Black Money Check: Integration of Big Data & Cloud Computing To Detect Black Money Rotation with Range-Aggregate Queries

The big data is difficult to be analyzed due to the presence and characteristics of huge amount of data. Hadoop technology plays a key role in analyzing the large scale data. The aggregate queries are executed on more columns concurrently and it is difficult for huge amount of data. This paper is proposing the method in which the fast RAQ is dividing the big data in to autonomous partitions by means of a balanced partition algorithm and later for each partition a local assessment sketch is generated. By the arrival of the range-aggregate query demand the fast RAQ gets the result in a direct manner by shortening local estimate from all partition and then the cooperative results are provided. Thus in fast RAQ technique three tier Architecture is insisted and they are of  

1. Extracting the helpful information’s from Unstructured Data, 2.Implementation of the big data in Multi system Approach, 3.Application Deployment – Insurance/ Banking.