Big Data Concept

  Introduction In the era of big data, organizations are inundated with vast amounts of information from diverse sources, ranging from structured databases to unstructured streams like social media and IoT devices. Data lakes have emerged as a scalable solution for storing this raw data in its native format, allowing for flexible analysis without predefined schemas. However, managing these repositories at scale presents significant challenges, including data quality issues, governance, and efficient retrieval. Enter agentic AI—a paradigm shift in artificial intelligence where autonomous agents can reason, plan, and execute tasks independently. Unlike traditional AI models that respond reactively, agentic AI systems act proactively, adapting to dynamic environments. When integrated with data lakes, agentic AI streamlines large-scale data management by automating ingestion, processing, governance, and analytics. This chapter explores the synergy between agentic AI and data lakes...

  Introduction The exponential growth of data in modern systems has made real-time anomaly detection a critical capability across industries such as finance, healthcare, cybersecurity, and manufacturing. Traditional methods often struggle with the scale, speed, and complexity of big data environments. Agentic AI, characterized by autonomous, goal-oriented systems capable of reasoning and decision-making, offers a transformative approach. This chapter explores the principles, architectures, and applications of Agentic AI for real-time anomaly detection in big data, highlighting its advantages over conventional methods and addressing challenges and future directions. Understanding Agentic AI Agentic AI refers to intelligent systems that operate autonomously, make decisions based on environmental inputs, and adapt to achieve specific goals. Unlike traditional AI, which often relies on predefined rules or supervised learning, Agentic AI leverages advanced reasoning, planning, and le...

  Introduction In the era of big data, where organizations grapple with massive volumes of information generated at unprecedented speeds, artificial intelligence (AI) technologies have become indispensable for extracting value and driving decisions. Traditional machine learning (ML) has long been the cornerstone of data analysis, enabling predictive modeling and pattern recognition. However, the emergence of agentic AI represents a paradigm shift toward more autonomous, goal-oriented systems capable of handling complex, dynamic environments. This chapter explores the definitions, differences, advantages, challenges, and applications of agentic AI compared to traditional ML in big data contexts, highlighting how these technologies are transforming industries. Understanding Traditional Machine Learning Traditional machine learning encompasses algorithms that learn from data to make predictions or decisions without being explicitly programmed for each task. It includes supervise...

  Introduction In the era of digital transformation, big data has become the lifeblood of organizations across industries. With the exponential growth of data from sources like social media, IoT devices, sensors, and transactional systems, traditional analytical methods often fall short in extracting meaningful insights efficiently. Predictive analytics, which involves using historical data, statistical algorithms, and machine learning techniques to forecast future outcomes, has emerged as a critical tool for decision-making. However, the sheer volume, velocity, variety, and veracity of big data pose significant challenges. Enter agentic AI—autonomous intelligent systems capable of perceiving their environment, making decisions, and taking actions to achieve specific goals without constant human intervention. Unlike traditional AI, which is often reactive or rule-based, agentic AI embodies agency, allowing it to adapt, learn, and optimize in dynamic settings. This chapter delv...