Designing Data Lakes and Warehouses with GCP

Organizations today generate massive volumes of data, but storing, managing, and analyzing this information efficiently remains a significant challenge. Traditional storage methods often create isolated silos, preventing teams from extracting timely and actionable insights. This text-based course guides you through the transition from legacy storage to modern cloud data architectures. You will understand how to combine the cost-effective flexibility of data lakes with the structured analytical power of data warehouses into a unified data lakehouse architecture on GCP. What you'll learn: - Understand the core differences between data lakes, data warehouses, and modern data lakehouses. - Configure scalable storage solutions using Cloud Storage and BigQuery for analytical workloads. - Apply modern data lakehouse patterns to unify structured and unstructured data streams. - Implement open table formats like Apache Iceberg to ensure high performance and open standards. - Establish foundational data governance and security policies to protect sensitive cloud data assets. The course begins with essential terminology and structural definitions before guiding you through architectural design principles and practical cloud configurations. You will reinforce your knowledge through written conceptual reviews and structural design exercises. This course is designed for beginner data engineers, cloud architects, and IT professionals who want to build a solid foundation in cloud data architecture with no prior cloud storage experience required. Start reading today to design modern, efficient, and scalable cloud data platforms.