Google BigQuery for Data and ML Engineers

Google BigQuery for Data and ML Engineers

Instructors: Pearson

Access provided by Interbank

1 module

Gain insight into a topic and learn the fundamentals.

Intermediate 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.

Intermediate level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Master BigQuery’s serverless architecture, data warehousing, and advanced SQL to efficiently manage and analyze large datasets.
Learn to build, evaluate, and deploy machine learning models directly within BigQuery, covering essential techniques.
Explore the cutting-edge applications of generative AI in BigQuery.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

There is 1 module in this course

Learn how to use Google BigQuery to enhance your data engineering and machine learning skills in this practical, instructor-led course. Taught by experienced cloud architect and author Dan Sullivan, you’ll work with BigQuery’s serverless architecture, advanced SQL, and data warehousing features to efficiently manage and analyze large datasets.

This course is suitable for both beginners and those with experience. You’ll get hands-on practice with data ingestion, transformation, and building reliable data pipelines. The curriculum covers how to create, evaluate, and deploy machine learning models within BigQuery, as well as recent generative AI applications. Through real-world projects and clear instruction, you’ll build the skills needed to use BigQuery in your day-to-day work. Whether you’re new to the field or looking to expand your knowledge, this course offers practical tools and techniques for data engineering and machine learning.

This module provides a comprehensive overview of BigQuery for data engineering and machine learning. It covers BigQuery’s architecture, data ingestion (batch and streaming), data quality checks, and data exploration using SQL, Python, and Spark. The module then explores machine learning workflows, including classification, regression, and time series modeling directly in BigQuery. It concludes with practical applications of generative AI and text processing in BigQuery, featuring hands-on labs throughout to reinforce learning.