ETL (Extract, Transform & Load) Automation
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I1P106Keywords:
ETL (Extract, Transform, Load), Scheduling, Automation, AI-driven ETLAbstract
ETL (Extract, Transform, Load) automation is revolutionizing data integration by streamlining the processes of extracting data from various sources, transforming it to fit analytical needs, and loading it into target systems. In an era where data-driven decision-making is paramount, traditional ETL systems face scalability, speed, and efficiency limitations. Automated ETL overcomes these challenges by enabling real-time data processing, reducing manual intervention, and improving overall data quality. This article explores the evolution from traditional to automated ETL, highlighting the benefits of automation, such as scalability, cost efficiency, and consistency. It also delves into key technologies and tools, like AI-driven processes and cloud-native platforms, while addressing challenges such as data security and tool customization. As ETL automation continues to evolve, the integration of AI, low-code/no-code solutions, and serverless architectures promises to make data integration even more accessible and efficient. Organizations that embrace ETL automation will gain a competitive edge in the ever-expanding data landscape
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References
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