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The Agentic Data Warehouse: Architecting Autonomous ETL, Self-Healing Governance, and Actionable Embedded Analytics
ISBN Book

The Agentic Data Warehouse: Architecting Autonomous ETL, Self-Healing Governance, and Actionable Embedded Analytics

Author:
Sumit Sachdeva & Dr. P. Bastin Thiyagaraj

ISBN: 978-93-49929-00-5

DOI: https://doi.org/10.63282/978-93-49929-00-5


The agentic data warehouse: architecting autonomous ETL, self-healing governance, and actionable embedded analytics explores the next generation of intelligent data warehouse systems powered by autonomous and agent-based artificial intelligence. The book presents innovative approaches for designing self-managing ETL pipelines, adaptive data governance frameworks, and embedded analytics capable of delivering real-time, actionable insights. It examines how AI agents can automate data integration, monitor data quality, detect anomalies, enforce compliance, and optimize decision-making processes with minimal human intervention. By combining concepts from data warehousing, machine learning, automation, and business intelligence, the book provides researchers, data architects, and industry professionals with practical strategies for building resilient, scalable, and intelligent data ecosystems that support modern digital enterprises.


Sumit Sachdeva is a strategic and results-driven IT leader with over 17 years of extensive experience in directing large-scale business intelligence, data warehousing, and advanced data analytics projects. Currently serving as the Technical Manager for Predictive Analytics and Business Intelligence at The Scotts Company LLC, he specializes in modernizing enterprise architecture, implementing cloud-native processing frameworks, and driving operational efficiencies through data-driven insights. Throughout his distinguished career, Sumit has led cross-functional teams and engineered robust analytic environments across major enterprise ecosystems, including SAP, AWS, and Databricks. A recognized expert in his field, his research on artificial intelligence, container orchestration, and cloud-hybrid data architecture has been widely published in leading international journals.

Dr. P. Bastin Thiyagaraj is currently serving as an Assistant Professor in the Department of Information Technology at St. Joseph’s College (Autonomous), Tiruchirappalli, Tamil Nadu, India. With 16 years of experience in academia, he brings strong expertise in teaching, research, and academic coordination. His research interests include Artificial Intelligence (AI), Machine Learning, Data Analytics, Intelligent Systems, and Emerging Digital Technologies. His work focuses on applying AI techniques to real-world domains such as social media analytics, predictive modeling, automation, and intelligent decision-making systems. Dr. Bastin Thiyagaraj has authored and co-authored 16 research publications in reputed national and international journals and conferences. He actively participates in seminars, workshops, and faculty development programs to enhance academic excellence and research innovation. He also serves as the Additional Coordinator of the Joseph Academy of Soft Skills (JASS), contributing to leadership training and student skill development initiatives. As a committed educator and researcher, Dr. Bastin Thiyagaraj continues to inspire students and contribute to advancements in intelligent and data-driven technologies.


Title: The Agentic Data Warehouse: Architecting Autonomous ETL, Self-Healing Governance, and Actionable Embedded Analytics
Author: Sumit Sachdeva & Dr. P. Bastin Thiyagaraj
Publisher: ScienceTech Xplore
Copyright: ScienceTech Xplore
Language: English
Publication Format: Online
ISBN: 978-93-49929-00-5
DOI: https://doi.org/10.63282/978-93-49929-00-5
Subject/Genre: Computer Science & Artificial Intelligence
Pages: 1-286
Chapters: 13
Year of Publication: 2026
URL: https://sciencetechxplore.org/978-93-49929-00-5.php
Cite As:

Sachdeva, S., & Thiyagaraj, P. B. (2026). The Agentic Data Warehouse: Architecting Autonomous ETL, Self-Healing Governance, and Actionable Embedded Analytics. ScienceTech Xplore. https://doi.org/10.63282/978-93-49929-00-5

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