Harnessing AI for Autonomous Data Engineering: Streamlining Data Integration and ETL Processes
The increasing sophistication of data systems requires higher levels of automation of data engineering that includes data integration and ETL. Traditional ETL processes fail to meet the volume, variety, and velocity demands required for data processing, causing delays and many shortcomings. In this article, it is posited that AI is set to revolutionalise data engineering through the automation and enhancement of these vital and complex procedures. Some of the application of machine learning algorithm and AI model to data integration and ETL process entails the following; Self-driving: This will ensure that the data integration process is intelligent and less dependent of the human touch and expertise hence improving the quality of data feeding the ETL system Self-optimizing: The use of AI to enhance the ETL system enables it to self optimize and adapt to the new changes hence improving its performance. For this study, different case studies from the financial sector, the healthcare sector, as well as the electronic commerce sector are considered to highlight the role of AI in the enhancement of data engineering. The work further confirms that AI in the ETL processes enhances the velocity of data transformation whilst it offers more exact and consistent data in a shorter time hence faster decision making. In this paper, we indicate how AI has the possibilities of enhancing data engineering and bring more efficiency and scalability in the future. It briefly reviews the issues that come with trying to apply AI techniques to data engineering pipeline and outlines the possible directions for future work aimed at improving the automation of data integration and ETL tasks.
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