Distributed Data Processing Techniques In Cloud Systems

Authors: C. N. R. Rao

Abstract: Distributed data processing techniques in cloud systems have become fundamental for managing and analyzing large-scale data generated by modern applications. With the exponential growth of data from social media, IoT devices, enterprise systems, and web applications, traditional centralized processing approaches are no longer sufficient. Cloud-based distributed processing frameworks enable scalable, efficient, and fault-tolerant handling of massive datasets by distributing computational tasks across multiple nodes. This study explores key distributed processing models such as MapReduce, stream processing, batch processing, and in-memory computing. It also examines widely used frameworks including Hadoop, Spark, and Flink, highlighting their architectures and performance characteristics. The paper discusses how cloud environments support elasticity, parallelism, and high availability for large-scale data processing tasks. Additionally, it addresses challenges such as data consistency, network latency, fault tolerance, and resource optimization. Emerging trends such as serverless computing, edge-cloud collaboration, and real-time analytics are also reviewed. The findings emphasize that distributed data processing is essential for enabling efficient big data analytics, supporting scalable applications, and driving data-driven decision-making in cloud systems.

DOI: http://doi.org/