>> Live from San Jose,
in the heart of Silicone Valley,
it's theCUBE.
Covering Datawork Summit 2018.
Brought to you by Hortonworks.
>> Welcome back to theCUBE's live coverage
of Dataworks Summit here in San Jose, California.
I'm your host, Rebecca Knight,
along with my cohost James Kobielus.
We're joined by Scott Gnau,
he is the chief technology officer at Hortonworks.
Welcome back to theCUBE, Scott.
>> Great to be here.
>> It's always fun to have you on the show.
So, you have really spent your entire career
in the data industry.
I want to start off at 10,000 feet,
and just have you talk about where we are now,
in terms of customer attitudes,
in terms of the industry,
in terms of where customers feel,
how they're dealing with their data
and how they're thinking about their approach
in their business strategy.
>> Well I have to say,
30 plus years ago starting in the data field,
it wasn't as exciting as it is today.
Of course, I always found it very exciting.
>> Exciting means nerve-wracking.
Keep going.
>> Or nerve-wracking.
But you know, we've been predicting it.
I remember even you know,
10, 15 years ago before big data was a thing,
it's like oh all this data's going to come,
and it's going to be you know 10x what it is.
And we were wrong.
It was like 5000x, you know what it is.
And I think the really exciting part
is that data really used to be relegated frankly,
to big companies as a derivative work of ERP systems,
and so on and so forth.
And while that's very interesting,
and certainly enabled a whole level
of productivity for industry,
when you compare that to all
of the data flying around everywhere today,
whether it be Twitter feeds and even doing live polls,
like we did in the opening session today.
Data is just being created everywhere.
And the same thing applies to that data that applied
to the ERP data of old.
And that is being able to harness, manage
and understand that data
is a new business creating opportunity.
And you know, we were with some analysts the other day,
and I think one of the more quoted things
that came out of that when I was speaking with them,
was really, like railroads and shipping in the 1800s
and oil in the 1900s,
data really is the wealth creator of this century.
And so that creates a very nerve-wracking environment.
It also creates an environment,
a very agile and very important technological breakthroughs
that enable those things to be turned into wealth.
>> So thinking about that,
in terms of where we are at this point in time
and on the main stage this morning someone had likened it
to the interstate highway system,
that really revolutionized transportation,
but also commerce.
>> I love that actually.
I may steal it in some of my future presentations.
>> That's good but we'll know
where you pilfered it.
>> Well perhaps if data is oil the edge,
in containerized applications and piping data,
you know, microbursts of data across the internet of things,
is sort of like the new fracking.
You know, you're being able to extract more
of this precious resource from the territory.
>> Hopefully not quite as damaging
to the environment.
>> Maybe not.
I'm sorry for environmentalist if I just offended you,
I apologize.
>> But I think you know,
all of those analogies are very true,
and I particularly like the interstate one this morning.
Because when I think about what we've done
in our core http platform,
and I know Arun was here talking about
all the great advances that we built into this,
the kind of the core hadoop platform.
Very traditional.
Store data, analyze data but also bring in
new kinds of algorithms,
rapid innovation and so on.
That's really great but that's kind of half of the story.
In a device connected world,
in a consumer centric world,
capturing data at the edge,
moving and processing data at the edge
is the new normal, right?
And so just like the interstate highway system
actually created new ways of commerce
because we could move people and things more efficiently,
moving data and processing data more efficiently
is kind of the second part of the opportunity that we have
in this new deluge of data.
And that's really where we've been
with our Hortonworks data flow.
And really saying that the complete package of managing data
from origination at the edge
all the way through analytic
to decision that's triggered back at the edge
is like the holy grail, right?
And building a technology for that footprint,
is why I'm certainly excited today.
It's not the caffeine,
it's just the opportunity of making all of that work.
>> You know, one of the,
I think the key announcement for me at this show,
that you guys made on HDP 3.0 was containerization
of more of the capabilities of your distributed environment
so that these capabilities,
in terms of processing.
First of all, capturing and analyzing an moving that data,
can be pushed closer to the end points.
Can you speak a bit Scott,
about this new capability or this containerization support?
Within HDP 3.0 but really in your broader portfolio
and where you're going with that
in terms of addressing edge applications perhaps,
autonomous vehicles or you know,
whatever you might put into a new smart phone
or whatever you put at the edge.
Describe the potential containerizations
to sort of break this ecosystem wide open.
