WHAT IS BIG DATA?
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When we talk about Big Data we refer to a set of data: size (volume), complexity (variability) and speed of growth, especially new sources of data. These data sets are so bulky that the data processing software simply cannot be managed. However, these massive volumes of data can be used to address problems that were hard to solve in the past.
Why is Big Data so important?
The collection of large amounts of data and the search for trends within the data, allows companies to move much more quickly, smoothly and efficiently. It also allows them to eliminate problems in some areas, before problems are so big that they can damage their benefits or reputation.
Big Data analysis helps organizations to leverage their data and use it to identify new opportunities. That, in turn, leads to the most intelligent business movements, the most efficient operations, the biggest profits and the happiest customers.

Characteristics
• Volume: the amount of data generated and saved. The size of the data determines the value and potential knowledge, and if you can consider them as true big data.
• Variety: the type and nature of the data to help people analyze it and use the results effectively. Big data uses images, audio and video. They also complete ordered pieces through data fusion.
• Speed: in this context, the speed at which the data is generated and processed to meet the demands and challenges of its analysis.
• Veracity: the quality of the data captured can vary greatly and affect the results of the analysis.

There are many tools to deal with big data. Some examples include Hadoop, NoSQL, Cassandra, business intelligence, machine learning and MapReduce. The nature of information today is different from information in the past. Due to the abundance of sensors, microphones, cameras, medical scanners, images, and other items in our lives, the data generated from this larger document of all available information.
The use of Big Data has helped researchers discover things that could have taken them years to discover for themselves without the use of these tools. Due to the speed of the analysis, it is possible that the data analyst can change their ideas based on in the result obtained and rework the procedure over, and over again until you find the true value you are trying to reach.

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