Optimization of ETL Process in Data Warehouse Through a Combination of Parallelization and Shared Cache Memory

Authors

  • M. Faridi Masouleh Information Technology Management Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • M. A. Afshar Kazemi Information Technology Management Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • M. Alborzi Information Technology Management Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • A. Toloie Eshlaghy Information Technology Management Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
Volume: 6 | Issue: 6 | Pages: 1241-1244 | December 2016 | https://doi.org/10.48084/etasr.849

Abstract

Extraction, Transformation and Loading (ETL) is introduced as one of the notable subjects in optimization, management, improvement and acceleration of processes and operations in data bases and data warehouses. The creation of ETL processes is potentially one of the greatest tasks of data warehouses and so its production is a time-consuming and complicated procedure. Without optimization of these processes, the implementation of projects in data warehouses area is costly, complicated and time-consuming. The present paper used the combination of parallelization methods and shared cache memory in systems distributed on the basis of data warehouse. According to the conducted assessment, the proposed method exhibited 7.1% speed improvement to kattle optimization instrument and 7.9% to talend instrument in terms of implementation time of the ETL process. Therefore, parallelization could notably improve the ETL process. It eventually caused the management and integration processes of big data to be implemented in a simple way and with acceptable speed.

Keywords:

Shared cache memory, ETL Process, parallelization, ETL optimization

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References

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How to Cite

[1]
Faridi Masouleh, M., Afshar Kazemi, M.A., Alborzi, M. and Toloie Eshlaghy, A. 2016. Optimization of ETL Process in Data Warehouse Through a Combination of Parallelization and Shared Cache Memory. Engineering, Technology & Applied Science Research. 6, 6 (Dec. 2016), 1241–1244. DOI:https://doi.org/10.48084/etasr.849.

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