EMERGING REAL TIME STREAMING ANALYTICS PROCESSING USING HADOOP FRAMEWORK

Sensors, machines, vehicles, cell phones, webbased social networking systems and other constant sources are creating persistent stream of information. This information is utilized by the organizations to get the advantage from those information. A real time streaming system should address the issues of researchers, developers and records focus activities groups without requiring complex code for incorporation of numerous outsider tools. As there is increment in measure of information that is produced and gathered, statistical analysis wants adaptable, flexible, and high performance tool to analyze and obtain only the necessary data from the large growing data in a required timely manner. Hadoop Distributed File System (HDFS) is one of the file system to store huge measure of information. HDFS can oversee and keep up information in a dispersed manner. Real Time Streaming data can be put away into noSQl databases, for example, Mongo DB and Hive. Enormous information investigation can be performed on information put away on Hadoop distributed file system utilizing Apache Hive, Tez , Storm, Flume and Apache Presto. Hive is a environment which is over Hadoop (Map Reduce), and gives more significant level language to apply to the Hadoop's fundamental part Map Reduce to process the information. The key focal points of this methodology are it can equipped for processing and saving of the enormous measure of information. It additionally can adapt to the a large number of client demands at the same time. It can give the scalability to the machine is increasingly attractive with the guide of including new nodes. Incorporating the Visualization equipment with Big Data projects will give the gigantic picture to the clients to see the bits of knowledge of the Big data. It can offer the analytical reports for giving the big view about the file system.. Keywords— Real Time Streaming, HDFS, Hive, Tez, Storm, Flume, Storm, Apache Presto, Mongo DB, Big Data