Dimensions of Automated ETL Management: A Contemporary Literature Review

ETL solutions are becoming more widely used because of the increasing complexity of data systems and the importance of high-quality data sources and data processing for making decisions. The raw data is retrieved and placed into a designated warehouse for efficient information analysis and processing. Using data management technologies to acquire insights into functional and operational elements is one of the critical components of software engineering in the present environment. There is a need to improve ETL performance for real-time applications. However, several scholarly and business investigations have been into the efficacy of data management dynamics and ETL technologies. As a result, it is essential to look at methods of increasing the effectiveness of ETL processes to meet the immediate demands of operations. This paper explores existing ETL systems' history, constraints, potential, and how machine learning models are integrated into ETL processes. However, as evidenced by a review of relevant academic literature, there is widespread support for using machine learning models to improve and perfect ETL applications. Although many machine learning models can be helpful in ETL processing, only a tiny fraction of market products use them all. Machine learning-based models optimize ETL-based data management, concentrating on limits and future potential.