Data Engineering Foundations: Data Lakes and Warehouses on Google Cloud

In the era of big data, organizations rely on robust storage architectures to turn raw information into actionable insights. Understanding when to use a data lake versus a data warehouse is the first critical step to building successful cloud data pipelines. This text-based course guides you through the core principles of data engineering in a cloud environment. You will learn how to evaluate business use cases, select the right storage solutions on Google Cloud, and design architectures that balance cost, speed, and scalability. What you'll learn: - Understand the distinct roles, benefits, and trade-offs of data lakes and data warehouses. - Explore core Google Cloud storage services, including Cloud Storage and BigQuery, for structured and unstructured data. - Design modern lakehouse architectures that combine the flexibility of lakes with the analytical power of warehouses. - Apply fundamental data governance, security, and access control best practices to protect cloud data assets. - Analyze how data engineering pipelines optimize business operations and support downstream analytics. The journey begins with foundational terminology and the core responsibilities of a data engineer before moving into technical deep dives of cloud storage services and pipeline design. You will read conceptual explanations and review architectural design patterns to solidify your understanding. This course is designed for aspiring data engineers, cloud architects, and business analysts who are new to data engineering and want to build a strong foundational knowledge of cloud storage solutions without needing prior cloud experience. Start reading today to build a solid foundation in modern cloud data architecture.