Building Intelligent Data Lakes: Leveraging AI in Data Engineering for Better Insights
Keywords:
Artificial Intelligence (AI), Data Lakes, Data Engineering, Machine Learning, Predictive AnalyticsAbstract
The proliferation of data in today’s digital age necessitates the development of intelligent data
management solutions that can efficiently handle, process, and analyze vast quantities of
information. This paper explores the integration of Artificial Intelligence (AI) with data
engineering to build intelligent data lakes that offer enhanced data processing capabilities and
deeper insights. Intelligent data lakes leverage AI to optimize data ingestion, storage, and retrieval
processes while employing advanced analytics and machine learning techniques to uncover
actionable insights. This study investigates the key components of AI-driven data lakes, including
automated data integration, real-time analytics, and predictive modeling. By examining case
studies and real-world applications, we illustrate how AI can transform traditional data lakes into
dynamic and intelligent systems capable of supporting complex decision-making processes. The
paper also discusses the challenges and best practices for implementing AI-driven data lakes,
highlighting the importance of data quality, scalability, and security. The findings provide valuable
guidance for organizations seeking to harness the power of AI to enhance their data engineering
practices and derive more meaningful insights from their data assets.
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