ETL Best Practices : Transforming Raw Data into Business Insights

Authors

  • N V Rama Sai Chalapathi Gupta Lakkimsetty   Independent Researcher, USA

Keywords:

ETL, Data Transformation, Data Warehousing, Big Data, AI-Driven ETL, Cloud Computing, Data Governance

Abstract

Extract, Transform, Load (ETL) processes play a critical role in modern data management, enabling organizations to extract raw data, transform it into meaningful formats, and load it into analytical systems for business insights. With the advent of big data, cloud computing, and AI-driven analytics, ETL has evolved significantly. This paper explores best practices in ETL processes, discussing key strategies for optimizing data extraction, transformation, and loading. The research provides insights into modern ETL architectures, including ELT, data mesh, and serverless ETL solutions, while highlighting challenges related to security, compliance, and performance scalability.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-07-14

Issue

Volume 9, Issue 4, July-August-2022

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
N V Rama Sai Chalapathi Gupta Lakkimsetty "ETL Best Practices : Transforming Raw Data into Business Insights" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 4, pp.533-546, July-August-2022.