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Google BigQuery Build a Modern Data Warehouse

Name: Google BigQuery Build a Modern Data Warehouse
Author: Baturi

Google BigQuery Build a Modern Data Warehouse

Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With Google BigQuery Build a Modern Data Warehouse, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 51m | Size: 1.52 GB
Build data warehouses in Google BigQuery using SQL, Python, ETL, advanced queries, and data governance.

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.

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 usingGoogle 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 practicalETL 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.

Homepage

https://www.udemy.com/course/google-bigquery-build-a-modern-data-warehouse/

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