Learn how to build, manage, and query efficient data warehouses using Google BigQuery, SQL, and Python. This course provides a practical introduction to BigQuery, helping you develop the skills needed to organize data, perform ETL operations, execute queries, and apply essential data governance practices in a cloud-based environment. You will begin by exploring the BigQuery platform, setting up your environment, navigating the user interface, and working with public datasets.

As you progress, you will learn how to create datasets and tables, define schemas, and apply partitioning and clustering strategies for efficient data storage. You will also create tables using SQL and explore how data warehouses can be built through both the BigQuery interface and Python.

The course also introduces practical ETL workflows using Python, including extracting data from CSV, Excel, and JSON files and loading it into BigQuery. You will explore SQL-based data retrieval, aggregate and window functions, User-Defined Functions (UDFs), and essential data security and governance practices.

Through hands-on lessons and practical applications, you will strengthen your ability to work with data in BigQuery and connect data warehousing concepts with real-world scenarios. By the end of the course, you will have a solid foundation for creating efficient data warehouses, querying information, and supporting informed, data-driven decision-making.