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ETL Strategies for Large-Scale Retail Data Warehouses

Author(s) Ravi Kiran Koppichetti
Country United States
Abstract Large-scale retail data warehouses are critical for storing and analyzing vast amounts of transactional, operational, and customer data. Effective ETL (Extract, Transform, Load) strategies are essential for ensuring that data is accurately extracted from diverse sources, transformed into a usable format, and loaded into the data warehouse for analysis. This paper explores the challenges of implementing ETL processes in large-scale retail data warehouses and provides strategies for optimizing ETL workflows. Key topics include data integration, scalability, performance optimization, and the use of modern ETL tools and technologies. The paper concludes with recommendations for designing robust ETL pipelines that meet the demands of the retail industry.
Keywords ETL, Extract Transform Load, Data Warehouse Architectures, Data Warehouse Data Mining, Machine Learning, Artificial Intelligence, Data Science, Data Preprocessing, Data Pre-Processing, Data Preparation
Field Engineering
Published In Volume 3, Issue 8, August 2022
Published On 2022-08-02
Cite This ETL Strategies for Large-Scale Retail Data Warehouses - Ravi Kiran Koppichetti - IJLRP Volume 3, Issue 8, August 2022. DOI 10.5281/zenodo.15026506
DOI https://doi.org/10.5281/zenodo.15026506
Short DOI https://doi.org/g88f5n
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