Fundamentals of Data Pipelines and Lineage for AWS Data Engineering

In modern data engineering, building a pipeline is only half the battle. To ensure data quality, compliance, and cost-efficiency, you must understand how data is stored, structured, and tracked as it moves through your cloud environment. This written course equips you with the foundational knowledge required to design reliable data architectures and prepare for cloud data engineering challenges. You will transition from understanding raw data characteristics to implementing structured, searchable, and fully auditable data systems on AWS. Through clear, text-based explanations and architectural walkthroughs, you will learn how to optimize storage costs and trace data origin with confidence. What you'll learn: - Understand the core properties of big data, including volume, velocity, and variety. - Analyze modern storage formats like Parquet and implement efficient partitioning strategies. - Configure metadata schemas and centralize data discovery using the Glue Data Catalog. - Track data lineage and machine learning artifacts using SageMaker ML Lineage Tracking. - Apply zero-trust data governance and access control concepts to your cloud pipelines. - Design cost-effective, scalable data storage and retrieval workflows on AWS. This course begins with fundamental data concepts and storage types before guiding you through metadata management, cataloging, and lineage tracking techniques. You will explore practical design patterns that ensure your data remains discoverable, traceable, and secure. This course is designed for beginner data engineers, cloud architects, and those preparing for AWS data exams who want a solid grasp of data management principles without needing prior cloud administration experience. Start reading today to build transparent, secure, and optimized data pipelines.