Integrating AI with Data Engineering Pipelines: Enhancing Decision-Making in Real-Time Systems
Keywords:
AI Integration, Data Engineering Pipelines, Real-Time Decision-Making, Predictive Analytics, Anomaly Detection, Latency Optimization, Streaming Data ProcessingAbstract
The integration of Artificial Intelligence (AI) with data engineering pipelines is transforming
decision-making processes in real-time systems. This paper explores the synergistic potential of
AI-driven techniques in optimizing data flows, enhancing the accuracy of predictive models, and
ensuring timely insights for critical operations. By embedding AI algorithms within data pipelines,
organizations can process large volumes of streaming data, detect anomalies, and make dynamic
decisions with minimal latency. The paper also delves into the challenges of scalability, data
consistency, and latency reduction in real-time environments, highlighting key strategies for
integrating AI into traditional data engineering frameworks. The results demonstrate how this
integration improves system efficiency, decision-making precision, and responsiveness in
industries such as finance, healthcare, and manufacturing
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