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Paper Title

Balancing Performance and Economics in Hybrid Cloud Data Architectures

Authors

Praveen Kodakandla

Keywords

hybrid cloud architecture, performance optimization, cost governance, multi-cloud, edge computing, data sovereignty, AI automation

Abstract

As enterprises increasingly distribute their data and workloads across on-premises and cloud environments, achieving the right balance between performance and economic efficiency has become a defining challenge in modern data strategy. Hybrid cloud data architectures allow organizations to combine the control and security of local systems with the elasticity and innovation of cloud platforms. However, realizing this potential requires deliberate design choices that optimize cost, scalability, and operational performance. This article examines the core principles of hybrid cloud data architecture, emphasizing design strategies, cost-governance models, and performance optimization methods. Drawing insights from real-world implementations—particularly in the banking and financial sectors—it illustrates how enterprises can achieve agility and compliance while containing costs. The discussion also explores key challenges such as latency, interoperability, vendor lock-in, and data sovereignty, and highlights emerging trends like AI-driven infrastructure management, edge computing, and zero-trust security frameworks. By presenting a structured and practical approach, this study serves as a reference for IT leaders and data architects seeking to build resilient, high-performing, and financially sustainable hybrid ecosystems.

How To Cite

Choose the style your journal or department asks for, then copy it. Every version below is generated from this paper's own record.

IJRTI — journal style
"Balancing Performance and Economics in Hybrid Cloud Data Architectures", IJRTI - International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.7, Issue 2, page no.135-148, February-2022, Available :https://ijrti.org/papers/IJRTI2202021.pdf
APA — 7th edition
Kodakandla, P. (2022). Balancing Performance and Economics in Hybrid Cloud Data Architectures. International Journal for Research Trends and Innovation, 7(2), 135-148. https://ijrti.org/viewpaperforall.php?paper=IJRTI2202021
MLA — 9th edition
Kodakandla, Praveen. "Balancing Performance and Economics in Hybrid Cloud Data Architectures." International Journal for Research Trends and Innovation, vol. 7, no. 2, 2022, pp. 135-148, https://ijrti.org/viewpaperforall.php?paper=IJRTI2202021.
Chicago — 17th, bibliography
Kodakandla, Praveen. "Balancing Performance and Economics in Hybrid Cloud Data Architectures." International Journal for Research Trends and Innovation 7, no. 2 (2022): 135-148. https://ijrti.org/viewpaperforall.php?paper=IJRTI2202021.
Harvard — author–date
Kodakandla, P. (2022) 'Balancing Performance and Economics in Hybrid Cloud Data Architectures', International Journal for Research Trends and Innovation, 7(2), pp. 135-148. Available at: https://ijrti.org/viewpaperforall.php?paper=IJRTI2202021
IEEE — numbered reference
P. Kodakandla, "Balancing Performance and Economics in Hybrid Cloud Data Architectures," IJRTI, vol. 7, no. 2, pp. 135-148, Feb. 2022.
Vancouver — biomedical
Kodakandla P. Balancing Performance and Economics in Hybrid Cloud Data Architectures. IJRTI. 2022 Feb;7(2):135-148.
AMA — 11th edition
Kodakandla P. Balancing Performance and Economics in Hybrid Cloud Data Architectures. IJRTI. 2022;7(2):135-148. Accessed at https://ijrti.org/viewpaperforall.php?paper=IJRTI2202021
BibTeX — Zotero, Mendeley, LaTeX
@article{IJRTI2202021, author = {Praveen Kodakandla}, title = {Balancing Performance and Economics in Hybrid Cloud Data Architectures}, journal = {International Journal for Research Trends and Innovation}, volume = {7}, number = {2}, pages = {135-148}, year = {2022}, month = {February}, issn = {2456-3315}, url = {https://ijrti.org/viewpaperforall.php?paper=IJRTI2202021} }
RIS — EndNote, RefWorks
TY - JOUR AU - Kodakandla, Praveen TI - Balancing Performance and Economics in Hybrid Cloud Data Architectures T2 - International Journal for Research Trends and Innovation JA - IJRTI VL - 7 IS - 2 PY - 2022 SN - 2456-3315 UR - https://ijrti.org/viewpaperforall.php?paper=IJRTI2202021 SP - 135 EP - 148 ER -

Issue

Volume 7 Issue 2, February-2022
Pages : 135-148

Other Publication Details

Paper Reg. ID IJRTI_206812
Published Paper ID IJRTI2202021
Downloads 205,824
Research Area Engineering
Country -, -, India
Published February 2022

About Publisher

International Journal for Research Trends and Innovation Published by IJRTI (JW Publication)
2456-3315 ISSN
10.57 Impact Factor
2016 ESTD Year
Open Access
Impact Factor 10.57 calculated by Google Scholar and Semantic Scholar.
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 10.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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Licence

© 2022 — Authors hold the copyright of this article. This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
 Disclaimer: The content, data and findings in this article are based on the authors’ research and have been peer-reviewed for academic purposes only. Readers are advised to verify all information before practical or commercial use. The journal and its editorial board are not liable for any errors, losses or consequences arising from its use.

Declarations

Funding

No external funding was received for this study.

Conflict of Interest

The authors declare that they have no conflict of interest.

Acknowledgements

The authors would like to thank the reviewers and the editorial board of International Journal for Research Trends and Innovation for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.

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