The Role of Generative AI in Transforming Data Engineering Workflows and Automating Computational Infrastructure Design
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I6P101Keywords:
Generative AI, Data Engineering, Computational Infrastructure, Workflow Automation, Predictive Modeling, Large Language Models, GANs, Resource OptimizationAbstract
Generative Artificial Intelligence (AI) has been a disruptive technology in various fields, with its use in data engineering processes and design of computational infrastructure receiving a great deal of interest. The old method of data engineering comprised manual data ingestion, data cleaning, transforming and managing data storage. These are resource-rich activities which are susceptible to human error and can only be done effectively when the domain expert is around. Generative AI provides automation, predicting features, and smart architecture, which puts the possibility to accelerate data pipelines, to optimize computational resources to optimize decision-making processes. This paper examines how large language models, generative adversarial networks, and diffusion models, which are generative artificial intelligence systems, can be applied to data engineering practices. We conduct a review of the existing literature concerning the AI-based automation in the infrastructure design, provide the overview over the methodology of Workflow transformation, and provide the case studies to illustrate efficiency improvement, enlargement of possibilities, and cost decrease. Moreover, the paper establishes a generative AI embedding scheme in data pipelines, such as automatic data coding, predictive data transformation, anomalies detection, and resource allocation. This analysis implies that the use of generative AI prevents not only the optimization of certain routine activities but also offers smart suggestions concerning the optimization of infrastructure. We do this by presenting comparative analyses, which demonstrate the quantitative and qualitative advantages of adopting generative AI in enterprise data setting. Finally, the paper finds that the generative AI has the potential to restructure manual processes in data engineering and allow companies to utilize data more efficiently minimizing operational costs and complexity of design
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