>> Yeah, I think there are a couple of aspects
to containerization and by the way,
we're like so excited about kind of the cloud first,
containerized HDP 3.0 that we launched here today.
There's a lot of great tech that our customers
have been clamoring for that they can take advantage of.
And it's really just the beginning,
which again is part of the excitement of being
in the technology space and certainly being part
of Hortonworks.
So containerization affords a couple of things.
Certainly, agility.
Agility in deploying applications.
So, you know for 30 years
we've built these enterprise software stacks
that were very integrated,
hugely complicated systems
that could bring together multiple different applications,
different workloads and manage all that
in a multi-tendency kind of environment.
And that was because we had to do that, right?
Servers were getting bigger,
they were more powerful
but not particularly well distributed.
Obviously in a containerized world,
you now turn that whole paradigm on its head
and you say,
you know what?
I'm just going to collect
these three microservices that I need to do this job.
I can isolate them.
I can have them run in a server-less technology.
I can actually allocate in the cloud servers to go run,
and when they're done they go away.
And I don't pay for them anymore.
So thinking about kind of that
from a software development
deployment implementation perspective,
there huge implications but the real value for customers
is agility, right?
I don't have to wait until next year to upgrade
my enterprise software stack to take advantage
of this new algorithm.
I can simply isolate it inside of a container,
have it run,
and have it go away.
And get the answer, right?
And so when I think about,
and a number of our keynotes this morning were talking about
just kind of the exponential rate of change,
this is really the net new norm.
Because the only way we can do things faster,
is in fact to be able to provide this.
>> And it's not just microservices.
Also orchestrating them through Kubernetes,
and so forth, so they can be.
>> Sure. That's the how versus yeah.
>> Quickly deployed as an ensemble
and then quickly de-provisioned
when you don't need them anymore.
>> Yeah so then there's obviously the cost aspect, right?
>> Yeah.
>> So if you're going to run a whole bunch of stuff
or even if you have something as mundane
as a really big merge join inside of hive.
Let me spin up a thousand extra containers
to go do that big thing,
and then have them go away when it's done.
>> And oh, by the way,
you'll be deployed on.
>> And only pay for it while I'm using it.
>> And then you can possibly distribute those containers
across different public clouds
depending on what's most cost effective
at any point in time Azure or AWS or whatever it might be.
>> And I tease with Arun,
you know the only thing that we haven't solved
is for the speed of light,
but we're working on it.
>> In talking about how this warp speed change,
being the new norm,
can you talk about some of the most exciting use cases
you've seen in terms of the customers
and clients that are using Hortonworks in the coolest ways.
>> Well I mean obviously autonomous vehicles is one
that we all captured all of our imagination.
'Cause we understand how that works.
But it's a perfect use case for this kind of technology.
But the technology also applies in fraud detection
and prevention.
It applies in healthcare management,
in proactive personalized medicine delivery,
and in generating better outcomes for treatment.
So, you know, all across.
>> It will bind us in every aspect of our lives
including the consumer realm increasingly, yeah.
>> Yeah, all across the board.
And you know one of the things that really changed,
right, is well a couple things.
A lot of bandwidth so you can start to connect these things.
The devices themselves are particularly smart,
so you don't any longer have to transfer all the data
to a mainframe and then wait three weeks,
sorry, wait three weeks for your answer
and then come back.
You can have analytic models running on and edge device.
And think about, you know, that is really real time.
And that actually kind of solves for the speed of light.
'Cause you're not waiting for those things
to go back and forth.
So there are a lot of new opportunities
and those architectures really depend
on some of the core tenets
of ultimately containerization
stateless application deployment and delivery.
And they also depend on the ability to create feedback loops
to do point-to-point and peer kinds
of communication between devices.
This is a whole new world of how data get moved
and how the decisions around date movement get made.
And certainly that's what we're excited about,
building with the core components.
The other implication of all of this,
and we've know each other for a long time.
Data has gravity.
Data movements expensive.
It takes time, frankly,
you have to pay for the bandwidth
and all that kind of stuff.
So being able to play the data where it lies
becomes a lot more interesting
from an application portability perspective
and with all of these new sensors,
devices and applications out there,
a lot more data is living its entire lifecycle in the cloud.
And so being able to create that connective tissue.
>> Or as being as terralexical on the edge.
>> And even on the edge.
>> In with machine learn,
let me just say, butt in a second.
