>> Announcer: Live from Las Vegas, it's theCUBE,
covering NetApp Insight 2018.
Brought to you by NetApp.
>> Welcome back to theCUBE, we are live.
We've been here all day at NetApp Insight
in Las Vegas at the Mandalay Bay.
I'm Lisa Martin with Stu Miniman
and we're joined by a couple of guests.
One of our alumni, Bharat Badrinath,
the V.P. of Product Solutions and Marketing at NetApp.
Hey, Bharat, welcome back.
>> Thank you, thanks for having me.
>> And we've also got Renee Yao,
who is a Senior Product Marketing Manager
for Deep Learning and AI Systems at Nvidia.
Renee, welcome to theCUBE.
>> Thanks for having me.
>> So guys, this is a pretty big event.
NetApp's biggest customer-partner event,
the keynote, standing room only this morning
five thousand plus people, lot of buzz, lot of momentum.
Speaking of momentum, NetApp and Nvidia just launched
an interesting partnership a couple months ago.
Bharat, talk to us about how NetApp is
working with Nvidia to really take advantage of AI
and allow your customers to do that as well.
>> Sure.
So, as we started talking to customers
and started looking at what they were investing in,
AI bubbled up, right up to the top.
And given our rich history in NFS,
high performance NFS, it became an obvious choice
for NetApp to invest in this space.
So we've been working with Nvidia for a really long time,
probably close to a year, to start
integrating our products with their DGX-1 supercomputer
and providing it as a single package to our customers,
which makes it a lot easier for them to deploy their AI
instead of waiting months for testing infrastructure,
which the data scientists don't want to do.
We get them a pre-tested, pre-validated system
and our All-Flash Fast, which has been
winning multiple awards and the recent
A800 announcement were perfect choice for us to
integrate into this architecture for the system.
>> Alright, Renee, in the keynote this morning,
the Futurist, he said--
We talked about data as the new oil,
he said AI is the new electricity.
Maybe you can speak a little bit
as to why this is so important.
Having gone to a lot of shows this year,
it felt like every single show I go to,
I see Nvidia, arm in arm with partners,
because there's a huge wave coming.
>> Yes, absolutely, and I think
there was this hype about data,
there was this hype about AI,
and I think the years of Big Data World,
that's creating data, absolutely the foundation for AI,
and AI as the new electricity is a very, very good analogy.
And let's do some math, shall we?
So Swiss Federal Railway, it's a very good customer of ours.
For those of you who don't know,
they're kind of like the heart or center of
all the railway tracks going through,
serving about 1.2 million passengers on a day-to-day basis.
Securing their security is very, very important.
Now, they also have a lot of switches that turn on,
then the train can go by and
with the tunnels and bridges and switches,
so they need to make sure that
these trains actually don't collide.
So when one train goes by with 11 switches,
that gives you 30 ways of possible routing.
Two trains, 900 ways.
80 trains, 10 to the eightieth power of ways.
That's more than the observed atoms in the universe.
And they actually have more than 10 thousand trains.
So think about, can human being possibly calculate
that much data and possibilities in their brain?
As smart as we all want to think we all are,
they turn to DGX, and the full day of
simulation on DGX-1 was only 17 seconds
for them to get back results.
And I think that analogy of AI as the new electricity,
just talking about the speed of light, is very spot on.
>> So this isn't hype anymore, this is actually reality.
And you gave a really great example of
how a large transportation system is using it
to get almost real time information.
Bharat, talk to us about NetApp storage, history,
26 years, you guys have really made
a lot of pivots in terms of your
digital transformation, your cultural transformation.
How are you helping with, now,
kind of the added power of Nvidia,
helping customers to, the hype's gone,
actually deploy it, live it, and benefit a business from it?
>> Yeah, absolutely, I think, as you rightly pointed out,
NetApp has made a lot of pivots.
Right, and I think the latest journey in terms of
being empowering our customers with data
has been a very powerful mission for the company.
We entered the Flash market a little bit
later than our competitors,
but we have made dramatic progress in that space.
In fact, recently, based on the latest IDC report,
we were number one in All-Flash market worldwide,
so that is quite an accomplishment
for a company which was late to the market.
And having said that, that's because of the
innovation engine that is still
alive and well within NetApp.
We're announcing, as you've seen in the conference,
we're announcing a lot of new products and technology
which are way ahead of what our competitors are offering,
but I think it is all hinged on what our customers need.
The customer benefits because, yeah,
it has profound benefit of changing how customers operate,
their entire operations,
it can transform dramatically overnight.
And as Renee pointed out, Big Data gave the foundation
which collected all the data, but wasn't able to process it.
But AI with the power of Nvidia
and DGX is able to utilize that
to create those outcomes for customers.
And from our perspective, we bring
two key value adds to the space.
One, we're able to serve up the data
at incredibly high speeds with our
award-winning All-Flash systems.
But more importantly, data today lives everywhere.
If you think about it,
edge is becoming even more important.
You can't expect an autonomous car
to make an instantaneous decision
without the backing of data, which means it can't,
everything can't reside in the cloud, it may be at the edge.
Some of it may be at your data center.
How do you tie all three together, edge, core, and cloud?
And that's where the data fabric,
the vision of data fabric that
you saw today comes in the picture.
