Using machine learning to automate the ETL pipelines and data integration helps companies manage vast amounts.
Keywords:
Data integration, ETL pipelines, machine learning, enterprise data management, data extractionAbstract
Since companies rely more & more on huge amounts of the data to guide strategic decisions, maintenance & the integration of these massive databases have become major challenges for modern companies. Conventional ETL (Extract, Transform, Load) pipelines often find it difficult to expand effectively while being fundamental for data processing because of the growing complexity, volume & variety of data. Including machine learning into ETL pipelines offers a strong fix for this problem, thereby enabling data operations to be automated & so enhancing the general scalabilities & efficiency of the data integration processes. Important for maintaining high-quality, consistent data throughout the pipeline, complex operations such anomaly detection, schema matching & the data transformation may be automated by companies using ML algorithms. Moreover, actual time data processing made possible by ML helps companies to examine & react to data as it is generated, therefore guaranteeing more timely & informed decision-making. Emphasizing how ML-driven automation may greatly lower human participation, enhance data quality & increase the general effectiveness of data integration systems, this paper investigates the revolutionary capability of ML in reinventing standard ETL techniques. The practical challenges of implementing the ML in enterprise-scale data pipelines including the necessity of high-quality labeled data, model training & the resolution of integration issues are discussed in this study. It looks at how machine learning affects the many steps of ETL—data extraction, transformation, and loading among others.
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