Open source big data analytics technique

Abstract

In this mobile computing and business era, a huge amount of data is generated, which is Big Data. Such a large data becomes unmanageable and cannot be used for analytics using traditional methods. A large number of fields and sectors, ranging from economic and business activities, involve with Big Data problems. Big Data analytics is extremely valuable to make decisions for increasing productivity in businesses, which gives us a lot of opportunities to make great progresses in many fields. So, this paper discusses approaches and environments for carrying out analytics for Big Data applications. It revolves around important areas of analytics, Big Data, tools, and data base used. A comparative study is done and tabulated on parameters like Data Base used, real-time analytics, size, etc. Then on open source technology Kibana, Elastic search and JASON Query, a big data analytics experimental setup is done. Analytics is done in many dimensions like domain counts, percentile gross margins, sector-wise count, etc. Their drawn results are recorded and reported in form of graphs.