Data Lake Optimization Using Delta Architecture on Cloud Platforms

Published Paper PDF: https://ijrmeet.org/wp-content/uploads/2025/06/IJRMEET0625280036_Data%20Lake%20Optimization%20Using%20Delta%20Architecture%20on%20Cloud%20Platforms.pdf

DOI: https://doi.org/10.63345/ijrmeet.org.v13.i6.3

⁠Satyam Agarwal

Independent Researcher

Gandhi Road, Baraut

Abstract

The rapid proliferation of data within enterprises has elevated the strategic importance of data lakes as foundational repositories for advanced analytics and machine learning. However, traditional data lake implementations often suffer from performance bottlenecks, data governance challenges, and escalating storage costs. Delta Architecture, an evolution of the Delta Lake paradigm, introduces ACID transactions, schema enforcement, and efficient file management to address these concerns on cloud platforms. This manuscript explores optimization strategies for data lakes leveraging Delta Architecture on leading cloud providers. We examine architectural patterns, storage layout designs, metadata management approaches, and compute resource orchestration to achieve sub-second query latencies, data reliability, and cost-effective scalability. Drawing on a comprehensive literature review and empirical evaluation on AWS, Azure, and Google Cloud, we quantify performance gains from micro-batch compaction, Z-order clustering, and auto-tuning of compute clusters. Results demonstrate up to 85% reduction in query execution time and 40% savings in storage consumption compared to baseline parquet-based data lakes. We conclude with best practices for deployment, orchestration, and operational monitoring, and outline the scope and limitations of our study to guide future enhancements.

Keywords

Delta Architecture; data lake optimization; cloud platforms; ACID transactions; Z-order clustering; performance tuning

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