Big Data Concept

  Introduction In today’s data-driven world, the majority of enterprise data is unstructured—ranging from emails, social media posts, videos, audio files, IoT sensor streams, to customer feedback. Unlike structured data, which fits neatly into databases and tables, unstructured data lacks a predefined model, making it harder to analyze using traditional methods. Big data systems must therefore evolve beyond storage and retrieval to intelligent interpretation. Agentic AI—a new paradigm of artificial intelligence where autonomous, goal-directed AI agents manage complex workflows—emerges as a powerful solution for handling unstructured data effectively. The Challenge of Unstructured Data in Big Data Ecosystems Organizations generate massive volumes of unstructured data daily, but only a small fraction is analyzed for insights. Key challenges include: Volume and Velocity: The continuous influx of large-scale data streams from diverse sources. Variety: Different data forma...

  Introduction In the rapidly evolving landscape of artificial intelligence (AI), the emergence of agentic AI represents a paradigm shift from passive tools to proactive entities capable of independent action. Agentic AI systems, often referred to as AI agents, are designed to perceive their environment, reason about goals, plan sequences of actions, and execute them autonomously. This chapter delves into the integration of agentic AI within data-driven enterprises—organizations that leverage vast amounts of data to inform strategy, operations, and innovation. As businesses increasingly rely on data analytics, machine learning, and real-time insights, agentic AI offers a pathway to autonomous decision-making, enabling enterprises to operate with unprecedented efficiency, agility, and intelligence. The chapter explores the foundational concepts of agentic AI, its synergy with data-driven frameworks, practical applications, benefits, challenges, and future implications. By exami...