Big Data - The 5 Vs Everyone Must Know

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This slide deck, by Big Data guru Bernard Marr, outlines the 5 Vs of big data. It describes in simple language what big data is, in terms of Volume, Velocity, Variety, Veracity and Value.

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  • hi Bernard looks like your slides struck accord with GODAN as they have used them all in their latest document (http://www.godan.info/documents/data-revolution-agriculture) however despite using a CC-BY-NC-SA licence they forgot to attribute you.... on the plus side you are not the only one... at least one other author has been plagiarised by a bit of cut and paste that omitted their moral rights....
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  • With the huge scale of data doubling every year, using anylytics measuring the experience, Script with HDFS and Flume., I see trouble shooting hands on with newer software processesing through Virtualization predictive
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  • Your deck explains only 4 V's and not 5. BY the way there are more V's associated with Big Data for example Visualization.
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  • I learned a lot at thedevmasters.com. It is an amazing service special there mentoring program gave me real hand on experience in troubleshooting. I was able to create a full “Sports Statistics : Given a data set of runs scored by players in different countries in different years. Write a flume configuration to copy this data to HDFS using flume and then write a PIG script to process data using PIG to find out the sum of run scored and balls played by each player.” , in less than 6 hour time all by myself. Amazing professional team of mentors and software educators. Visit www.thedevmasters.com and robin@thedevmasters.com, 1(866)340-1375
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  • Great to see others finally coming to appreciate the "V"s of Big Data that Gartner first defined over 15 years ago, albeit without the professional courtesy of attributing them to us. Note however that only the original 3Vs I first identified back then are definitional qualities of Big Data. Other "V"s that people (cleverly?) add are not measures of magnitude. And value is an aspirational attribute at that. To see my original 2001 piece on the 3Vs: http://goo.gl/wH3qG. To see what Batman thinks of those being cute by adding other Vs: http://blogs.gartner.com/doug-laney/batman-on-big-data/. --Doug Laney, VP Research, Gartner, @doug_laney
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Big Data - The 5 Vs Everyone Must Know

  1. Big Data The 5 Vs
  2. To get a better understanding of what Big Data is, it is often described using 5 Vs: Velocity VolumeVariety Veracity Value
  3. Volume Refers to the vast amounts of data generated every second. We are not talking Terabytes but Zettabytes or Brontobytes. If we take all the data generated in the world between the beginning of time and 2008, the same amount of data will soon be generated every minute. This makes most data sets too large to store and analyse using traditional database technology. New big data tools use distributed systems so that we can store and analyse data across databases that are dotted around anywhere in the world.
  4. Variety Refers to the different types of data we can now use. In the past we only focused on structured data that neatly fitted into tables or relational databases, such as financial data. In fact, 80% of the world’s data is unstructured (text, images, video, voice, etc.) With big data technology we can now analyse and bring together data of different types such as messages, social media conversations, photos, sensor data, video or voice recordings.
  5. Velocity Refers to the speed at which new data is generated and the speed at which data moves around. Just think of social media messages going viral in seconds. Technology allows us now to analyse the data while it is being generated (sometimes referred to as in-memory analytics), without ever putting it into databases.
  6. Veracity refers to the messiness or trustworthiness of the data. With many forms of big data, quality and accuracy are less controllable (just think of Twitter posts with hash tags, abbreviations, typos and colloquial speech as well as the reliability and accuracy of content) but big data and analytics technology now allows us to work with these type of data. The volumes often make up for the lack of quality or accuracy.
  7. Value Then there is another V to take into account when looking at Big Data: Value! Having access to big data is no good unless we can turn it into value. Companies are starting to generate amazing value from their big data.
  8. We currently only see the beginnings of a transformation into a big data economy. Any business that doesn’t seriously consider the implications of Big Data runs the risk of being left behind.

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