Citation:

Nishanth Reddy Mandala, 2021. "ETL in Data Lakes vs. Data Warehouses", ESP Journal of Engineering & Technology Advancements, 1(2): 224-230.

Abstract:

The Extract, Transform, Load (ETL) process plays a pivotal role in data integration, enabling businesses to consolidate data from disparate sources into a unified system for analysis. This paper presents a comparative analysis of ETL processes in Data Lakes and Data Warehouses, focusing on the architecture, performance, and flexibility of each approach. The discussion highlights the distinct roles of data lakes, which handle large volumes of unstructured and semi-structured data, versus traditional data warehouses, which are optimized for structured, relational data. Several case studies and performance evaluations are included to illustrate the strengths and weaknesses of both architectures.

References:

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Keywords:

ETL, Data Lakes, Data Warehouses, Big Data, Data Processing, Data Integration, Data Architecture.