Adaptive AI-Driven Optimization of Hybrid Enterprise Analytics Platforms

Authors

  • Dheeraj Kumar Bansal

Keywords:

Hybrid Analytics, Enterprise Data Engineering, Artificial Intelligence, Machine Learning Optimization, Cloud Computing

Abstract

Enterprise analytics platforms have evolved from centralized data warehousing systems to sophisticatedhybrid architectures that integrate on-premise infrastructure with cloud-native services. Traditionaloptimization methods rely on static configurations and manual tuning procedures that prove inadequate for dynamic enterprise workloads characterized

References

Thomas H. Davenport and Randy Bean conducted the "Big Data and AI Executive Survey 2019," published by NewVantage Partners in 2019. [Online]. Available: https://www.the-digital-insurer.com/wp content/uploads/2019/02/1418-Big-Data-Executive-Survey-2019-Findings-122718.pdf

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Published

2026-02-22

How to Cite

Dheeraj Kumar Bansal. (2026). Adaptive AI-Driven Optimization of Hybrid Enterprise Analytics Platforms. Journal of Computational Analysis and Applications (JoCAAA), 35(2), 471–481. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5010

Issue

Vol. 35 No. 2 (2026): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.