Designing Modern Data Warehouses and Lakehouses

In today's data-driven world, organizations rely on robust systems to store, process, and analyze massive volumes of information. Understanding how to design these systems is the first step toward building a successful career in data engineering. This text-based course guides you through the core concepts of data storage architecture, helping you transition from basic databases to advanced enterprise data platforms. You will gain the conceptual clarity and practical understanding needed to evaluate, design, and manage modern data infrastructure. What you'll learn: - Understand the core differences between Data Warehouses, Data Lakes, and modern Lakehouse architectures - Learn foundational data modeling techniques including star schemas, snowflake schemas, and Data Vault basics - Explore data ingestion and processing patterns using ETL and ELT pipelines - Discover modern open table formats like Delta Lake and Apache Iceberg that power the Lakehouse paradigm - Apply data quality checks and basic observability principles to ensure data reliability - Analyze how data storage structures support downstream analytics and business intelligence The course begins with essential terminology, historical context, and foundational storage concepts before moving into architectural design patterns, pipeline construction, and modern data governance practices. It is designed for absolute beginners, aspiring data engineers, and data analysts who want to understand modern data infrastructure without any prior engineering experience. Start reading today to build a strong foundation in modern data architecture.