ETL in Big Data Architectures: Challenges and Solutions

Nishanth Reddy Mandala

Abstract: The Extract, Transform, Load (ETL) process is central to data integration in modern big data architectures. As organizations deal with increasingly larger datasets, managing the movement and transformation of data efficiently becomes a challenge. This paper examines the role of ETL in big data environments, focusing on the challenges posed by the size, speed, and diversity of data. We explore various techniques and technologies used to optimize ETL for big data, such as distributed processing, parallelization, and automation. Realworld examples and case studies are discussed to highlight the evolving nature of ETL in modern data ecosystems.

Keywords: ETL, Big Data, Data Integration, Distributed Processing, Hadoop, Spark, Data Transformation

How to Cite?: Nishanth Reddy Mandala, "ETL in Big Data Architectures: Challenges and Solutions", Volume 13 Issue 10, October 2024, International Journal of Science and Research (IJSR), Pages: 1061-1068, https://www.ijsr.net/getabstract.php?paperid=SR241014054151, DOI: https://dx.doi.org/10.21275/SR241014054151