AI-powered big data platforms for enterprise analytics

Abstract

This article presents a comprehensive analysis of AI-powered big data platforms that are revolutionizing enterprise- scale analytics across industries. The article examines the architectural evolution from traditional data warehouses to modern lakehouse paradigms, detailing how artificial intelligence integration transforms core data platform capabilities, including ingestion, storage, processing, and security. The article demonstrates quantifiable performance improvements, with organizations achieving reductions in processing time and cost efficiency gains compared to conventional systems. Through detailed case studies spanning cybersecurity, cloud cost optimization, IT infrastructure observability, and financial intelligence applications, the article illustrates how these platforms enable real-time decision-making, automated anomaly detection, and predictive insights that were previously unattainable. The article provides empirical performance analyses across varying workloads and implementation environments, documenting both technical metrics and strategic business impacts. The article concludes by identifying emerging research directions, including self-learning AI models, ultra-low-latency processing architectures, and federated analytics paradigms that will shape the next generation of enterprise data platforms. This article contributes a holistic framework for understanding how AI-integrated data platforms are transforming enterprise operations from reactive cost centers into proactive engines of innovation and competitive advantage.