International Journal of Contemporary Research in Multidisciplinary

Abstract:

The convergence of edge computing and artificial intelligence (AI) has emerged as a transformative paradigm in modern data engineering. This research paper explores the intricate interplay between edge computing and AI, unraveling their synergistic impact on data engineering workflows. With the proliferation of Internet of Things (IoT) devices generating vast amounts of data at the edge, coupled with the evolution of sophisticated AI algorithms, a new frontier in data processing and analytics has unfolded. The paper navigates through the fundamental principles of edge computing and AI, shedding light on their individual strengths. It delves into the pivotal role of edge devices, particularly IoT endpoints, in shaping the landscape of edge computing for data engineering. Concurrently, the paper investigates the diverse applications of AI in data engineering, encompassing machine learning, predictive analytics, natural language processing, and image recognition. As the exploration deepens, the integration of edge computing and AI within the realm of data engineering is scrutinized. The paper scrutinizes how edge-based AI processing redefines traditional data processing pipelines, influencing data preprocessing, transformation, and loading (ETL) processes. Real-world applications from various industries illuminate successful instances of this integration, providing tangible evidence of its impact. However, the journey is not without challenges. The paper identifies and addresses concerns related to data security, privacy, scalability, and resource management in the context of edge computing and AI integration. Strategies for mitigating risks and ensuring compliance with regulations are discussed. Looking forward, the paper contemplates future directions and emerging trends that are poised to shape the landscape of edge computing and AI in data engineering. It explores the potential integration of blockchain, 5G, and other cutting-edge technologies in this dynamic space. Ethical considerations and responsible AI practices are emphasized, underscoring the importance of transparency and accountability in the era of data-driven decision-making. In conclusion, the paper synthesizes key findings, insights, and recommendations, painting a comprehensive picture of how the fusion of edge computing and AI is redefining the boundaries of modern data engineering and charting new territories of efficiency, scalability, and intelligence.

Keywords:

Computing, Modern Data Engineering, 5G, Internet of Things, AI algorithm

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