>> Announcer: Live from San Jose, it's theCUBE,
presenting Big Data, Silicon Valley, brought to you
by SiliconANGLE Media and its ecosystem partners.
>> Welcome back to theCUBE.
Our continuing coverage of our event,
Big Data SV, continues, this is our first day.
We are down the street from the Strata Data Conference.
Come by, we're at this really cool venue,
the Forager Tasting Room.
We've got a cocktail party tonight.
You're going to hear some insights there
as well as tomorrow morning.
I am Lisa Martin, joined by my co-host, George Gilbert,
and we welcome back to theCUBE,
for I think the 900 millionth time,
the president and CEO of DataTorrent, Guy Churchward.
Hey Guy, welcome back!
>> Thank you, Lisa, I appreciate it.
>> So you're one of our regular VIP's.
Give us the update on DataTorrent.
What's new, what's going on?
>> We actually talked to you a couple of weeks ago.
We did a big announcement which was around 3.10,
so it's a new release that we have.
In all small companies, and we're a small startup,
in the big data and analytic space,
there is a plethora of features that I can reel through.
But it actually makes something
a little bit more fundamental.
So in the last year...
In fact, I think we chatted with you maybe six months ago.
We've been looking very carefully at how customers
purchase and what they want and how they execute
against technology, and it's very very different
to what I expected when I came into the company
about a year ago off the EMC role that I had.
And so, although the features are there,
there's a huge amount of underpinning around the experience
that a customer would have around big data applications.
I'm reminded of, I think it's Gartner that quoted that
something like 80% of big data applications fail.
And this is one of the things that
we really wanted to look at.
We have very large customers in production,
and we did the analysis of what are we doing well with them,
and why can't we do that en masse,
and what are people really looking for?
So that was really what the release was about.
>> Let's elaborate on this a little bit.
I want to drill into something where you said many projects,
as we've all heard, have not succeeded.
There's a huge amount of complexity.
The terminology we use is, without tarring and feathering
any one particular product, the open source community
is kind of like, you're sort of harnessing a couple dozen
animals and a zookeeper that works in triplicate...
How does DataTorrent tackle that problem?
>> Yeah, I mean, in fact I was desperately interested
in writing a blog recently about using the word community
after open source, because in some respects,
there isn't a huge community
around the open source movement.
What we find is it's the du jour way in which we want to
deliver technology, so I have a huge amount of developers
that work on a thing called Apache Apex,
which is a component in a solution,
or in an architecture and in an outcome.
And we love what we do, and we do the best we do,
and it's better than anybody else's thing.
But that's not an application, that's not an outcome.
And what happens is, we kind of don't think about what else
a customer has to put together,
so then they have to go out to the zoo
and pick loads of bits and pieces and then try to figure out
how to stitch them all together in the best they can.
And that takes an inordinately long time.
And, in general, people who love this
love tinkering with technologies,
and their projects never get to production.
And large enterprises are used to sitting down
and saying, "I need a bulletproof application.
"It has to be industrialized.
"I need a full SLA on the back of it.
"This thing has to have lights out technology.
"And I need it quick."
Because that was the other thing, as an aspect,
is this market is moving so fast,
and you look at things like digital economy
or any other buzz term, but it really means that if you
realize you need to do something,
you're probably already too late.
And therefore, you need it speedy, expedited.
So the idea of being able to wait for 12 months,
or two years for an application, also makes no sense.
So the arch of this is basically deliver an outcome,
don't try and change the way in which open source
is currently developed, because they're in components,
but embrace them.
And so what we did is we sort of looked at it and said,
"Well what do people really want to do?"
And it's big data analytics, and I want to ingest
a lot of information, I want to enrich it,
I want to analyze it, and I want to take actions,
and then I want to go park it.
And so, we looked at it and said, "Okay, so the majority
"of stuff we need is what we call a cache stack,
"which is KAFKA, Apache Apex, Spark and Hadoop,
"and then put complex compute on top."
So you would have heard of terms like machine learning,
and dimensional compute, so we have their modules.
So we actually created an opinionated stack...
Because otherwise you have a thousand to choose from
and people get confused with choice.
I equate it to going into a menu at a restaurant,
there's two types of restaurants,
you walk into one and you can turn
pages and pages and pages and pages of stuff,
and you think that's great, I got loads of choice,
but the choice kind of confuses you.
And also, there's only one chef at the back,
and he can't cook everything well.
So you know if he chooses the components
and puts them together, you're probably not going to
get the best meal.
And then you go to restaurants that you know
are really good, they generally give you
one piece of paper and they say,
"Here's your three entrees."
And you know every single one of them.
It's not a lot of choice, but at the end of the day,
it's going to be a really good meal.
>> So when you go into a customer...
You're leading us to ask you the question which is,
you're selling the prix fixe tasting menu,
and you're putting all the ingredients together.
What are some of those solutions and then, sort of,
what happens to the platform underneath?
>> Yeah, so what you don't want to do is to take these
flexible, microdata services,
which are open source projects,
and hard glue them together to create an application
that then has no flexibility.
Because, again, one of the myths that I used to assume
is applications would last us seven to 10 years.
But what we're finding in this space is this movement
towards consumerization of enterprise applications.
In other words, I need an app and I need it tomorrow
because I'm competitively disadvantaged,
but it might be wrong, so I then need
to adjust it really quick.
It's this idea of continual developed, continual adjustment.
But that flies in the face of all
of this gluing and enterprise-ilities.
And I want to base it on open source, and open source,
by default, doesn't glue well together.
And so what we did is we said okay, not only do you have
to create an opinionated stack, and you do that because
you want them all to scale into all industries,
and they don't need a huge amount of choice,
just pick best of breed.
