Big Data in Practice: Harnessing Spark, Kafka, and cloud computing for scalable AI solutions (English Edition)

Jitender Jain, Medha Gupta

ISBN 10: 9365896118 ISBN 13: 9789365896114

Published by BPB Publications, 2026

Language: English

Condition: New Soft cover

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Bibliographic Details

Title: Big Data in Practice: Harnessing Spark, Kafka, and cloud computing for scalable AI solutions (English Edition)
Publisher: BPB Publications
Publication Date: 2026
Language: English
ISBN 10: 9365896118
ISBN 13: 9789365896114
Binding: Paperback
Condition: New
Dimensions: 19.05 centimeters width by 1.5 centimeters height by 23.5 centimeters depth
Weight: 454 grams

About this title

Synopsis

Artificial intelligence systems today are driven by data at unprecedented scale. As machine learning, real-time inference, and generative AI reshape industries, organizations need robust big data platforms to ingest, process, and operationalize vast and complex datasets. Big data has become the backbone of modern AI systems, making data engineering skills essential for professionals across technology, analytics, and AI roles.

This book provides a practical guide to designing and building data platforms that power AI applications. It covers core big data technologies such as Hadoop, Spark, Kafka, NoSQL, and cloud data platforms, then connects them to the AI lifecycle, including data ingestion, feature engineering, scalable model training, real-time inference, and MLOps. Real-world use cases across finance, healthcare, e-commerce, and autonomous systems demonstrate how these technologies work together in production environments.

By the end of this book, the readers will be equipped to design end-to-end big data pipelines, support scalable AI and ML workloads, and extract insights from data at any velocity or volume. Whether you are a data engineer, ML practitioner, or architect, this book prepares you to build and operate AI-ready data systems with confidence.

What you will learn

● Design scalable big data platforms for AI systems.

● Process streaming and batch data at scale.

● Apply cloud-native architectures for data and AI.

● Engineer features and train models at scale.

● Deploy models with real-time inference and MLOps.

● Govern data security, privacy, and compliance at scale.

Who this book is for

This book is aimed at intermediate level professionals working with data and enterprise systems who want to apply big data technologies in real-world AI projects. It is well suited for data engineers, ML practitioners, software engineers, architects, and IT professionals building scalable AI-driven data platforms.

Table of Contents

1. Introduction to Big Data and AI integration

2. Big Data Storage and NoSQL Databases

3. Distributed Batch Processing with MapReduce and Apache Spark

4. Real-time Data Streaming and Analytics

5. Cloud-based Big Data Platforms

6. Data Ingestion, Preparation, and Feature Engineering

7. Scalable Machine Learning Model Training

8. Model Deployment and Real-time Inference

9. MLOps and Pipeline Automation

10. Big Data in Finance and FinTech

11. Big Data in Healthcare and Biomedicine

12. Big Data in E-commerce and Marketing

13. Big Data in IoT and Autonomous Systems

14. Data Governance, Security, and Privacy

15. Emerging Trends and Future Outlook

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