Big Data in Practice

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.

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

● Jitender Jain is a seasoned software engineer and AI architect with over 17 years in the technology industry. He has led huge enterprise projects in the finance and retail sectors, designing end-to-end platforms that handle billions of records for mission-critical applications. Notably, the author is the inventor and has many patents, like US11893819B2, which is enhancing OCR technology through AI for large-scale unstructured data processing in real-time. This patented work, involving automated income verification from document images, exemplifies how advanced AI solutions can be built by harnessing data through big data technologies. Throughout his career, he has implemented enterprise data pipelines using API driven methodology leveraging Spark and Kafka, and has a deep understanding of how to operationalize advanced AI models in real-time environments. He has exposure to finance and retail sectors, where he built sophisticated enterprise-level software systems leveraging big data, engineering, and AI to drive business innovation. He is an influential speaker at multiple industry and academia conferences and is also a Forbes Technology Council member. Here to share practical knowledge and help professionals apply big data tools to solve real-world problems.

● Medha Gupta is a senior data engineer with multiple years of experience. She specializes in building scalable data pipelines and analytics platforms that support data-driven decision- making. With strong expertise in big data processing, cloud-based data architectures, and data modeling, she works on transforming raw data into reliable, high-quality datasets for reporting and advanced analytics. Medha has hands-on experience with modern data engineering tools and cloud platforms, enabling efficient ingestion, processing, and optimization of large datasets. Her work focuses on improving data reliability, lineage, performance, and accessibility across enterprise systems, helping organizations derive meaningful insights from complex data ecosystems.