Searching, Indexing And Sentimental Analysis On Big Data

As the data grows, there is impact on the time taken for searching the data, as well as to create new indexes along with the size of repository. In order to improve the performance of search while scaling the data we need to increase the size of hardware, which include higher processing power and higher memory size. However it is not cost effective. So we are opt in using the optimized approach for Big data. The optimization of the technology stack which include Apache hadoop and Apache Solr helps to maintain the data with reasonable performance. The optimization is most important while scaling the instance of Big data with Hadoop and Solr. Searching and indexing using Solr can be done in reasonable amount of time. Without indexing we need to search the whole document which take more time. The purpose of storing the index is to optimize the speed and performance. Searching and indexing is done on the data using Apache Solr and sentimental analysis is done based on the reviews of the customer.