Building Data Lakes and Warehouses on the Cloud

Traditional data storage often forces a choice between the raw flexibility of data lakes and the structured power of data warehouses. Modern cloud architectures bridge this gap, allowing organizations to run high-performance analytics directly on scalable storage. This text-only course guides you through the foundational concepts of cloud data engineering, from basic storage definitions to designing a unified data lakehouse. You will understand how to organize, secure, and query massive datasets efficiently. What you'll learn: • Understand the core differences and use cases for data lakes, warehouses, and modern lakehouses. • Configure cloud storage buckets to ingest and organize raw structured and unstructured data. • Design optimized tables in BigQuery for high-performance analytics and cost-effective querying. • Apply modern data governance and zero-trust security principles to protect sensitive datasets. • Implement open table formats like Apache Iceberg to enable seamless multi-engine data access. • Practice writing efficient SQL queries to analyze data across hybrid storage layers. The course starts with essential terminology and structural foundations before moving into practical design patterns. You will read through step-by-step architectural guides and conceptual breakdowns that prepare you to build robust data pipelines. Designed for aspiring data engineers, database administrators, and tech enthusiasts, this course requires no prior cloud data experience, though a basic familiarity with SQL is helpful. Start building modern, scalable data architectures today.