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Mastering Data Engineering with BigQuery
Paperback
ISBN13: 9789349887718
Publisher: Orange Education Pvt Ltd
Published: Mar 16 2026
Pages: 442
Weight: 1.66
Height: 0.90 Width: 7.50 Depth: 9.25
Language: English
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.
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.
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
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