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- All right, so let's
run through a use case

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of image processing with AWS Lambda.

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In this scenario, we're going to have

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our mobile application
users upload images,

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and have those images be processed

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into various size thumbnails.

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Now, I think we all know
that the Internet loves cats.

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In this case, we have an application

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dedicated to uploading pictures of cats.

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The first thing that we need to do

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is enable our mobile application

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to have direct access
to an Amazon service,

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such as the Amazon Simple Storage Service.

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Now, by doing this, by uploading directly

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to an Amazon service,

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that enables us to bypass our backend API

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and it can help reduce
the load on that API.

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The first thing that we're going to do

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is leverage the Amazon Cognito service,

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which, in turn, can interact
with major login providers

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like Facebook and Twitter

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in order to have our user
login with those services.

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By leveraging Amazon Cognito,

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we don't have to worry about
the details of logging in

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with Facebook versus Twitter
versus Google and so on.

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All we do is ask Amazon Cognito
to handle that stuff for us.

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The first step is that
the user will log in

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with Facebook or Twitter,

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Amazon Cognito handles the details

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and returns temporary credentials,
as we've talked about,

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back to our mobile application.

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Now, again, those temporary credentials

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will only live for perhaps 15
minutes, so once they expire,

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we don't have to worry
about rotating them,

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we don't have to worry
about embedding credentials

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into our application.

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Once our application has
those temporary credentials,

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we can then upload directly

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to an Amazon Simple
Storage Service bucket.

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From there, once the image
lands in that bucket,

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an event will be fired that we can tie

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into a Lambda function,

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and the Lambda function can
be invoked by that event.

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Details about that image will
be delivered to the function.

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The function can then download the image

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from that S3 bucket,

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process it into various size thumbnails

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for different size devices,

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and then store the processed images

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in yet another Simple
Storage Service bucket.

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From there, this particular
bucket would be used

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as what we call a distribution origin

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for the Amazon CloudFront service,

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Amazon CloudFront being our
content delivery network.

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From there, those images will
be served from edge locations

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out to the other millions of devices

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out there on the Internet

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that are viewing these pictures of cats.

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You can see here, by doing this,

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we don't have any
infrastructure to maintain.

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We don't have EC2 instances that we need

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to launch and manage and create.

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We don't have, really, even an API

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that we have to worry about.

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In this particular example,

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we're leveraging services that are already

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inherently highly available
and fall tolerant.

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For application developers,
leveraging services like this

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can really ease development
and speed our time to market.

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Now, as we go forward,

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we will see some of
these use cases in demos

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that are introducing services

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that we haven't yet talked about,

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but the point here is to
spur your imagination.

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I want you to start thinking
of the possibilities

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of things that can be done
within Amazon Web Services.

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I want you to start to see

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just how flexible all
of these services are

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and how they can interact
and play with each other

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to achieve just about
anything you can imagine.

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Again, this is image processing

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with leveraging Amazon
Simple Storage Service,

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Amazon CloudFront, and AWS Lambda.