Modern Data Architecture in AI

Optimize AI data storage, versioning, and partitioning with lakehouse (English Edition)

Abhik Choudhury, Praneeth Puchakayala, Aishwarya Badlani

• Publisher: BPB Publications
• Year: 2025
• eBook ISBN: 9789365899771
• Edition: 0
• Subtopic: Data Mining

Description
Building effective AI solutions demands a robust data architecture capable of handling vast, diverse, and real-time data. This book aims to provide a deep exploration of the tools, technologies, strategies, and best practices that necessitate the design, implementation, and management of data architectures tailored to AI.

The book starts by introducing fundamental concepts of modern data architecture for AI, laying the groundwork for understanding its importance. It then digs deep into the aspects of data ingestion and collection strategies. Subsequently, it discusses data storage and management techniques that cater specifically to AI workloads. Readers will understand the concepts of data processing, transformation, and building scalable and efficient data pipelines, and how to orchestrate interconnected processes. The book further explores the topics of scalable ML infrastructure and stream processing, concluding with insights into visualization, explainable AI, and future trends.

By the end of this book, the readers will have a comprehensive understanding and the skills to develop and manage scalable and efficient AI systems. They will have a firm grasp on the collection, storage, processing, and transformation of data, ensuring data governance and security. After reading this book, you will be well-equipped to design, build, and manage cutting-edge data architectures for diverse AI workloads, empowering your strategic initiatives.

What you will learn
? Build data pipelines with automated orchestration and monitoring.
? Design scalable data lakes and lakehouse architectures for AI workloads.
? Learn data governance, security, and compliance frameworks.
? Leverage emerging technologies like quantum and edge computing.
? Optimize infrastructure for distributed ML training and serving.
? Visualize AI insights and apply explainable AI methods for transparency.
? Understand LLMs, generative AI, federated learning, and their data architecture impact.
? Architect real-time AI systems with online learning and low-latency stream processing.

Who this book is for
This book is for data engineers, ML engineers, and enterprise architects who are at the forefront of designing and implementing scalable AI data systems. It is an essential guide for building robust data foundations. Software developers transitioning into AI infrastructure roles and technical leaders planning AI initiatives will also benefit significantly.

Table of Contents
1. Introduction to Modern Data Architecture for AI
2. Data Collection and Ingestion Strategies
3. Data Storage and Management for AI Workloads
4. Data Processing and Transformation for AI
5. Modern Data Pipeline Management
6. Data Governance, Security, and Compliance in AI
7. AI Algorithms and Their Impact on Data Architecture
8. Scalable Machine Learning Infrastructure
9. Real-time AI Systems and Stream Processing
10. Data Visualization and Explainable AI
11. Emerging Trends in AI Data Architecture

Table of contents

• Cover
• Title Page
• Copyright Page
• Dedication Page
• About the Authors
• About the Reviewer
• Acknowledgements
• Preface
• Table of Contents
• 1. Introduction to Modern Data Architecture for AI
• 2. Data Collection and Ingestion Strategies
• 3. Data Storage and Management for AI Workloads
• 4. Data Processing and Transformation for AI
• 5. Modern Data Pipeline Management
• 6. Data Governance, Security, and Compliance in AI
• 7. AI Algorithms and Their Impact on Data Architecture
• 8. Scalable Machine Learning Infrastructure
• 9. Real-time AI Systems and Stream Processing
• 10. Data Visualization and Explainable AI
• 11. Emerging Trends in AI Data Architecture
• Index