Abstract: In this big data world we find daily based data which are generated day by day e.g. google, amazon, Facebook etc. This large amount of data is un-reliable to store, manage and analyze. This large volume of data runs on Commodity hardware, which is parallel arranged. The data that are large in volume takes more time to execute for particular process and causes fail to distributed system because these huge volume data runs on distributed system. To overcome this issue a framework was proposed called HADOOP for big data processing and is being used now days in organizations. It process large amount of data in small amount time rather than large distributed systems. It has automatic fault tolerance capacity to tackle with failed of systems in execution or processing time using Map Reduce programming technique. Here execution time is still an issue for delivering large amount of data and processing it repeatedly for particular oracle. Existing HADOOP system does not have any feature to reduce time for recompilation. We propose a HADOOP distributed system for large data processing, which has two type of cache memory one is local cache, and other is distributed centralized cache memory. We use these cache memories for reducing the recompilation time.
Title: Distributed Data Caching For Big Data
Author: N. Sundaram, R. Saranya
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
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