Big Data Concept

  Introduction In an era where data is often hailed as the new oil, organizations face mounting pressures to manage it effectively while adhering to stringent regulatory frameworks. Data governance encompasses the policies, processes, and technologies that ensure data is accurate, available, secure, and compliant with legal standards. Compliance, on the other hand, involves aligning these practices with laws such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the US, or sector-specific mandates like HIPAA for healthcare. Enter agentic AI—autonomous systems capable of perceiving their environment, reasoning about tasks, planning actions, and executing them with minimal human intervention. Unlike traditional AI, which is reactive and rule-based, agentic AI operates proactively, adapting to dynamic scenarios through goal-oriented behavior. This chapter explores how agentic AI is revolutionizing data governance and complia...

  Introduction The convergence of big data and cloud computing has transformed how organizations handle massive datasets, offering scalability, cost-efficiency, and real-time analytics. Cloud platforms like AWS, Azure, and Google Cloud enable storage and processing of petabytes of data across distributed environments. However, this integration introduces significant security vulnerabilities, from misconfigurations to sophisticated cyber threats, potentially leading to data breaches that compromise sensitive information. In 2025, with data volumes projected to exceed 180 zettabytes globally, securing big data in the cloud is paramount for industries such as healthcare, finance, and e-commerce. This chapter examines key vulnerabilities in cloud-based big data systems and strategies to mitigate them. We explore foundational concepts, security techniques, real-world applications, challenges, and emerging trends. By adopting robust security frameworks, organizations can harness big...

  Introduction Big data has transformed industries by enabling unprecedented insights, predictive capabilities, and operational efficiencies. However, its real-world implementation introduces significant challenges in security, privacy, and ethics. The scale, variety, and velocity of big data amplify risks related to unauthorized access, data breaches, and ethical misuse. This chapter explores these challenges and provides practical strategies for responsible implementation, emphasizing safeguards, regulatory compliance, and ethical considerations. By addressing these issues, organizations can harness big data's potential while mitigating risks and fostering trust. 4.1 Big Data Security: Risks and Safeguards Understanding Security Risks Big data environments are prime targets for cyberattacks due to the volume and value of data they store. Key risks include: Data Breaches : Unauthorized access to sensitive datasets, such as personal or financial information, can lead to signifi...