Abstract
In present scenario, the growing data are naturally unstructured. In this case to handle the wide range of data, is difficult. The proposed paper is to process the unstructured text data effectively in Hadoop map reduce using Python. Apache Hadoop is an open source platform and it widely uses Map Reduce framework. Map Reduce is popular and effective for processing the unstructured data in parallel manner. There are two stages in map reduce, namely transform and repository. Here the input splits into small blocks and worker node process individual blocks in parallel. This map reduce generally is based on java. While Hadoop Streaming allows writing mapper and reducer in other languages like Python. In this paper, we are going to show an alternative way of processing the growing unstructured content data by using python. We will also compare the performance between java based and non-java based programs.
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 callMore From: International Journal of Engineering & Technology
Disclaimer: 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.
