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[MUSIC]

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In this stage, we'll take a more specific
look into where big data is being used,

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as well as how companies or
organizations are making use of its power.

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The most important point to remember
throughout these examples is that you

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don't have to be a massive company
to leverage data you have available

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on a large scale.

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Although it's helpful,

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you don't need to have a data
scientist on staff to get started.

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Now oftentimes, you can tackle the data
problems you have right now with only

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a technical engineer that can write code
in languages like Python, Java, or Scala.

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This is especially important to remember
for startups, where in this case,

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any advantage over your competitors
could mean the difference between

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succeeding or failing.

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Again, with just a few hundred megabytes
or gigabytes of data on hand, you can

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begin to see the benefits from using
modern tools and frameworks in big data.

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It does help, though, to take a look at
more notable use cases of big data at

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the companies we all know and
use on a regular basis.

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Google processes 40,000 search
queries every second, on average,

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which is 3.5 billion per day, and
1.2 trillion every year, worldwide.

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Snapchat averages 2.5 billion pictures
taken per day that have to be processed,

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delivered, and stored.

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Facebook averages 8
billion video views daily.

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All of those videos have to have
stats collected about them for

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each user, as well as being
cached effectively worldwide.

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With these examples in mind, let's discuss
the types of data that you may encounter

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in the wild, and then we'll run through
the three major domains of big data,

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storage, computations, and infrastructure.

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Let's kick it off by discussing
what types of data exist,

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right after this quick break.