Big Data Overview

The Data sets that are too large and complex to manipulate or interrogate with standard methods or tools so it cannot be processed using some conventional methods. Now a days social networks, mobile phones, sensors and science contribute to pet bytes of data created daily. Creators of web search engines were among the first to confront this problem. We've all heard a lot about "big data," but "big" is really a red herring. Companies like telecommunication, and other data-centric industries have had huge datasets for a long time. The storage capacity continues to expand, today's "big" is certainly tomorrow's "medium" and next week's"small." or it can be defined as "big data" is when the size of the data itself becomes part of the problem. The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytic. We're discussing data problems ranging from gigabytes to petabytes of data. These useful informations for companies or organizations with the help of gaining richer and deeper insights and getting an advantage over the competition. Hence big data implementations need to be analyzed and executed as accurately as possible. At some point, traditional techniques for working with data run out of steam. The information platforms are similar to traditional data warehouses, but different. Some rich APIs, are designed for exploring and understanding the data rather than for traditional analysis and reporting. ————————————————————