Modern Data Architecture on Google Cloud: Master the Art of Building High-Performance Data and AI Platforms on GCP for Enterprises

by Gopal Sahu

Overview

Modernize on-premise systems and build data systems from scratch that support data intensive analytics on Google Cloud Platform with this guide.

Key Features

• Learn to take advantage of GCP features to build modern data systems
• Enhance your design decisions for modernizing on-premise systems on Google Cloud
• Build a solid foundation of various data architectures like Data Lake, Data Mesh, Data Lakehouse and implement them using GCP

Book Description

Data is a key asset for organizations in this era of digitization. Harnessing the insights from data is a key challenge as data is generated at high speed and volume. Use cases like advanced analytics, real-time analytics, and data science applications need data to be gathered and processed at scale. With the flexibility and advanced features of the public cloud, organizations are moving their existing data systems to the cloud or building future-ready data platforms. GCP is one of the leading cloud providers with a huge catalog of data services. GCP uses the same infrastructure that powers Google Search, YouTube, Gmail, etc. This book is a field guide that helps you build modern platforms on GCP. The book starts by going deep into the fundamentals of data architectures, then takes a hands-on approach to create a unified design for future-ready data platforms, and concludes with best practices for scalability and cost. This book offers a balanced approach of theoretical concepts and hands-on exercises to help readers learn to make decisions for selecting cloud services and technologies for architecture design and implementation. By the end of the book you'll be able to build Data Architectures using GCP and scale them by Implementing systems for data analysis, visualisation, AI & ML

What you will learn

• Learn how to build Data platforms from scratch on GCP
• Gain deeper understanding of various data architectures like Data Warehouse, Data Lake, Data Vault, Data Mesh & Data Lakehouse
• Take decisions on choosing various services and technologies for the data platform
• Implement enterprise level features of a data platform like data quality, data governance and compliance
• Build complex DevOps and MLOps systems on GCP
• Implementing data observability, logging, monitoring for the overall system
• Learn best practices for various services and optimizing for cost on GCP

Who this book is for

If you are a data architect who wants to gain expertise in building enterprise ready data architectures on GCP, this book is an excellent handbook with hands-on examples for building it from scratch as well as take decisions while modernizing an on-premise data system onto Google Cloud.

Product Details

ISBN-13:	9781801078139
Publisher:	Packt Publishing
Publication date:	02/20/2026
Pages:	429
Product dimensions:	75.00(w) x 92.50(h) x (d)

About the Author

Gopal Sahu is a Strategic Cloud Engineer (Cloud Data Architect) at Google with a decade of experience. He is known for designing datawarehouse and data lake architectures on GCP, modernizing data platforms for Fortune 5 and major e-commerce companies, and building EDW on MPP Database for a Fortune 50 organization. Gopal holds an M.Tech. in Data Science & Engineering from BITS Pilani and is a certified Google Professional Data Engineer. His strategic thinking, technical expertise, and professional qualifications make him a trusted authority in the field of data engineering, driving data innovation and enabling organizations to harness the power of advanced analytics and AI.

Table of Contents

Table of Contents

1. Fundamentals of Data Architecture
2. Essentials of Data Engineering
3. Designing a Data Architecture
4. GCP for Data Analytics
5. GCP Networking
6. GCP Security
7. GCP Environment Set-up
8. Designing the Bronze Layer of Data & Implementing ingestion architecture
9. Data Catalog : Serverless, fully managed, scalable metadata management service
10. Silver Layer : Implementing data processing systems for batch
11. Silver Layer : Implementing data processing systems for streaming
12. Setting-up DevOps for development and deployment
13. Logging, Monitoring, Data Quality, Lineage and Observability
14. Implementing Golden Layer of Data
15. Orchestrating on GCP
16. Building Systems for Data Analysis and Visualisation
17. Design and Implement robust systems for Advanced Analytics ( AI & ML)
18. Migrating Data Platforms to GCP
19. Design Best practices for performance and cost
20. GCP Certifications for Data Architects