Abstract
This article conducts a thorough comparative analysis of Apache Tez and MapReduce in the context of big data processing. It focuses on key performance metrics, scalability, and ease of use. The analysis begins with an overview of the architectural distinctions between the two frameworks, emphasizing their fundamental design principles. A detailed performance evaluation follows, considering factors such as execution time, resource utilization, and throughput across diverse workloads. The study explores scalability by examining how Apache Tez and MapReduce respond to increasing data volumes and computational demands. Cluster size effects, resource allocation strategies, and adaptability to dynamic workloads are scrutinized. Additionally, the article evaluates the frameworks' ease of use for developers and administrators, incorporating aspects like programming model simplicity, debugging capabilities, and system configurability. User experiences are gathered through surveys and practical use cases. The conclusions drawn from this analysis offer valuable insights for organizations and practitioners seeking suitable distributed computing frameworks. By addressing both performance and user experience, the article aims to provide a comprehensive perspective on the strengths and weaknesses of Apache Tez and MapReduce, assisting decision-makers in making informed choices for their big data processing requirements.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
