Why use Google BigQuery for data warehousing?

Google BigQuery enables organizations to build scalable, cloud-based enterprise data warehouse solutions with fast query performance, advanced analytics, and simplified data management.

What will I get if I purchase the Certificate?

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Build a Data Warehouse Using BigQuery

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Build a Data Warehouse Using BigQuery

Q: What is a data warehouse?

A data warehouse is a centralized system designed to store, organize, and analyze large volumes of structured data for reporting, analytics, and business intelligence.

Instructors: Caio Avelino

Included with Learn more

1 module

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Create efficient data warehouses using BigQuery and optimize tables using Google BigQuery UI and SQL.
Build dynamic ETL pipelines with Python while applying partitioning and clustering strategies for efficient enterprise data warehouse performance.
Perform advanced queries using aggregate and window functions to generate insights from large-scale data warehouse with BigQuery environments.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 assignments¹

AI Graded see disclaimer

Taught in English

There is 1 module in this course

This course provides a practical, hands-on introduction to Google BigQuery for building scalable data warehouses using BigQuery and performing advanced cloud-based analytics. Learners will explore what is a data warehouse, how modern enterprise data warehouse systems operate, and how to efficiently manage large datasets using Google BigQuery.

Through real-world exercises and guided demonstrations, participants will learn how to set up and access BigQuery, build a data warehouse with BigQuery using both the web interface and Python, and apply optimization techniques to improve query performance, scalability, and cost efficiency in cloud environments. Designed for data analysts, data engineers, and business intelligence professionals, this Data warehouse using BigQuery course is also valuable for aspiring analysts seeking to strengthen SQL expertise and gain practical experience with modern data warehouse architectures and enterprise-scale data management. Learners will develop hands-on skills in querying, transforming, and analyzing large datasets to support data-driven decision-making and business intelligence initiatives. A basic understanding of SQL and foundational data concepts is recommended. By the end of the course, participants will be able to design scalable data warehouse with BigQuery solutions, execute complex analytical queries, and extract actionable insights using the full capabilities of Google BigQuery.

Unlock the power of Google BigQuery as you embark on a journey to understand the basic data warehouse builder and query expert. In this comprehensive course, you'll learn to harness the capabilities of BigQuery, from setting up and accessing the platform to creating data warehouses using both UI and Python. Through hands-on lessons and practical applications, you'll develop the basic skills needed to manage, query, and optimize your data in this powerful cloud-based platform.

What's included

24 videos6 readings6 assignments

24 videosTotal 112 minutes

Introduction to the Instructor3 minutes
Introduction to BigQuery 2 minutes
Setting Up and Accessing BigQuery 3 minutes
Navigating the BigQuery UI4 minutes
Exploring "BigQuery-Public-Data" Datasets 3 minutes
Introduction to Data Warehousing and BigQuery 4 minutes
Creating Datasets for Data Organization 5 minutes
Defining Tables with Schema and Settings 5 minutes
Partitioning and Clustering Strategies 5 minutes
Creating Tables Using SQL 7 minutes
Creating Partitioned Tables Using SQL 2 minutes
Installing Anaconda Package and Configuring Credentials 4 minutes
Introduction to Jupyter Notebook UI 3 minutes
Manipulating Data with Pandas: Replacing and Appending Tables 9 minutes
Adding Schema Information Dynamically 7 minutes
Incorporating Partitioning and Clustering Configurations 6 minutes
Understanding ETL (Extract, Transform, Load) 2 minutes
Performing ETL with CSV, Excel, and JSON files 13 minutes
Introduction to Data Query Language 2 minutes
Exploring SQL Examples for Data Retrieval 7 minutes
Leveraging Aggregate and Window Functions for Advanced Queries 6 minutes
User-Defined Functions (UDFs) for Custom Transformations 5 minutes
Data Security and Governance Best Practice3 minutes
Conclusion2 minutes

6 readingsTotal 55 minutes

Welcome to the Course: Course Overview5 minutes
Additional Resources10 minutes
Additional Resources10 minutes
Additional resources10 minutes
Additional resources10 minutes
Additional resources10 minutes

6 assignmentsTotal 190 minutes

Understanding BigQuery platform30 minutes
Data Warehouse Using UI Interface And SQL30 minutes
Creating A Data Warehouse Using Python30 minutes
ETL With Python From External Sources30 minutes
Querying And Governance30 minutes
Final Assessment40 minutes

Instructors

Caio Avelino

STARWEAVER

9 Courses9,664 learners

Offered by

STARWEAVER

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Frequently asked questions

This course teaches how to build scalable data warehouses using BigQuery, optimize queries, create ETL pipelines with Python, and analyze large datasets using Google BigQuery.

This course is ideal for data analysts, data engineers, business intelligence professionals, and aspiring analysts interested in modern enterprise data warehouse solutions.

Basic SQL knowledge, familiarity with cloud platforms, and foundational Python programming skills are recommended for working with data warehouse with BigQuery environments.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.

Build a Data Warehouse Using BigQuery

Build a Data Warehouse Using BigQuery

What you'll learn

Skills you'll gain

Tools you'll learn

Details to know

There is 1 module in this course

Build a Data Warehouse Using BigQuery

What's included

Instructors

Offered by

Explore more from Data Analysis

BigQuery Fundamentals for Teradata Professionals

BigQuery Fundamentals for Redshift Professionals

BigQuery Fundamentals for Oracle Professionals

BigQuery Fundamentals for Snowflake Professionals

Why people choose Coursera for their career

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.

Break down barriers to learning with big savings

Start with easy savings for hard-working teams

Frequently asked questions

What will I learn in this Data Warehouse using BigQuery course?

Who should enroll in this course?

What skills are recommended before taking this course?

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