International Journal on Science and Technology

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Optimizing Data Ingestion for Machine Learning Training in Large-Scale Social Media Platforms

Author(s) Ramesh Mohana Murugan
Country United States
Abstract This comprehensive article explores the critical yet often overlooked challenge of data ingestion optimization for machine learning systems in large-scale social media environments. As social platforms generate unprecedented volumes of data, efficient ingestion processes become essential for maintaining computational performance and enabling rapid model iteration. The article examines data engineers' multifaceted challenges, including I/O bottlenecks, network latency issues, and storage format inefficiencies that directly impact GPU utilization. We present a framework for dramatically improving data pipeline efficiency by systematically exploring parallel data loading architectures, optimal storage format selection, and advanced feature engineering techniques such as flattening and reordering. It demonstrates that strategic optimization of the data ingestion layer can substantially reduce training times, lower computational resource requirements, and accelerate the development cycle for machine learning applications in social media contexts.
Keywords Keywords: Data Ingestion Optimization, Feature Engineering, Parallel Data Loading, Storage Format Efficiency, Machine Learning Pipeline.
Field Computer
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-29
Cite This Optimizing Data Ingestion for Machine Learning Training in Large-Scale Social Media Platforms - Ramesh Mohana Murugan - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.3015
DOI https://doi.org/10.71097/IJSAT.v16.i1.3015
Short DOI https://doi.org/g899gp
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