Future-Ready Data Foundation with MongoDB: Principles for designing scalable and AI-ready data architectures (Paperback or Softback)
Language: English
Published by Packt Publishing 8/7/2026, 2026
- Softcover
- New
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Future-Ready Data Foundation with MongoDB: Principles for designing scalable and AI-ready data architectures.
Seller Inventory # BBS-9781807607173
- Title
- Future-Ready Data Foundation with MongoDB: Principles for designing scalable and AI-ready data architectures (Paperback or Softback)
- Author
- Evans, Sarah
- Publisher
- Packt Publishing 8/7/2026
- Publication year
- 2026
- Condition
- New
- Book Type
- Book
- Binding
- Paperback or Softback
- Language
- English
- ISBN 10
- 1807607178
- ISBN 13
- 9781807607173
- Item weight
- 0.27 pounds
Learn how MongoDB helps organizations build scalable, secure, and AI-ready data foundations that power RAG, AI agents, and intelligent applications while simplifying performance, governance, and growth.
Key Features
- Identify the data requirements that enable AI applications to succeed in production
- Evaluate retrieval strategies for accurate and trustworthy AI experiences
- Apply architectural principles for scalable, AI-ready data platforms
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
AI success depends on more than models, it requires a data foundation built for scale, intelligence, and trust. In Future-Ready Data Foundation with MongoDB, you’ll discover the principles behind building AI-ready data architectures that support modern applications, retrieval-augmented generation (RAG), and AI agents. This concise guide explores the critical role of data in AI modernization and shows how MongoDB helps organizations create a unified foundation for innovation.
Through practical architectural insights, you’ll learn how retrieval strategies influence the quality and reliability of AI outcomes and how MongoDB’s document model and vector search capabilities support intelligent data access. You’ll also explore the scalability, performance, and operational patterns required to keep AI systems running efficiently, including replication, sharding, workload isolation, and search optimization. Finally, you’ll examine the governance, observability, security, and compliance considerations that help organizations deploy AI responsibly and at scale.
By the end of this book, you’ll be equipped to evaluate, design, and optimize data foundations that support the next generation of AI-powered applications.
What you will learn
- Identify the data foundations required for successful AI initiatives
- Compare lexical, vector, and hybrid retrieval strategies
- Understand how RAG and AI agents shape modern AI architectures
- Use MongoDB's document model to store and manage data for AI applications
- Explore native vector search for semantic retrieval and AI-powered search experiences
- Assess scaling patterns using replication, sharding, and dedicated Search Nodes
- Apply governance, security, and compliance practices for AI systems
Who this book is for
This book is for technology leaders, data architects, data engineers, platform engineers, and AI practitioners who want to build strong foundations for modern AI initiatives. It is also valuable for decision-makers evaluating how data architecture affects the scalability, performance, security, and governance of AI systems. By reading this book, you’ll gain the architectural knowledge needed to assess, design, and support AI-ready data platforms that power RAG applications, AI agents, and other intelligent solutions.
Table of Contents
- Building a Unified Data Foundation for Sustainable AI Modernization
- Selecting the Right Retrieval Strategy for AI Workloads
- How MongoDB's Architecture Supports Modern AI
- How Replication, Sharding, and Workload Isolation Keep AI Systems Running
- Eliminating the Hidden Cost of AI Infrastructure with MongoDB
- Governance and Compliance for AI-Ready Organizations
"Synopsis" may belong to another edition of this title.
About the Author
Sarah Evans is a Senior Curriculum Engineer at MongoDB, where she designs and develops technical learning experiences for MongoDB University. With a background that bridges curriculum design and hands-on technical expertise, she specializes in translating complex database and data modeling concepts into clear, accessible content for developers and technology professionals. Sarah is passionate about making technical education practical and engaging, drawing on her experience developing curricula that empowers learners to apply knowledge in real-world scenarios. Since joining MongoDB in 2021, she has contributed to a wide range of educational initiatives spanning data modeling, database architecture, and developer enablement. Sarah is also deeply interested in AI-driven development and building AI-powered products, and she brings that enthusiasm to her work helping learners navigate the intersection of modern databases and intelligent application design.
"About the title" may belong to another edition of this title.
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