Automating the data integration and ETL pipelines through machine learning to handle massive datasets in the Enterprise
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V1I2P109Keywords:
Data integration, ETL pipelines, machine learning, automation, massive datasets, enterprise data management, real-time processing, anomaly detection, schema matching, data quality, data transformation, data extraction, data loading, scalability, data governance, machine learning algorithms, model training, data labeling, workflow optimization, real-time analytics, data pipelines, data consistency, enterprise-scale applications, data processing, automated data workflowsAbstract
As organizations rely more & more on huge amounts of information to make these strategic decisions, managing & combining these huge datasets has become a major issue for their modern companies. Conventional ETL (Extract, Transform, Load) pipelines are important for processing their information, but they generally have trouble scaling well as data becomes more sophisticated, huge & varied. Adding machine learning (ML) to ETL pipelines is a strong way to solve this problem. It makes it easier to automate these data operations and makes data integration processes more efficient & scalable overall. Organizations may use ML algorithms to automate more complex tasks like schema matching, anomaly detection & the data transformation. These tasks are important for keeping the information in the pipeline high-quality & more consistent. ML also lets firms handle their information in actual time, which means they can study & react to data as it is created. This makes sure that decisions are made more quickly & with more information. This study talks about how machine learning might make a major difference in how ETL works. It talks about how machine learning-powered automation might drastically reduce the requirement for human involvement, improve the quality of data, and make data integration systems perform better overall. The paper talks on the actual world problems of using ML in huge scale data pipelines, such as the requirement for well labeled information, model training & fixing their integration problems. It looks at how ML affects the different steps of ETL, such as loading, transforming & extracting their information. It speaks about the prospective advantages of utilizing ML, such quicker processing speeds, more accurate data, and improved scalability. ML lets us automate and make ETL processes work better in the end. This makes them better able to satisfy the demands of contemporary data-driven enterprises that are always changing, while yet following tight rules for data quality and control
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