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Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Quite simply, big data reflects the changing world we live in. The more things change, the more the changes are captured and recorded as data. Take weather as an example. For a weather forecaster, the amount of data collected around the world about local conditions is substantial. Logically, it would make sense that local environments dictate regional effects and regional effects dictate global effects, but it could well be the other way around. One way or another, this weather data reflects the attributes of big data, where real-time processing is needed for a massive amount of data, and where the large number of inputs can be machine generated, personal observations or outside forces like sun spots.
Processing information like this illustrates why big data has become so important:
Data in its raw form has no value. Data needs to be processed in order to be of valuable. However, herein lies the inherent problem of big data. Is processing data from native object format to a usable insight worth the massive capital cost of doing so? Or is there just too much data with unknown values to justify the gamble of processing it with big data tools? Most of us would agree that being able to predict the weather would have value, the question is whether that value could outweigh the costs of crunching all the real-time data into a weather report that could be counted on.