Volume 16, Number 6

Exploring Fault Tolerance Strategies in Big Data Infrastructures and Their Impact on Processing Efficiency

  Authors

Akaash Vishal Hazarika ¹ and Mahak Shah ², ¹ North Carolina State University, USA, ² Columbia University, USA

  Abstract

As big data systems continue to grow in scale and complexity, ensuring fault tolerance while maintaining processing efficiency has become increasingly challenging. This paper presents a comprehensive analysis of fault tolerance strategies in distributed big data infrastructures, examining three primary approaches: replication, checkpointing, and consensus algorithms. Through a systematic review of current implementations and case studies across major platforms including Apache Hadoop, Spark, and Flink, we evaluate the performance implications of these strategies using key metrics such as throughput, latency, and resource utilization. Our analysis reveals significant trade-offs between reliability and performance, particularly in write-intensive workloads where replication factors directly impact system performance. The paper also examines how different architectures, including client-server, peer-to-peer, and service-oriented models, influence the effectiveness of fault tolerance mechanisms. Based on our findings, we propose recommendations for implementing fault tolerance at scale and identify emerging research directions, including the integration of machine learning for adaptive fault tolerance and the potential of hybrid approaches in managing the reliability-performance balance. This research contributes to the understanding of how organizations can optimize their fault tolerance strategies while maintaining acceptable processing efficiency in large-scale distributed systems.

  Keywords

Fault Tolerance, Big Data, Distributed Systems, Replication, Checkpointing, Consensus Algorithms