Data Engineering for AI

Data engineering is the critical discipline of building and maintaining the systems that enable organizations to collect, store, process, and analyze vast amounts of data, especially for advanced applications like AI and ML. It is about ensuring that it is reliable, accessible, and high-quality for everyone who needs it.

This book provides a thorough exploration of the complete data lifecycle, starting with data engineering's development and its vital link to AI. It provides an overview of scalable data practices, from legacy systems to cutting-edge techniques. The reader will explore real-time data collection, secure ingestion, optimized storage, and dynamic processing techniques. The book features detailed discussions on ETL and ELT frameworks, performance tuning, and quality assurance that are complemented by real-world case studies. All these empower the data engineers to design systems that are seamless and integrate well with AI pipelines, driving innovation across diverse industries.

By the end of this book, readers will be well-equipped to design, implement, and manage scalable data engineering solutions that effectively support and drive AI initiatives within any organization.

WHAT YOU WILL LEARN

* Design real-time data ingestion and processing systems.
* Implement optimized data storage solutions for AI workloads.
* Ensure data quality, compliance in dynamically changing environments.
* Build scalable data collection methods, including for AI training data.
* Apply data engineering solutions in complex, real-world AI projects.
* Conduct SQL analytics and craft insightful, AI-driven visualizations.

WHO THIS BOOK IS FOR
This book is for data engineers, AI practitioners, and curious professionals with a foundational understanding of databases, programming, and ETL processes. A basic understanding of computer science concepts, cloud computing, and analytics is helpful.

1. Introduction to Data Engineering in AI
2. Managing Data Collection
3. Data Ingestion in Action
4. Data Storage in Real-time
5. Data Processing Techniques and Best Practices
6. Data Integration and Interoperability
7. Ensuring Data Quality
8. Understanding Data Analytics
9. Data Visualization and Reporting
10. Operational Data Security
11. Protecting Data Privacy
12. Data Engineering Case Studies

Sundeep Goud Katta is a seasoned technology leader based in California, with over 13 years of experience in AI-driven solutions, cloud-based architectures, and scalable CRM platforms. As a lead, he has spearheaded enterprise-grade initiatives that streamline deployment pipelines, enhance system resilience, and drive intelligent automation through GPT-powered models and predictive analytics. Sundeep’s technical expertise spans across CRM experience platforms, Azure cloud ecosystems, and modern web technologies, including Three.js, Revit, and WPF. He has played a pivotal role in large-scale platform migrations, performance tuning, and the creation of robust validation and monitoring frameworks that power secure, high-performing, and user-centric systems. An active contributor to the tech community, Sundeep has served as a reviewer for IEEE COMPASS, judged prestigious industry awards such as Globee and Brandon Hall, and reviewed technical publications for leading publishers including O’Reilly and Manning. His passion for innovation, technical excellence, and knowledge-sharing positions him as a leading voice in the evolving landscape of scalable data engineering.

Lav Kumar is a seasoned full-stack developer with over 12 years of experience architecting and delivering scalable, enterprise-grade software solutions. Based in Fremont, California, Lav currently serves as a lead member of technical staff at a leading CRM company in San Francisco, where he plays a pivotal role in advancing AI-powered search capabilities. His work focuses on enhancing user experience through personalized search results and the optimization of intelligent search algorithms. Lav’s professional journey includes impactful tenures at Nokia and Samsung, where he contributed to the development of core features and applications that helped shape the commercial success of flagship products. His technical expertise spans Java/J2EE, modern UI development, microservices architecture, and the integration of AI/ML technologies into production systems. A passionate advocate for data science and big data, Lav is dedicated to building innovative, data-driven solutions that scale. His contributions have earned him multiple awards for excellence and innovation across his career. As a Salesforce Trailhead Ranger and an Accelerate Program Graduate, Lav demonstrates an ongoing commitment to professional growth and technical mastery.