Big Data Concept

  Introduction In today’s digital economy, organizations generate and store data from countless sources: enterprise applications, IoT devices, cloud services, customer interactions, and third-party systems. This data, often vast and heterogeneous, needs to be integrated before it can drive insights. Traditional approaches to data integration—manual ETL (Extract, Transform, Load) processes, rule-based pipelines, and custom scripts—are time-intensive, error-prone, and lack adaptability. Agentic AI , a new paradigm of autonomous and proactive artificial intelligence, is transforming this landscape. By automating integration processes, Agentic AI reduces human intervention, ensures data consistency, and enables real-time decision-making in big data platforms. Challenges in Traditional Data Integration Complexity of Sources – Data comes in structured, semi-structured, and unstructured formats. Scalability Issues – Manual pipelines often fail to handle petabyte-scale work...

  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 The explosion of big data has transformed industries, enabling organizations to harness vast amounts of information for strategic decision-making. However, the complexity and scale of big data workflows—encompassing data collection, processing, analysis, and visualization—pose significant challenges. Agentic AI, characterized by its autonomy, adaptability, and goal-oriented behavior, is emerging as a transformative force in managing these workflows. Unlike traditional AI, which relies on predefined rules or supervised learning, Agentic AI systems can independently reason, learn, and make decisions, making them ideal for dynamic and large-scale data environments. This chapter explores the real-world applications of Agentic AI in big data workflows, highlighting its impact across industries such as healthcare, finance, retail, and more. Understanding Agentic AI in Big Data Contexts Agentic AI refers to systems that exhibit agency—autonomous decision-making, environm...