International Journal of Contemporary Research in Multidisciplinary

Abstract:

The integration of Artificial Intelligence (AI) with Data Engineering stands at the forefront of modern data-driven decision-making. This research paper navigates the landscape of best practices in Data Engineering, emphasizing the seamless integration of AI technologies. As organizations strive to unlock the full potential of their data, understanding the fundamentals, real-world applications, challenges, and opportunities becomes paramount. The paper commences with an exploration of the foundational principles of Data Engineering and AI, establishing the groundwork for a cohesive integration. It transitions to an examination of real-world applications, showcasing the synergy between Data Engineering and AI in predictive analytics, e-commerce personalization, healthcare decision support, and cross-industry contexts. Challenges and opportunities are dissected, spanning critical domains such as data quality assurance, scalability, ethical considerations, real-time processing, interdisciplinary collaboration, and security and privacy. The paper provides insights into mitigating challenges and leveraging opportunities as organizations navigate the dynamic interplay between AI and Data Engineering. Highlighting best practices, the paper offers a roadmap for AI and Data Engineering integration. Automated Data Engineering, Explainable AI, interdisciplinary collaboration, AI in data governance, hybrid cloud architectures, and advancements in data security emerge as pillars of successful integration. Each best practice is meticulously examined, providing practical insights for organizations to enhance their data infrastructure. In conclusion, the paper consolidates the key findings and presents a forward-looking perspective. It emphasizes the significance of ongoing advancements, calling for a proactive approach in adopting future directions such as enhanced interpretability, increased automation, interdisciplinary collaboration, and fortified data security. This research paper serves as a comprehensive guide for organizations seeking to optimize their data infrastructure through the effective amalgamation of Data Engineering best practices and AI technologies.

Keywords:

Data Engineering, AI Integration, Natural Language Processing, Machine Learning

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