Mastering Data Engineering with BigQuery

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!

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

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.

About the Technical Reviewer
Dr. Sankaraiah Sreeramula is the Founder and Chief Executive Officer at Turilytix. AI spearheads transformative efforts in business efficiency through cutting-edge advancements in machine learning, artificial intelligence, automated analysis, and visual intelligence. With a career spanning over 18 years, Dr. Sankaraiah has developed AI-driven solutions across a diverse array of industries, including FMCG, retail, finance, and manufacturing, collaborating with some of the world’s leading companies in semiconductors, pulp, and paper. Recognized globally for his innovative contributions, he was honored as the innovative leader in 2023, the top innovative leader driving Generative AI, and a global inspirational leader in 2024.

Dr. S. Neelavathy Pari is an Assistant Professor (Senior Grade) in the Department of Computer Technology, Anna University, Chennai. She specializes in the security of Wireless Networks, Cloud Computing, Data Structures and Algorithms, Soft Computing, and Emerging Computer Technologies. With over 20 years of academic and research experience, she has contributed extensively to curriculum development, interdisciplinary research, and technology-driven education.

Vatsal Mishra is an accomplished Data Engineer with over 9 years of experience specializing in data pipeline architecture, GenAI applications, and software development. With a strong foundation in Electronics and Communications Engineering from the Indian Institute of Information Technology, he has developed expertise in Python, Apache Spark, Kafka, Java, and cloud data technologies, including GCP, Snowflake, and AWS. Vatsal has a proven track record in leading data engineering initiatives at prominent organizations, including Google and Urban Company, where he designed scalable data platforms.