BigQuery Fundamentals for Data and ML Engineers

Modern data professionals need to process massive datasets and build predictive models without the complexity of managing infrastructure. BigQuery provides a serverless, highly scalable data warehouse that bridges the gap between data engineering and machine learning. This text-based course guides you from fundamental cloud data concepts to executing advanced analytics and deploying machine learning models using standard SQL. You will learn how to structure data, optimize query costs, and harness the built-in machine learning capabilities of BigQuery to turn raw data into actionable insights. Through clear written explanations and practical code examples, you will master the workflows required to manage and analyze data at scale. What you'll learn: - Understand core BigQuery architecture, storage structures, and foundational cloud data concepts. - Configure partitioned and clustered tables to optimize query performance and control costs. - Build robust data pipelines by loading, transforming, and exporting large datasets. - Create, evaluate, and deploy predictive models directly inside the data warehouse using BigQuery ML. - Apply modern analytics engineering practices, including schema design and query optimization. The course begins with essential terminology and basic cloud data warehousing concepts before progressing to hands-on query writing, data pipeline construction, and SQL-based machine learning workflows. This course is designed for aspiring data engineers, analysts, and machine learning enthusiasts who want to build a solid foundation in cloud-based data warehousing. No prior BigQuery experience is required, though a basic familiarity with SQL is helpful. Start reading today to unlock the power of modern cloud data warehousing and built-in machine learning.