International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 72 | Issue 10 | Year 2024 | Article Id. IJCTT-V72I10P105 | DOI : https://doi.org/10.14445/22312803/IJCTT-V72I10P105

Scaling Data Engineering with Advanced Data Management Architecture: A Comparative Analysis of Traditional ETL Tools Against the Latest Unified Platform

Paulami Bandyopadhyay

Received Revised Accepted Published
20 Aug 2024 24 Sep 2024 09 Oct 2024 23 Oct 2024

Citation :

Paulami Bandyopadhyay, "Scaling Data Engineering with Advanced Data Management Architecture: A Comparative Analysis of Traditional ETL Tools Against the Latest Unified Platform," International Journal of Computer Trends and Technology (IJCTT), vol. 72, no. 10, pp. 22-30, 2024. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V72I10P105

Abstract

This technical article critically examines the strengths and advantages of the modern data analytics platform in comparison to legacy ETL (Extract, Transform, Load) tools through an in-depth exploration of key features, performance, scalability, ease of use, and integration capabilities. This article will highlight how the advancement in technology has resulted in offering superior capabilities in terms of real-time processing, scalability, agility, advanced analytics, unified platforms, governance, security, deployment flexibility, cost efficiency, and ecosystem integration compared to traditional ETL processes. These advantages make them crucial for organizations aiming to maximize the value of their data assets and foster innovation in their business operations. There are diverse options available for implementing a new data pipeline or upgrading the existing orchestration mechanism. This article tends to do a thorough analysis by carefully evaluating these benefits, weaknesses, and considerations. Researchers and industry specialists can make informed decisions about selecting the appropriate ETL components or considering alternative data integration and processing approaches (such as ELT, streaming data platforms, or modern data analytics tools) that better suit their specific requirements and use cases.

Keywords

BI Tools, Data management, Data visualization, ETL, Unified Platform.

References

[1] Asma Qaiser et al., “Comparative Analysis of ETL Tools in Big Data Analytics,” Pakistan Journal of Engineering and Technology, vol. 6, no. 1, pp. 7-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Matt Palmer, Understanding ETL Data Pipelines for Modern Data Architectures, O Reilly, pp. 1-107, 2024. [Online]. Available: https://www.databricks.com/sites/default/files/2024-03/oreilly-technical-guide-understanding-etl.pdf
[3] J. Sreemathy et al., “Overview of ETL Tools and Talend-Data Integration,” 7 th International Conference on Advanced Computing and Communication Systems, Coimbatore, India, pp. 1650-1654, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] How to Compare ETL Tools, Fivetran, 2021. [Online]. Available: https://www.fivetran.com/blog/how-to-compare-etl-tools
[5] What is Business Analytics?., IBM. [Online]. Available: https://www.ibm.com/topics/business-analytics?utm_content=SRCWW&p1=Search&p4=43700068092238946&p5=p&p9=58700007560265559&gclid=CjwKCAjwooq3Bh B3EiwAYqYoEhbifHHnHcdoNzRE6pudsL0D8kERH79pjjJ93L54vZ9x1nNSaC0gJRoCYFkQAvD_BwE&gclsrc=aw.ds
[6] Ethan, ETL Tool Comparison Matrix: Costs, Features & FAQs, Portable, 2023. [Online]. Available: https://portable.io/learn/etl-tool-comparison-matrix
[7] Mark Smallcombe, Real-Time ETL: Evolving from Batch ETL to Streaming Pipelines, Integrate, 2020. [Online]. Available: https://www.integrate.io/blog/real-time-etl/
[8] Types of Data Integration Project. [Online]. Available: https://tdwi.org/~/media/TDWI/TDWI/Miscellaneous/2002/08/TDWI_Data_Integration_Webcast%20pdf.ashx
[9] Building an ETL Process that Fits Your Business Requirements, Altoros. [Online]. Available: https://www.altoros.com/blog/building-etl-that-fits-your-business-requirements/