, transform, and ensure the quality of large datasets. What you'll learn: - Understand foundational data engineering terminology, architectures, and the modern data lifecycle. - Build robust batch processing pipelines using Python and SQL. - Explore real-time streaming concepts and event-driven architectures. - Practice modern data transformation techniques and data modeling fundamentals. - Apply modern orchestration patterns to schedule, manage, and monitor your data workflows. - Implement basic testing and data quality checks to ensure reliable pipelines. The course begins with essential terminology and architecture concepts before moving into practical, text-based exercises. You will progressively read through structured lessons to understand how different components of a data infrastructure fit together. Designed for beginners, aspiring data engineers, and Python developers looking to transition into data engineering, no prior pipeline experience is required. Start reading today to build the foundation for your modern data engineering journey.