Data warehouses of large organizations like of AWS(Amazon Web Services ) deploy Hadoop and other analytics clusters in multiple locations. Emergence of Cloud efficiency is oriented towards the development, testing new analytics applications and processing of Big data. Scenarios of business world need a System where real-time data processing can be handled. This requires a real time streaming of massively generated data at their disposal. It is causing a biggest challenge in front of existing IT ecosystem. Such requirement calls for the displacement of the existing options. There is a paradigm shift in the demands of real-time analytics, market needs a mature interactive BI solutions. Framework like Hadoop are caught in a tug-of-war between, a way to extend the data warehouse and facilitating analytics across existing applications at low cost. The data gravity should be sustainable. The real time forming data sets, such as weather data, machine, census data, and sensor data, originating out of the enterprise needs to be trapped and processed over cloud.
Enterprise requires a framework which, graciously migrate from one scale to another and to another scale. Enterprises can no more afford to allot a change on a big chunk of data , generally which is, that is frozen in time , at data centers. Say for a case, a weather channel having millions of locations reporting weather every four hours, creates billions of updates in few minutes. The efficiency of the cloud is, not only in the delivery of that data, it’s also about processing, such voluminous data. The flexibility and elastic scalability is extremely important property over cloud. During coming years, operational needs of data will bring tremendous change in the dynamism of the cloud and big data processing tools. operational challenges , compel you to ponders upon data analytical needs, like, the kind of data born, upon the cost of the operation also the rapidity of data processing etc. the range of big data technologies running on cloud have to incorporate dynamic factors the data. Analytics is addictive to BI. Big data running on elastic infrastructure has bigger role to play for big data analytics. Taking computation to data is the finest idea, but the biggest factor is , to locate the right “data gravity”, where processing needs to be done, that to at real time system.