Hadoop Distributed file system, Hive and Its Applications: A Survey

Business intelligence is growing area across the industry and data getting collected and analyzed in rapid way due to which legacy warehousing tools has become very costly. Hadoop is framework which is open source and stores data and runs applications on cluster of normal i.e commodity hardware. Hadoop provides large amount of processing power and storage for various kinds of data. It is able to handle concurrent tasks or jobs. HDFS (Hadoop Distributed File System) is a distributed file system which can provide high performance data access across Hadoop cluster of servers. Due to Managing pools of big data and supporting big data analytics application HDFS has become a strong tool. Developer has to write custom programs in map reduce programming model which are difficult to maintain and reuse. Hive is open source solution built on top of hadoop which is used as data ware house. Hive supports HiveQL which is SQL-like language, which are compiled into mapreduce jobs to be executed on Hadoop

Equity Reports Optimization Using Hive, HDFS.

Now a day there is much competition in stock market world. Every brokerage firm Want to lesser down the TURN AROUND TIME for their applications so that business users/analyzers could be able provide solutions to their clients within a time frame. Equity clients prefer to do a business with firms which provide the very fast solutions for their queries. To solve clients query users has to fetch the reports from the software system. As stock market is time critical business. Every corporate has minimum time frame to take their business decisions. For taking business decisions in fast way, we should have usable data available within a time frame. With the help of reporting and research applications, it becomes easy to make accurate business data available in fast way