The Autonomous Data Future: Architecting Intelligent Data Platforms, Real-Time Analytics, and Agentic AI Systems for Next-Generation Enterprises

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Authors

Sheetal Tatiya
Data Engineering Mgmt and Governance Associate Manager at Accenture

Keywords:

Autonomous Data, Data Platforms, Real-Time Analytics, Agentic Artificial Intelligence, Artificial Intelligence, Sustainable AI, AI infrastructure

Synopsis

This book is for the visionaries, innovators and seekers of knowledge who maintain a dream and design a world led by intelligent autonomous systems self-improving as each byte expands exponentially. It pays tribute to those who recognize that data is not just information, but a fuel for transforming industries, maximizing human potential and changing the way decisions are made in an ever-more-digital world. To the early adopters of artificial intelligence, machine learning, and data science, this work is a tribute to your lasting contributions. Your commitment to innovation and solving the hardest problems has enabled autonomous systems to become a reality. Your work is still igniting innovation and carving new avenues for the growth of technologies. To researchers and scholars with a dedication to inquiry and discovery: this book acknowledges your bridge between the theory of deeper learning and its real-world implementation. With your work you allow systems to learn and evolve with better intelligence, forming the ground base for the modern-day data ecosystem.

This book is written for the engineers, developers and analysts who design and build the digital infrastructure. Your work is the foundation of reliable and scalable systems that allow data driven technologies to operate efficiently and effectively in real-world environments. To the teachers and mentors who inspire curiosity and critical thinking, this book is a testament to your impact. Your guidance is cultivating the minds of those who will be leading innovations of tomorrow and continuing this quest for discovery and excellence. Lastly, you, my family I simply cannot thank you enough for all your support and patience as well as encouragement. The confidence you have in your journey has carried us through, and it is this confidence that enables us to achieve this milestone. To my colleagues and collaborators, I appreciate your thoughts, discussions, and shared dedication. Many of the ideas given in this work are a result of your efforts.

Finally, this book is for the readers who want to learn and innovate in order to make a difference. So, May the Autonomous Data Future compel you to open your mind and challenge pre-existing notions and help build a future with purpose where data and intelligent systems serve mankind.

References

Nambiar, A., & Mundra, D. (2022). An overview of data warehouse and data lake in modern enterprise data management. Big data and cognitive computing, 6(4), 132.

Solanki, R. (2025). Data lakehouse architecture: The evolution of enterprise data management. Journal of Computer Science and Technology Studies, 7(6), 912-921.

Jha, V. Y. (2024). Data Lakehouse Architectures: Bridging Structured and Unstructured Data. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(6), 11218-11221.

Prabhakaran, S. P. (2025). Cloud-Native Data Analytics Platform with Integrated Governance: A Modern Approach to Real-Time Stream Processing and Feature Engineering.

Armbrust, M., Ghodsi, A., Xin, R., & Zaharia, M. (2021, January). Lakehouse: a new generation of open platforms that unify data warehousing and advanced analytics. In Proceedings of CIDR (Vol. 8, No. 1, p. 28). sn.

Downloads

Published

29 April 2026

Details about the available publication format: E-Book

E-Book

ISBN-13 (15)

978-93-7185-894-6

Details about the available publication format: Book (Paperback)

Book (Paperback)

ISBN-13 (15)

978-93-7185-239-5

How to Cite

Tatiya, S. . (2026). The Autonomous Data Future: Architecting Intelligent Data Platforms, Real-Time Analytics, and Agentic AI Systems for Next-Generation Enterprises. Deep Science Publishing. https://doi.org/10.70593/978-93-7185-894-6