(mellow energetic music)
- I'm Werner Vogels, Chief Technology Officer of Amazon.com.
I'm thrilled to welcome Mai-Lan Tomsen Bukovec,
AWS VP of Block and Object Storage,
as we highlight the importance
of building an evolvable system with Amazon S3.
Welcome Mai-Lan.
- Hi Werner, thanks for having me.
- When we started designing S3,
we did not really know how our customers
were going to be able to use this service.
So we made a purposely designed decision to keep it simple.
Yeah, hence S3, the simple storage service.
And I think as such, it's been an amazing basis,
not just for us to evolve on,
but also for our customers to conceptualize
how easy it is to use a simple storage service.
- How we evolve a service like S3
is a hundred percent driven
by what the outcomes are that our customers want.
And as you think about data lakes
as just one example of that,
we have tens of thousands of data lakes running on S3 today.
And when we're thinking about evolving a service like S3,
we look at what customers are doing with those data lakes.
And what they're doing is they're running,
they're building and running tens or hundreds
of applications on a shared data set.
And so back in the day when S3 first launched,
we were looking at what the customer outcome was,
and it was storage.
It was elastic storage of images and videos,
and storage that frankly grows incredibly fast.
And you need that pay as you go, that low cost,
that high durability storage.
And so we built it for that.
And then over time, what you've seen,
is that more and more transaction logs,
more and more parquet files,
more and more customer care recordings,
more and more datasets are coming into S3.
And now the outcome of what customers want
is not just that elasticity and that low cost,
but they want the ability to use that shared dataset
by hundreds of applications.
And so we built features like [Amazon S3] Access Points.
That's a new semantic.
We launched it a couple of years ago in S3,
that is just not part of the vernacular
for on premises storage or any other cloud provider.
And an Access Point,
basically gives the ability for a customer
to secure the data perimeter on a shared bucket
in a way that is unique to the access,
or the use of that access point by an application.
So you can do VPC only traffic.
You can give read-only traffic
to a subset of your data and your data lake.
And so that type of innovation,
the place it starts, Werner,
is the same place it started in 2006,
which is, what is the customer
wanting to do with their data?
And how do we build new concepts and new semantics
into this evolving world of storage
that S3 has always pioneered?
- You've been in charge for S3 for quite a while,
and also EBS and other storages.
Where's the blue skies?
Is there still blue sky?
- Absolutely.
We're just at the start
of what customers are doing with storage.
If you think about it Werner,
every business today is a data business.
The idea of centralizing all of your different datasets,
of all kinds, in AWS storage
is a pattern that's being adopted
all over the world, in every industry.
The first usage of that consolidated data
is clearly analytics.
That's what businesses use today
to make fast and correct decisions.
But where we're seeing the mainstreaming of data usage
is really around machine learning.
Everything from NuData which was,
is now a part of Mastercard where they're analyzing fraud.
They're looking specifically at account takeovers,
and they're using machine learning models
to prevent fraud, to detect and prevent fraud.
Or if it's GE Healthcare,
that's looking at petabytes of radiology images
to detect which patient needs to get care first,
you'll find that there is deep, deep usage of ML,
because that is the path forward for decision-making,
that and analytics
- So, important there is,
that there's a shift in terms
from let's say traditional Excel style datasets,
to much richer data.
Where video, audio, imagery
are no longer things to be listened to or viewed,
but they become datasets to be analyzed.
So can you talk a bit about that?
- Yeah, I think what's been interesting
is to watch how many different datasets
are being put into our storage.
And I think, you know, as you said,
the notion of an object being unstructured data,
that's where S3 started in 2006.
Unstructured data of images, and video, PDF files,
things like that.
And where it's gone since then,
is this ever increasing body of not just those datasets,
but also, you know things like customer care recordings.
That's a huge dataset.
Satellite imagery.
But I also think that if you look
at the rapidly emerging volume of dataset,
it's again, things like parquet files.
It's things that are used
for the analysis of business today.
And when you think about the data model
that can be built on that dataset,
and then you think about how to join these other datasets,
like learning on customer care recordings,
it just opens up this whole new world of machine learning.
Because for the first time with AWS,
you can take these different, and in the previous you know,
on-premises world, totally separate datasets,
they would grow in different ways, right?
And you bring them together in one shared storage model,
the type of insights that you get are new.
They're different.
- Thank you very much.
I think the culture around S3,
of continuous innovation is exemplary
for Amazon and for AWS.
Thank you.
- Thank you, Werner.
Nice to see you.
(mellow energetic music)