So one is performance, the ability to stream up
the kind of data at the speed of
the new processors are demanding,
at the speed the customers are demanding
to make business decisions and
also the edge to code the cloud, our data fabric,
which is unique and unparalleled in the industry.
>> Now, I'm wondering if you could both bring us
inside the customers a little bit.
If I think of the traditional storage customer,
I need performance, I have more and more
data that I need to deal with.
But Renee pointed out real outcomes,
which is beyond what a traditional
storage person would be doing.
Who are you working with at the customers--
How do they put together--
It almost sounds like you're building a car.
I've got the engine, I've got all the pieces.
Who helps put this whole solution together?
How does the partnership on the customer's side go together?
>> That's a great question.
I'll give my take and you can jump on it
because she's just returned from being on road shows
with joint customers and prospects.
So I believe it has to be a joint decision.
It's not like IT does it first
and the data scientists come in later.
Although it may be the case in certain instances
where the data scientists start the discussion
and then the IT gets brought in.
In an ideal case, just like building a car,
you want all the teams to be sitting together,
make sure they're making the right calls
because every compromise you make
at one end will impact the other.
So you want to make sure you make
the optimal decision end to end.
And that's where some of our channel partners
come in who kind of bridge the
data scientist team and the IT team.
In some cases, customers show up with data scientists
and IT teams together and some, it's one after the other.
>> Absolutely.
We see the same thing when we're on the road show.
Literally two weeks ago, in Canada,
by the way, there was a snowstorm,
and it was an unforeseen snowstorm,
you don't get snowstorm in October--
>> Yes, even for Canada, it was unforeseen.
>> Yeah, and we had a packed room of people
coming to learn about AI and in the audience,
we absolutely see people from the infrastructure side,
from the data center side, from the data scientist side,
and they realized that they really have to start talking
because none of them can afford to be reactive.
For example, the data scientists,
we want to do the innovation.
I can't just go to the infrastructure guys and say that,
"Hey, this is my workload, do something about it."
And the infrastructure guys don't
want to hold on to that problem
and then don't know what to do with it.
They really need to be ahead of everything
and I think the interesting thing is,
among those four cities that we're at,
we see customers from the government,
oil and gas, transportation, health care,
and just any industry you can think of, they're all here.
One specific example, do you know Mike's company
that actually came to us, they have about 15 petabytes
of data and that's storing 20 years
of historical data and they only have two staff
and they were not hiring more staff.
They were like, "We just want something that's
"going to be able to work and we know everything,
"so just give us a solution that's going to be able to
"easily scale up and out and enable us to continue to
"store more data, manage more data,
"and get insights out of data fast."
So they came to both of us,
it's just a very good, natural decision.
That's why we have a partnership together as well.
>> So you guys talked about kind of connecting
the data scientists with the infrastructure folks.
Where's the business involved in this conversation?
In terms of, we want to identify new products and services
to deliver faster than our competition, new markets.
Talk to us about, are the data scientists and
the infrastructure guys and girls following
business initiatives that have been set
or are the business leaders
involved in these joint conversations?
>> Go ahead, you take it.
>> Sure.
So, I think we see both.
We definitely see that there's top-level executives
saying that this is our initiative and we have to do it.
And they will make the decision that we have to
refresh our infrastructure from the ground up
to make sure we're supportive
of our data scientists' innovation.
We've also seen brilliant minds, researchers,
data scientists doing amazing things
and then roll it up to the VP level
and then roll it up to CEO level
to say that this has to be done because this--
For example, that simulation of 17 second results,
it's things that people used to cannot do in their lifetime,
now they can do it in seconds,
that kind of innovation just cannot be ignored.
>> Yeah, we see the same thing.
In fact, any team that has possession of that data
or is accountable for that data is
the one usually driving the decisions.
Because as you mine the data,
as you start deploying new techniques,
you realize new opportunities, which means
the business gets more interested in it and vice versa.
If the business is interested, they're going to look for
those answers within the data that they have.
>> So last thing, Renee, you were on the
Women in Tech panel that ended yesterday,
Bharat and I were both in the audience,
and one of the things that I thought was
really inspiring about your story is that you had given us,
the audience, an interesting example of a TV opportunity
that you were inspired to do by the CEO of Nvidia.
Give our audience who didn't have a chance to see that panel
a little bit, and in the last minute, of that story
and how you were able to step forward
and go, "I'm going to try this."
>> Yeah, of course.
I think that brings us back to
the concept that we have at Nvidia,
the speed of light concept, and you really have to learn,
act, to move at the speed of light,
just like our GPUs, with extreme performance.
And obviously, at that speed, none of us know everything.
So what Jensen, CEO, shared with us was,
in an all-hands meeting internally,
he told us that none of us are here
qualified to do any of our jobs,
maybe besides his legal counsel and CFO.
And all of us are here to learn,
and we need to learn as fast and as much as we can.
And we can't really just let the competition
determine where our limit is, but instead is
by the limit of what is possible.
So that is very much a fundamental
mindset change in this AI revolution.
>> Well thanks so much, Renee and Bharat,
for stopping by and sharing with us
the exciting things that you guys are doing with NetApp.
We look forward to talking with you again soon.
>> Thank you.
>> Me too, thanks.
>> For Stu Miniman, I'm Lisa Martin.
You're watching theCUBE, live from
NetApp Insight 2018 in Las Vegas.
Stu and I will be right back
with our next guests after a short break.
(techno music)