Big Data Concept

  Introduction The Internet of Things (IoT) has transformed how devices interact with one another and with humans. From smart homes and industrial automation to connected healthcare and logistics, IoT generates an enormous volume of data every second. However, the sheer velocity, variety, and volume of this data present unprecedented challenges for traditional data management systems. This is where Agentic AI steps in. Unlike conventional AI systems that require predefined instructions, Agentic AI operates with autonomy, adaptability, and the ability to make context-aware decisions in real time. When combined with IoT, it creates a robust ecosystem capable of managing, analyzing, and leveraging massive data streams efficiently. Understanding IoT Data Streams IoT devices—sensors, cameras, wearables, and industrial machines—produce continuous streams of raw data. These streams can include temperature readings, GPS signals, biometric data, traffic conditions, and more. Such da...

  Introduction The proliferation of Internet of Things (IoT) devices and real-time applications has led to an explosion of data generated at the network's edge. Traditional cloud-based big data analytics, where data is sent to centralized servers for processing, often introduces significant latency, bandwidth constraints, and privacy concerns. Edge computing addresses these challenges by processing data closer to its source, enabling faster decision-making and efficient resource utilization. This chapter explores the role of edge computing in big data analytics, focusing on its application in IoT and real-time systems, architectural frameworks, benefits, challenges, and implementation strategies. Understanding Edge Computing in Big Data Analytics What is Edge Computing? Edge computing refers to the decentralized processing of data at or near the source of data generation, such as IoT devices, sensors, or edge servers, rather than relying solely on centralized cloud infrastructu...

  Introduction Big data has transformed industries, economies, and societies, enabling unprecedented insights and efficiencies. However, as we stand at the crossroads of technological evolution, the future of big data is shaped by emerging challenges, innovative trends, and transformative technologies. This chapter explores the hurdles and opportunities that lie ahead, focusing on edge computing, quantum computing, sustainability, AI ethics, and predictions for the next decade. By examining these areas, we aim to inspire and prepare readers for a dynamic and responsible big data ecosystem. Emerging Challenges in Big Data Data Volume and Velocity Overload The exponential growth of data—projected to reach 180 zettabytes by 2025—strains storage, processing, and analytical systems. Real-time applications, such as autonomous vehicles and IoT devices, demand low-latency processing, pushing traditional cloud architectures to their limits. Centralized systems struggle to handle the velo...