Optimizing Big Data Analytics for Reliability and Resilience: A Survey of Techniques and Applications

Abstract

The advent of big data has revolutionized various industries, enabling organizations to make data- driven decisions and gain valuable insights. However, the sheer volume, velocity, and variety of big data pose significant challenges in ensuring the reliability and resilience of big data analytics pipelines. In this context, optimization techniques play a crucial role in enhancing the reliability and resilience of big data analytics. This paper provides a comprehensive survey of optimization techniques for reliable and resilient big data analytics. The paper first discusses the motivation for optimizing big data analytics for reliability and resilience. Then, it presents a detailed overview of various optimization techniques, including resource optimization, data partitioning, data compression, load balancing, and fault detection and tolerance. Finally, the paper discusses the applications of optimization techniques in various big data analytics domains, such as real-time analytics, fraud detection, recommendation systems, predictive analytics, and risk management.