BigQuery Fundamentals for Redshift Professionals

Transitioning from a cluster-based data warehouse to a serverless architecture can feel like a major shift. If you already understand Redshift, learning BigQuery is the fastest way to expand your modern data engineering toolkit. This text-based course bridges the gap between the two platforms, mapping familiar Redshift concepts like nodes, distribution keys, and vacuuming to BigQuery's serverless storage, slot allocation, and automatic optimization. You will learn to write efficient queries, manage datasets, and optimize costs without managing infrastructure. What you'll learn: - Compare Redshift and BigQuery architectures, mapping clusters to serverless slots. - Understand storage differences, translating distribution and sort keys to partitioning and clustering. - Write and optimize BigQuery SQL queries using modern standard SQL conventions. - Manage data ingestion, loading external datasets, and querying data lakes directly. - Configure access control, datasets, and table security policies. - Analyze query execution plans to control costs and optimize performance. The course begins with foundational architectural comparisons before guiding you through data loading, query writing, and cost management practices. You will progress from basic terminology mapping to executing analytical queries on large datasets. This course is designed for data analysts, database administrators, and data engineers familiar with Redshift who are new to BigQuery. No prior BigQuery experience is required. Start your transition to serverless data warehousing today.