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AI-Driven Data Mesh with AutoML for Enterprise Analytics

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  • Lingareddy Alva

    (IT Spin Inc, USA)

Abstract

This article explores the transformative potential of AI-driven Data Mesh architectures for enterprise analytics. By reimagining traditional centralized data structures through domain-driven ownership principles, organizations can achieve unprecedented scalability and agility. The implementation leverages Databricks Unity Catalog and Delta Sharing for federated governance while maintaining domain autonomy. At its core, an AutoML-powered Data Quality Engine ensures data integrity through machine learning capabilities that detect anomalies, impute missing values, and generate explainable reports. Event-driven pipelines built with Apache Kafka and Delta Live Tables enable real-time insights and forecasting, allowing businesses to respond immediately to changing conditions. This architectural paradigm empowers enterprises to move beyond static reporting toward autonomous data-driven operations with intelligent insights and seamless cross-domain collaboration.

Suggested Citation

  • Lingareddy Alva, 2025. "AI-Driven Data Mesh with AutoML for Enterprise Analytics," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(4), pages 750-756, May.
    • Handle: RePEc:alk:jcsts0:v:7:y:2025:i:4:id:9627
    DOI: 10.32996/jcsts.2025.7.4.87
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