Introduction to Data Engineering on Google Cloud
Learn data engineering fundamentals on Google Cloud. Master data pipelines, automation, and core Google Cloud components.
Course Cost
₹ 2,699
Beginner
Skill Level
9 Hours
Self-paced lessons
This comprehensive course introduces data engineering on Google Cloud, covering the roles and responsibilities of data engineers and how they align with Google Cloud offerings. You'll learn about data engineering tasks, core components, and various data pipeline patterns including extract-load, extract-load-transform (ELT), and extract-transform-load (ETL). The course also covers data replication, migration, and automation techniques using Google Cloud tools and services. Through hands-on labs and quizzes, you'll gain practical experience with BigQuery, Datastream, BigLake, Dataform, Dataproc, and Cloud Run, preparing you to address real-world data engineering challenges.
What you'll learn
Understand the role of a data engineer
Identify data engineering tasks and core components used on Google Cloud
Create and deploy data pipelines of varying patterns on Google Cloud
Utilize various automation techniques on Google Cloud
Master key Google Cloud tools for data engineering (BigQuery, Datastream, BigLake, etc.)
Learn data replication and migration strategies
Understand different data pipeline patterns (ELT, ETL)
Gain hands-on experience through practical labs and exercises
Skills you'll gain
This course includes:
61 Minutes PreRecorded video
6 quizzes, 6 hands-on labs
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate

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There are 8 modules in this course
This course provides a comprehensive introduction to data engineering on Google Cloud. It covers the fundamental concepts, tools, and techniques used by data engineers to manage and process data at scale. The curriculum is structured into modules that progressively build knowledge, starting with the basics of data engineering tasks and components, and advancing to complex topics like data pipeline patterns and automation techniques. Throughout the course, students will learn about various Google Cloud services and how they fit into the data engineering ecosystem. Hands-on labs provide practical experience with key tools like BigQuery, Datastream, BigLake, and Dataform. By the end of the course, learners will have a solid foundation in data engineering principles and practices, specifically tailored to the Google Cloud environment.
Course Introduction
Module 1 · 2 Minutes to complete
Data Engineering Tasks and Components
Module 2 · 2 Hours to complete
Data Replication and Migration
Module 3 · 1 Hours to complete
The Extract and Load Data Pipeline Pattern
Module 4 · 1 Hours to complete
The Extract, Load, and Transform Data Pipeline Pattern
Module 5 · 1 Hours to complete
The Extract, Transform, and Load Data Pipeline Pattern
Module 6 · 2 Hours to complete
Automation Techniques
Module 7 · 1 Hours to complete
Course Summary
Module 8 · 10 Minutes to complete
Fee Structure
Payment options
Financial Aid
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
Frequently asked Questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.




