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@article{180308,
        author = {Rajesh Sura},
        title = {Scalable AI-Powered Data Pipelines for Enterprise Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1094-1101},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180308},
        abstract = {Amid ongoing digital transformations, enterprises are generating and accumulating structured and unstructured data at an unprecedented scale. However, for value out of this data, the data must call to be scaled in an intelligent way and through automation, ingest, process, and analyze in real-time. AI-empowered data pipelines are a significant technological advancement, serving as the backbone for real-time data analytics and operational intelligence by enabling automated, scalable, and intelligent data processing across diverse enterprise environments. Without sacrificing the importance of data engineering and machine learning, it combines these two in a way such that one can use these pipelines to support different applications, such as customer behaviour prediction, anomaly detection, and a personalized recommendation system. And so this is a review of those ground principles, current tools, possible architectures, and operational speed bumps of such an AI-based data pipeline. All these are explored in terms of state-of-the-art frameworks, experimental case studies, and evolving best practices, and their insights are provided in terms of actionable insights for researchers, architects, and enterprise practitioners designing resilient and intelligent analytics systems.},
        keywords = {AI-Powered Pipelines; Big Data Infrastructure; Data Engineering; Enterprise Analytics; MLOps; Model Lifecycle; Real-Time Analytics; Scalable Architecture; Stream Processing; Workflow Automation.},
        month = {June},
        }