Testing of Extract Load and Transform (ETL) in Assorted Dimensions and Perspectives: A Data Science Integration Approach

Abstract

The presented work can be elevated to the effectiveness with integration of soft computing and metaheuristic approaches so that the overall performance can be improved. Working in the ever-evolving technical field, we are constantly immersed in the world of data science. The field is growing rapidly, and data science is closely related to data mining. However, data mining requires a data warehouse to be in place. If we want to create a data warehouse, we'll need to go through the process of Extract, Load, and Testing (ETL). ETL involves extracting data from different sources, transforming the extracted data into the correct format, and then loading it into a data warehouse. Integrating data science with the ETL is crucial for achieving optimal performance. Furthermore, achieving optimal performance is crucial for conducting accurate testing. By integrating soft computing and metaheuristic approaches, we can enhance the effectiveness of the presented work and improve its overall performance.