International Journal of Contemporary Research in Multidisciplinary

Abstract:

The proliferation of data in contemporary enterprises necessitates advanced analytics approaches to derive meaningful insights and drive informed decision-making. This research paper explores the integration of Artificial Intelligence (AI) into the domain of data engineering to enhance and automate analytical processes. The paper begins with an overview of the evolution of data engineering and the rising significance of analytics in modern organizations. Motivated by the growing need for sophisticated analytical capabilities, the study aims to elucidate the role of AI in transforming traditional analytics methods. The literature review traces the evolution of data engineering and underscores the transformative impact of AI on analytics. Emphasis is placed on machine learning, deep learning, and natural language processing as key components of AI-driven analytics. The research delves into the practical application of these technologies, showcasing their effectiveness in data preprocessing, predictive modeling, complex analysis, and unstructured data interpretation. The methodology section outlines the data collection process, model selection criteria, and the implementation of an end-to-end analytics pipeline that integrates AI models seamlessly into data engineering workflows. Results and discussions present performance metrics, case studies, and interpretations, highlighting the efficacy of AI-driven analytics compared to traditional methods. Challenges and opportunities in the integration of AI into data engineering are systematically examined. Addressing issues related to data privacy, security, interpretability, and bias, the paper explores potential avenues for advancement, including the integration of emerging technologies for enhanced efficiency. In conclusion, this research paper provides a comprehensive exploration of advanced analytics with AI in data engineering. The findings contribute to the evolving landscape of data-driven decision-making, emphasizing the transformative potential of AI in enhancing analytical capabilities. The study concludes with reflections on the implications of the research and proposes future directions for continued advancements in the field.

Keywords:

Data Engineering, Machine Learning, recurrent neural networks, Big Data technologies

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Methods