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 the era of big data, the exponential growth of information generated from various sources has revolutionized industries, particularly healthcare. Electronic health records (EHRs), wearable devices, genomic data, and telemedicine platforms produce vast datasets that enable advanced analytics, personalized medicine, and improved patient outcomes. However, this abundance of data comes with significant privacy risks. Sensitive information, such as medical histories, genetic profiles, and personal identifiers, can be exploited if not adequately protected, leading to identity theft, discrimination, or unauthorized surveillance. Anonymization techniques serve as a cornerstone for safeguarding privacy in big data environments. These methods aim to remove or obscure personally identifiable information (PII) while preserving the utility of the data for analysis. This chapter delves into the principles, methods, and applications of anonymization in large-scale systems, ...