Your guide to building intelligent, cloud-ready data pipelines.
Key Features
? Get a free one-month digital subscription to www.avaskillshelf.com
? Master end-to-end data engineering on Google Cloud, from ingestion to AI.
? Build hands-on pipelines using BigQuery, Dataflow, Dataproc, and Pub/Sub.
? Production-ready design covering performance, security, and governance.
Book Description
BigQuery sits at the core of modern cloud data platforms, enabling you to analyze massive datasets with speed, scalability, and simplicity. Mastering Data Engineering with BigQuery guides you through the complete lifecycle of cloud-native data systems on Google Cloud Platform—from data ingestion and storage to processing, orchestration, analytics, and machine learning—using BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Composer.
You will learn how to design scalable data pipelines, efficiently manage and query large datasets, optimize BigQuery performance, automate workflows, and apply machine learning directly within BigQuery. Throughout the book, core chapters focus on real-world architectures, production-ready patterns, and cost-efficient strategies that reflect how modern enterprises build and operate data platforms at scale.
Thus, whether you are a data engineer, cloud engineer, analyst, architect, or developer, this book equips you with the practical skills needed to succeed in data-driven roles.
What you will learn
? Design scalable, cloud-native data architectures on Google Cloud.
? Build batch and streaming pipelines using Dataflow and Dataproc.
? Store, query, and optimize data efficiently with BigQuery.
? Orchestrate and automate workflows using Cloud Composer.
? Apply BigQuery ML for integrated analytics and machine learning.
? Secure data platforms with GCP security and compliance controls.
Who is This Book For?
This book is ideal for data engineers, cloud engineers, analysts, machine learning engineers, and solution architects building scalable data systems on Google Cloud as well as IT professionals transitioning into cloud data engineering roles. Readers should have a basic programming knowledge (Python or SQL preferred) as prior cloud or data experience is helpful, but not a necessity!
Table of Contents
1. Introduction to Data Engineering on Google Cloud
2. Google Cloud Platform Essentials
3. Data Storage on GCP
4. Processing Data with Cloud Dataproc
5. Data Pipelines with Dataflow
6. Orchestrating Workflows with Cloud Composer
7. Analytics with BigQuery
8. Managing Data Integration with Cloud Pub/Sub
9. BigQuery Machine Learning
10. BigQuery Performance Optimization
11. Data Security and Compliance on GCP
Index
About the Author
Shanthababu Pandian is a technology leader with more than 24 years of experience in AI, ML, GenAI, and Data Science. A PhD scholar in AI, author, and global speaker, he has led impactful data and AI initiatives across healthcare, finance, and retail while mentoring the next generation of AI professionals.
