Docker : The next frontier for innovation, competition, and productivity.Big Data refers to datasets that grow so large that it is difficult to capture, store, manage, share, analyze and visualize with the typical database software tools. The popular ways to solve big data problems is to break complex problems into smaller pieces, send each piece to a different computer, solve the smaller pieces in parallel, and then pull the results together for analysis. This is the approach used by an open source program called Hadoop and its application programming model, MapReduce Big data types (e.g., location, time-series, and sensor data) require fundamental matrix operations that are not suited to traditional row-store, column-store, or MapReduce. Volume. As of 2012, about 2.5 exabytes of data are created each day, and that number is doubling every 40 months or so. More data cross the internet every second than were stored in the entire internet just 20 years ago Velocity. For many applications, the speed of data creation is even more important than the volume. Real-time or nearly real-time information makes it possible for a company to be much more agile than its competitors.
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