A Speedy Data Uploading Approach for Twitter Trend and Sentiment Analysis Using HADOOP

The current Analytics tools and models that are available in the market are very costly, unable to handle Big Data and less secure. The traditional Analytics systems takes a long time to come up with results, so it is not beneficial to use for Real Time Analytics. So, the proposed work resolves all these problems by combining the Apache Open Source platform which solves the issues of Real Time Analytics using HADOOP. It also provides scalability and reduced cost over analytics by using open Source Software. The work proposes to combine the Apache Open Source Modules and configure them to get the required result. This system also provide solution for speedy data downloading on HDFS by using source and sink (data ingestion) mechanism. The Hadoop is flexible and scalable architecture. The proposed work is based upon the phenomenon of combination of open source software along with commodity hardware that will increase the profit of IT Industry.