In information technology, big data refers to the data sets that consist of data sets which grow so large and complex that they become awkward to work with using on-hand database management tools. Difficulties that arise while using these tools are capture, storage, search, sharing, analytics and visualization. This trend continues because of the benefits of working with larger data sets allows analysts to spot business trends, prevent diseases and combat crime. Moreover, scientists also regularly encounter problems in meteorology, genomics and during complex physics simulations. Also since the cloud computing bandwagon is out of gas, vendors have jumped on the next one to roll down the pike: Big Data.

There are certain information that can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue. Big data analytics is a process that allows you to examine large amounts of data of different varieties to uncover hidden patterns, unknown correlations and other useful information. The primary goal of using this analytics platform is to help companies make better business decisions by enabling data scientist and other users to analyze large volume of data transactions as well as other data sources which may be left untapped by conventional intelligence business intelligence programs. These other data sources can include Web server logs and internet click stream data, social media activity reports, mobile phone call details records and also information that is captured through sensors.

Big data analytics can be easily done with the software tools that are commonly used as a part of advanced analytics disciplines such as predictive analytics and data mining. But the unstructured data sources that are used for big data analytics do not fit appropriately in the traditional data warehouses and furthermore, these warehouses may not be able to handle the processing demands that are posed by big data. As a result, a new class of big data technology has emerged that is being used in analytics programs that are quite huge. This technology includes No SQL databases, Hadoop and MapReduce which forms the core of an open source software framework that supports the processing of large data sets across clustered systems.

Potential pitfalls that can trip up organizations on big data analytics initiatives include a lack of internal analytics skills and also the high cost of hiring experienced analytics professionals along with the challenges in integrating Hadoop systems and data warehouses. However, vendors have now started to offer software connectors between those technologies.

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