The world moves with faster speed every day. Folk, companies, and entrepreneurs try to react at an ever-increasing speed. Reaching the limits of a human's ability to reaction, tools are build to process the massive and big amounts of data available to decision makers, analyze and present it. The processing of this data has a number of different application areas.
These applications are require an infrastructure and strategy of analysis specific to real time streaming data. The state of the Streaming infrastructure is centered on using commodity software and hardware to build its systems instead than the specialized systems required for real-time streaming analysis. This, combined with flexible cloud-based environment. These commodity systems allow companies to analyze their big and massive data in scale that infrastructure to meet forthcoming needs as the companies grows success and changes over time.
When real-time projects reach a certain fixed point, they should be adaptable and agile systems that can be conveniently modified, which requires that the users have a clean understanding of the stack as a entire in addition to their own areas of focus. "Real time" applies as much to the development of new analysis as it does to the big data itself. Number of well-meaning and planed projects have failed because they took so lengthy time to implement at user level. that the people who solicited the project have either moved on to another things or simply forgotten why they wanted the data in the first place. So By making the projects agile and incremental, this things must be avoided as much as possible.
There are a various sources of real time streaming data. Even the fact is there are ever more and more data resources being made available in the at the fast pace, i am going to discussed in my next post some of the application areas that have made streaming data really engrossing and interesting.