Big Data Analytics for Concurrent Data Processing

Attractively voluminous data describes an immense volume of structured and unstructured data that is difficult to process utilizing traditional database techniques. The tremendous growth in arrival rates of data to support a large number of user queries creates complex problems in the traditional structured databases. In this paper, the input file is assigned to a master who has the ability to split and control the work flow with different workers. This will reduce the fault tolerance issues raised with nodes. They will evaluate the intermediate files and data items. Over again the processed data will be amalgamated and the required output will be immediately/middle file given to the user. Also the first solution for processing perpetual text queries efficiently to address the above challenges is given. The solution indexes the streamed documents in main recollection with a structure predicate on the principles of the inverted file, and processes document advent and expiration events with an incremental threshold-predicated method.