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The Hidden Tax: A Production Framework for Cloud Cost Architecture in Enterprise Data Workloads

Rahul Jain

Infra Technology Specialist, USA

Abstract

Public cloud infrastructure has enabled enterprise data platforms to scale with unprecedented speed, yet many organizations still lack financial discipline in their architecture decisions. Teams optimize for speed and scale first, treating cost optimization as a post-hoc billing exercise rather than a first-class engineering requirement. In this paper, we present an eighteen-month study of a production data platform within a Fortune 500 organization, encompassing Apache Spark on Amazon EMR, Presto clusters, and Amazon Redshift. By analyzing resource telemetry alongside detailed cloud billing data, we identify three structural sources of waste that persist even in mature cloud environments: (1) cluster sizing anchored to peak load rather than actual demand, resulting in average CPU utilization below 35%; (2) batch workload scheduling that ignores the fault-tolerant nature of Spark pipelines, leaving spot instance discounts untapped; and (3) storage architectures that replicate on-premises Hadoop patterns—co-locating persistent data with compute—creating redundant data copies across workload-specific clusters.

 

We propose and evaluate a four-component cost-conscious architecture: spot-aware orchestration with lineage-based checkpointing, queue-driven dynamic cluster sizing, tiered storage decoupled from compute, and a governance layer that attributes costs to individual workloads. Deployed incrementally over twelve months, this framework reduced monthly cloud compute spend by 47%, increased pipeline SLA compliance from 98.4% to 99.9%, and decreased average job completion time by 18%. These results demonstrate that cloud cost optimization for data platforms is fundamentally an architectural problem—not a billing problem—and must be treated with the same rigor as performance and reliability engineering.

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 2 No. 6 (2019): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2019.0206003

Pages

2522-2530

Published

November 8, 2019
Copyright

Open Access

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

How to Cite

Rahul Jain (%2019). The Hidden Tax: A Production Framework for Cloud Cost Architecture in Enterprise Data Workloads. International Journal of Science, Research and Technology , Vol. 2 No. 6 (2019): International Journal of Science, Research and Technology (IJSRAT) , pp. 2522-2530. https://doi.org/10.15662/IJSRAT.2019.0206003

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