Implementing Cloud-Native Data Lakes: A Comparative Study of GCP, Azure, and Hadoop Architectures for Global Retail Merchandising
Keywords:
cloud-native data lakes, GCP, Azure, Hadoop, retail merchandising, scalability, cost efficiency, query performance, data ingestion, ecosystem integration.Abstract
The rapid growth of data in global retail merchandising has necessitated the adoption of cloud native data lakes to enable scalable, efficient, and cost-effective data management. This study conducts a comparative analysis of three prominent architectures—Google Cloud Platform (GCP), Microsoft Azure, and Hadoop—to evaluate their suitability for retail use cases. Through a mixed-methods approach, the research benchmarks key performance metrics, including data ingestion speed, query performance, scalability, and cost efficiency, while also examining qualitative insights from real-world implementations.
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