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Google BigQuery: Build a Modern Data Warehouse
New
Rating: 4.8 out of 5(8 ratings)
441 students

Google BigQuery: Build a Modern Data Warehouse

Build data warehouses in Google BigQuery using SQL, Python, ETL, advanced queries, and data governance.
Created byStarweaver Group, Paul Siegel
Last updated 10/2026
English
ArabicGerman

What you'll learn

  • Learn the ins and outs of BigQuery's interface, set up your environment, and access datasets with ease.
  • Create and organize datasets, define tables, and utilize partitioning and clustering for efficient data storage.
  • Build dynamic ETL pipelines using Python, replace and append tables, and configure schema, partitioning, and clustering.
  • Understand querying with SQL, exploring aggregate and window functions, and understanding data security and governance.

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Course content

6 sections • 30 lectures • 1h 52m total length

  • Introduction to the Instructor3:21

    Meet your instructor and get introduced to the course, its structure, key topics, and learning journey.

  • Introduction to BigQuery1:48

    Learn the fundamentals of Google BigQuery and its capabilities.

  • Setting Up and Accessing BigQuery3:15

    Learn how to set up your environment and access BigQuery.

  • Navigating the BigQuery UI4:02

    Explore the BigQuery interface and its key areas.

  • Exploring "Bigquery-Public-Data" Datasets2:14

    Discover how to access and explore public datasets in BigQuery.

  • Additional Resources0:26
  • Practice Quiz: Understanding BigQuery Platform

Requirements

  • Basic SQL knowledge, familiarity with cloud platforms & programming skills preferably in Python for building data warehouses using BigQuery

Description

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.

Who this course is for:

  • Aspiring Data Analysts looking to build practical skills in BigQuery and data warehousing.
  • Data Engineers interested in creating datasets, tables, and efficient data warehouse structures.
  • Business Intelligence Professionals who want to query and analyze data using SQL and BigQuery.
  • Developers and Technical Professionals who want to perform ETL workflows using Python.
  • Data Professionals interested in learning data security, governance, partitioning, and clustering.

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