- [Instructor] Sometimes, when you're thinking
about strategy, you get to choose between going for
a single big win, or maybe many small wins.
For example, in baseball,
you can go for the glorious home run,
or you can play small ball
and just focus on getting runners on base
and systematically moving them forward.
If you're writing novels,
you can hope to have a massive bestseller,
which is hard, or you can write a dozen books
that sell well within a particular niche.
And when it comes to helping people,
you can engage in a giant, newsworthy, grand gesture,
or you can focus on a thousand small acts of kindness.
You have a similar strategic choice in big data.
You can either put all of your data
and all of your processing hardware
in a single, massive server farm
or other central location.
Or, you can spread out the work to many,
many small processors.
In big data, this latter approach goes by
a couple of different names.
One is Edge Computing.
And the idea with Edge Computing is that you have
central storage and processing
in the regular big data model,
and that would be shown by this central unit
in the middle of this network.
And then you have connected devices
at the edge of the network.
But in Edge Computing, those devices on the edge,
the ones on the periphery, can also store,
and even process data, taking some of the work load
away from the central computer.
Another name for this is Fog Computing,
and this comes from the idea that you have
the cloud for data storage and data processing,
which is large, online servers.
But Fog is the same kind of cloud,
but now it's on the ground, and it's all around you.
The idea here is that you're bringing
what's in the cloud down to where the people are.
Now, there are several motivations for this.
Number one is, when you put things out there
on the edge, or when you bring the cloud down
to where the people are,
you can do things much faster.
You have low latency of response.
And obviously, if you have poor connectivity,
this is a good way of dealing with it
because you don't necessarily have to connect
to get the information or to do the processing.
There can be reduced server load at the central location.
But probably one of the most important ones
for a lot of consumers is privacy.
If the identifying or private information
never leaves your phone, if it never leaves your watch,
if it never leaves your laptop,
if it always stays with you,
then you don't have the same kind of privacy concerns
that might happen when everything's stored
in a single central location.
Now, I can give you a few examples
of Edge or Fog computing that are particularly useful.
One is facial recognition.
This can be done on your phone.
Once a network is trained well enough,
it can be done right there on your device.
Or, a more interesting one is sensors in jet engines.
This is one where the ability to function
with low latency, and in remote environments,
is very important.
Sensors can be tiny.
And they can actually be inside the jet engine
and monitoring temperatures for anomalies
to make sure that things are functioning properly
as the engine is operating.
And one of the most science fiction ones
is actually putting very small computers,
and by very small, I mean a third of a milometer
on each edge.
That's the thickness of a few sheets of paper.
These can actually be put inside biological cells,
inside cancer cells, and they include
a temperature monitor, a power source,
and a way of transmitting data.
That is Edge computing taken to an extreme.
In each case, you're taking tasks that maybe
could have been done at a large central location,
and you're putting them right at the point
where they are most needed,
where they can be responded to immediately,
where it can be done privately,
and it can do things that might
otherwise be impossible.
And that is the promise of taking some of
the tasks of big data and bringing it out
of the central location to the edges,
or out of the cloud into the fog.