Map Reduce Applications | Cloud and Distributed Computing | SNS Institutions
Автор: Kanchana. M
Загружено: 2025-12-17
Просмотров: 13
In this video, I explain the need for MapReduce in processing large-scale data and why traditional systems are not sufficient for big data environments. The lecture covers key applications of MapReduce, such as log processing, data mining, web indexing, and batch analytics, and also discusses where MapReduce is not suitable, including real-time processing, interactive queries, and iterative computations due to higher latency.
In this video, I also summarize the core concepts of MapReduce, including the Map and Reduce phases, distributed execution, scalability, and fault tolerance. The session helps students understand when MapReduce is the right choice and how it forms the foundation for modern cloud and big data processing frameworks.
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