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Data Sources - Intro to Hadoop and MapReduce

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    Organisations have been generating
    data since way back
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    But as time goes on, more & more
    data is being generated.
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    IBM estimates that 90% of world's data
    was created in the last two years alone.
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    This is a simple example.
    Think of your cellphone.
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    Whenever your cellphone is turned on,
    it's connected to the cell towers.
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    As you move around,
    it'll connect to different towers
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    in a different signal streaks.
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    All of that connection data is collected
    by the phone company & it's being logged.
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    They can use that information to find
    dead spots in the coverage
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    & know which towers are busiest &
    need increased capacity.
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    They can even trace you if you make an
    emergency phone call but
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    don't get your exact location.
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    This is an enormous amount
    of data we have.
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    Another example as you when visit a
    website like Amazon or Netflix,
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    everything, you do there is logged: what
    pages you view, how long you spend there,
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    where you coming from.
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    They can even capture things like what
    browser you are using.
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    Again this is a huge amount of data.
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    Phone data & website logs are just examples.
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    In addition, things like X-rays are
    creating huge amounts of data.
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    & people doing research to detect
    similarities in tumors.
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    The increase in amount of data we're
    generating opens up huge possibilities.
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    But it comes with problems too.
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    Where do we've to store all this data?
    & process it too?
Tytuł:
Data Sources - Intro to Hadoop and MapReduce
Video Language:
English
Team:
Udacity
Projekt:
ud617 - Intro to Hadoop and Mapreduce
Duration:
01:16

English subtitles

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