One of the areas that we're focusing on increasingly
in Wikibot in terms of our focus on machine learning
at the edge,
is more and more machine learning frameworks
are coming into the browser world.
Javascript for the most like tenser flow JS,
you know more of this inferencing and training
is going to happen inside your browser.
That blows a lot of people's minds.
It may not be heavy hitting machine learning,
but it'll be good enough for a lot of things that people do
in their normal life.
Where you don't want to round trip back to the cloud.
It's all happening right there,
in you know, Chrome or whatever you happen to be using.
>> Yeah and so the point being now,
you know when I think about the early days,
talking about scalability,
I remember ship being my first one terabyte database.
And then the first 10 terabyte database.
Yeah, it doesn't sound very exciting.
When I think about scalability of the future,
it's really going to,
scalability is not going to be defined as petabytes
or exabytes under management.
It's really going to be defined
as petabytes or exabytes affected across a grid of storage
and processing devices.
And that's a whole new technology paradigm,
and really that's kind of the driving force
behind what we've been building
and what we've been talking about at this conference.
>> Excellent.
>> So when you're talking about these things.
I mean how much,
are the companies themselves prepared,
and do they have the right kind of talent
to use the kinds of insights that you're able to extract?
And then act on them in the real time.
'Cause you're talking about how this is saving a lot
of the waiting around time.
So is this really changing the way business gets done,
and do companies have the talent to execute?
>> Sure.
I mean it's changing the way business gets done.
We showed a quote on stage this morning
from the CEO of Marriott, right?
So, I think there a couple of pieces.
One is business are increasingly data driven
and business strategy is increasingly the data strategy.
And so it starts from the top,
kind of setting that strategy and understanding the value
of that asset and how that needs to be leveraged
to drive new business.
So that's kind of one piece.
And you know, obviously there are more and more folks
kind of coming to the realization that that is important.
The other thing that's been helpful is, you know,
as with any new technology there's always kind of
the startup shortage of resource
and people start to spool up and learn.
You know the really good news,
and for the past 10 years I've been working with
a number of different university groups.
Parents are actually going to universities
and demanding that the curriculum include data,
and processing and big data and all of these technologies.
Because they know that their children educated
in that kind of a world,
number one, they're going to have a fun job to go to everyday.
'Cause it's going to be something different everyday.
But number two they're going to be employed for life.
(laughing)
>> Yeah.
>> They will be solvent.
>> Frankly the demand has actually created a catch up
in supply that we're seeing.
And of course, you know,
as tools start to get more mature and more integrated,
they also become a little bit easier to use.
You know, less, there's a little bit easier deployment
and so on.
So a combination of,
I'm seeing a really good supply,
there really, obviously we invest in education
through the community.
And then frankly,
the education system itself,
and folks saying this is really the hot job
of the next century.
You know, I can be the new oil barren.
Or I can be the new railroad captain.
It's actually creating more supply
which is also very helpful.
>> Data's the heart of what I call the new stem cell.
It's science, technology, engineering, mathematics
that you want to implant in the brains of the young
as soon as possible.
I hear ya.
>> Yeah, absolutely.
>> Well Scott thanks so much for coming on.
But I want to first also,
we can't let you go without the fashion statement.
You arrived on set wearing it.
>> The elephants.
>> I mean it was quite a look.
>> Well I did it because then you couldn't see
I was sweating on my brow.
>> Oh please, no, no, no.
>> 'Cause I was worried about this tough interview.
>> You know one of the things I love about your logo,
and I'll just you know,
sounds like I'm fawning.
The elephant is a very intelligent animal.
>> It is indeed.
>> My wife's from Indonesia.
I remember going back one time they had Asian elephants
at a one of these safari parks.
And watching it perform,
and then my son was very little then.
The elephant is a very sensitive, intelligent animal.
You don't realize 'till you're up close.
They pick up all manner of social cues.
I think it's an awesome symbol for a company
that's all about data driven intelligence.
>> The elephant never forgets.
>> Yeah.
>> That's what we know.
>> That's right we never forget.
>> Him forget 'cause he's got a brain.
Or she, I'm sorry. He or she has a brain.
>> And it's data driven.
>> Yeah.
>> Thanks very much.
>> Great. Well thanks for coming on theCUBE.
I'm Rebecca Knight for James Kobielus.
We will have more coming up from Dataworks just after this.
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