Citation:

Abhishek Vajpayee, 2023. "The Role of Machine Learning in Automated Data Pipelines and Warehousing: Enhancing Data Integration, Transformation, and Analytics," ESP Journal of Engineering & Technology Advancements 3(3): 84-96.

Abstract:

An increasing amount of data leaves organizations to choose efficient, automated data pipelines and warehousing systems to tackle the surging volume and complexity of data, as sanctioned by the prominence of big data and cloud-based solutions. These systems rely on Machine Learning (ML) techniques for their performance and reliability improvement. This paper studies the interfacing of ML within automated data pipelines and warehousing frameworks by investigating how ML models can optimize data ingestion, data transformation, and data quality assurance processes. ML automates anomaly detection, data cleansing, and transformation processes, freeing humans to produce more accurate, reliable data flow from source to storage. In addition, this study presents case studies of practical ML applications in real data pipelines, which delineate current challenges as well as future directions. As found, machine learning does not just improve operational efficiency and scalability and make decisions more efficiently; it also provides cleaner, more consistent data for analysis.

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Keywords:

Machine Learning, Automated Data Pipelines, Data Warehousing, Big Data, Data Transformation, Anomaly Detection, Data Quality, Data Integration.