Batch Processing with Hadoop Map Reduce: A P...

Batch Processing with Hadoop Map Reduce: A Performance and Scalability Study

Author Name : Govindaiah Simuni

ABSTRACT The continuous generation of large data volumes requires efficient batch processing models for big data in the context of big data analysis. Hadoop MapReduce is the most popular distribution model for multi-data processing, and this study looks into its effectiveness and scalability in the processing of big atomic data sets. The research uses the framework to split raw data into sections that can be analyzed across clustered environments and the issues with large datasets. This paper evaluates Hadoop MapReduce's strengths, weaknesses, opportunities, and threats with assistance from prior research materials and case studies in various fields of application, including e-commerce and smart cities. The research findings show that despite the framework's good prospects regarding scalability, concerns ranging from resource contention and load balancing to inadequate configuration of the framework reduce its effectiveness in large-scale applications. In addition, the research offers practical suggestions for performance and scalability improvement, making it a practical guide for data engineers and organizations desiring to optimize their Hadoop MapReduce applications in batchprocessing scenarios