Vol. 1 No. 1 (2021): Journal of AI-Assisted Scientific Discovery
Articles

Automated Data Pipeline Creation: Leveraging ML algorithms to design and optimize data pipelines

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  • Muneer Ahmed Salamkar,
  • Jayaram Immaneni
Muneer Ahmed Salamkar
Senior Associate at JP Morgan Chase, USA
Jayaram Immaneni
Sre Lead, JP Morgan Chase, USA
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Published 02-06-2021

Keywords

  • Data engineering automation,
  • ML-driven ETL processes
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Muneer Ahmed Salamkar and Jayaram Immaneni, “Automated Data Pipeline Creation: Leveraging ML algorithms to design and optimize data pipelines”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 230–250, Jun. 2021, Accessed: Dec. 23, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/220

Abstract

Automated data pipeline creation, powered by machine learning (ML) algorithms, significantly transforms how businesses design, manage, and optimize their data workflows. Traditionally, building and maintaining data pipelines is a manual, time-consuming, & error-prone task that requires constant adjustments to accommodate changes in data sources, formats, and processing needs. This traditional approach can lead to inefficiencies and delays, particularly as the volume and complexity of data continue to grow. With the integration of ML, businesses can automate the pipeline creation and optimization process, drastically reducing the time, effort, and cost involved. ML algorithms analyze historical data to identify patterns and trends, using advanced techniques such as reinforcement learning to enhance the design and performance of data pipelines continuously. As a result, these pipelines become adaptive and self-optimizing, automatically adjusting to new data requirements without manual intervention. The ability to detect bottlenecks, predict potential issues, & suggest performance improvements further enhances pipeline efficiency, scalability, and reliability. ML-powered pipelines also possess the ability to self-correct, address problems before they cause significant disruptions or downtime, and ensure seamless and uninterrupted data flow. This self-correction feature is crucial in maintaining high reliability and minimizing the risk of system failures. Additionally, ML models provide real-time feedback that allows businesses to fine-tune their data pipelines continuously, keeping them resilient to changes in data sources or volume. This adaptability ensures that data pipelines can scale with the growing demands of data processing & analysis. Businesses benefit from streamlined workflows, reduced operational costs, improved scalability, and enhanced insights, ultimately empowering faster, data-driven decision-making. By leveraging ML in data pipeline creation, organizations can stay ahead of the curve in today’s fast-paced, data-centric world.

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