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Hybrid Data Lake Entities Localization Optimization Based on Complex Networks
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DOI：10.1109/ACCESS.2026.3656719.png)
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Abstract
En 中文
Data Lakes are a relevant analytics architecture, with current implementations in hybrid and multi-cloud environments presenting governance challenges in cost management and data localization. This work proposes the “Entity Migration Recommender System,” an engine that ingests Data Lake metadata to create optimized complex network digraphs with recommended entity localities. The tool demonstrated a 16.2% overall cost reduction by optimizing data entity placement across cloud and on-premise infrastructure. This method leverages complex network metrics to address the critical dimension of data interoperation costs in distributed analytics environments.
Keywords:
Cloud computing
complex networks
data analytics
data lakes