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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM17988

Optimizing Data Storage Architectures for Real-Time and Batch Processing Workloads: Balancing Scalability and Cost-Efficiency

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM17988

Volume:

6 |

3 |

Mar 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM17988

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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VG

Varun Garg

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Abstract

Applications powered by data in media, finance, and IoT call for architectures that enable batch processing as well as real-time workloads. For hybrid processing requirements, conventional solutions sometimes find it difficult to strike a mix between cost and scalability. This work presents a hybrid data storage system tuned for scalability and economy of cost. Combining tiered storage, data partitioning, and effective processing pipelines helps the design maintain sub-10 ms latency for real-time applications and lowers operational expenses by up to 30%. Experimental results confirm the efficiency of the design, so it is appropriate for sectors needing fast data insights together with big-scale analytics.

KEYWORDS

Real-time processing batch processing hybrid data storage scalability cost-effective data architecture.

Cite This Article

Varun Garg, "Optimizing Data Storage Architectures for Real-Time and Batch Processing Workloads: Balancing Scalability and Cost-Efficiency," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 3, Mar 2022. https://doi.org/10.55041/IJSREM17988

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Publication Date

7/11/2024

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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