But you need to then put a sleeve around them
so they all act as though they are a single application.
And so we actually announced a thing calls Epoxy.
It's a bit of a riff on gluing,
but it's called DataTorrent Epoxy.
So we have, it's like a microdata service bus,
and you can then interchange the components.
For instance, right now, Apache Apex is this string-based
processing engine in that component.
But if there's a better unit, we're quite happy
to pull it out, chuck it away, and then put another one in.
This isn't a ubiquitous snap-on toolset,
because, again, the premise is use open source,
get the innovation from there.
It has to be bulletproof and enterprise-ility
and move really fast.
So those are the components I was working on.
>> Guy, as CEO, I'm sure you speak
with a lot of customers often.
What are some of the buying patterns that you're seeing
across industries, and what are some of the major business
value that DataTorrent can help deliver to your customers?
>> The buying patterns when we get involved,
and I'm kind of breaking this down
into a slightly different way, because we normally
get involved when a project's in flight,
one of the 80% that's failing, and in general,
it's driven by a strategic business partner
that has an agenda.
And what you see is proprietary application vendors
will say, "We can solve everything for you."
So they put the tool in and realize it doesn't have
the flexibility, it does have enterprise-ility,
but it can't adjust fast.
And then you get the other type who say,
"Well we'll go to a distro or we'll go
"to a general purpose practitioner,
"and they'll build an application for us."
And they'll take open source components,
but they'll glue it together with proprietary mush,
and then that doesn't then grow past.
And then you get the other ones, which is,
"Well if I actually am not guided by anybody,
"I'll buy a bunch of developers, stick them in my company,
"and I've got control on that."
But they fiddle around a lot.
So we arrive in and, in general, they're in this middle
process of saying, "I'm at a competitive disadvantage,
"I want to move forward and I want to move forward fast,
"and we're working on one of those three channels."
The types of outcomes, we just, and back to the expediency
of this, we had a telco come to us recently,
and it was just before the iPhone X launched,
and they wanted to do AB testing
on the launch on their platform.
We got them up and running within three months.
Subsequent from that launch, they then repurposed
the platform and some of the components
with some augmentation, and they've come out
with three further applications.
They've all gone into production.
So the idea is then these fast cycles of microdata services
being stitched together with the Epoxy resin type approach--
>> So faster time to value, lower TCO--
>> Exactly.
>> Being able to get to meet their customers' needs faster--
>> Exactly, so it's outcome-based and time to value,
and it's time to proof.
Because this is, again, the thing that Gartner picked up on,
is Hadoop's difficult, this market's complex
and people kick the tires a lot.
And I sort of joke with customers, "Hey if you want to
"obsess about components rather than the outcome,
"then your successor will probably come see us
"once you're out and your group's failed."
And I don't mean that in an obnoxious way.
It's not just DataTorrent that solves this same thing,
but this it the movement, right?
Deal with open source, get enterprise-ilities,
get us up and running within a quarter or two,
and then let us have some use and agile repurposing.
>> Following on that, just to understand going in
with a solution to an economic buyer,
but then having the platform be reusable,
is it opinionated and focused on continuous
processing applications, or does it also address
both the continuous processing and batch processing?
>> Yeah, it's a good answer.
In general, and again Gatekeeper,
you've got batch and you've got realtime and string,
and so we deal with data in motion,
which is string-based processing.
A string-based processing engine can deal
with batch as well, but a batch cannot deal with string.
>> George: So you do both--
>> Yeah
>> And the idea being that you can have one
programming model for both. >> Exactly.
>> It's just a window, batch is just a window.
>> And the other thing is, a myth bust, is for the last
maybe eight plus years, companies assume that the first
thing you do in big data analytics is collect all the data,
create a data lake, and so they go in there,
they ingest the information, they put it into a data lake,
and then they poke the data lake posthumously.
But the data in the data lake is, by default, already old.
So the latency of sticking it into a data lake
and then sorting it, and then basically poking it,
means that if anybody deals
with the data that's in motion, you lose.
Because I'm analyzing as it's happening
and then you would be analyzing it after at rest, right?
So now the architecture of choice is ingest the information,
use high performance storage and compute,
and then, in essence, ingest, normalize, enrich,
analyze, and act on data in motion, in memory.
And then when I've used it, then throw it off
into a data lake because then I can basically do posthumous
analytics and use that for enrichment later.
>> You said something also interesting
where the DataTorrent customers, the initial successful ones
sort of tended to be larger organizations.
Those are typically the ones with skillsets to,
if anyone's going to be able to put pieces together,
it's those guys.
Have you not...
Well, we always expected big data applications,
or sort of adaptive applications,
to go mainstream when they were either packaged apps
to take all the analysis and embed it,
or when you had end to end integrated products
to make it simple.
Where do you think, what's going to drive this mainstream?
>> Yeah, it depends on how mainstream you want mainstream.
It's kind of like saying how fast is a fast car.
If you want a contractor that comes into IT
to create a dashboard, go buy Tableau,
and that's mainstream analytics, but it's not.
It's mainstream dashboarding of data.
The applications that we deal with, by default,
the more complex data,
they're going to be larger organizations.
Don't misunderstand when I say,
"We deal with these organizations."
We don't have a professional services arm.
We work very closely with people like HCL,
and we do have a jumpstart team that helps people get there.
But our job is teach someone, it's like a kid with a bike
and the training wheels, our job is to teach them
how to ride the bike,
and kick the wheels off, and step away.
Because what we don't want to do is to put
a professional services drip feed into them
and just keep sucking the money out.
Our job is to get them there.
Now, we've got one company who actually are going to go live
next month, and it's a kid tracker, you know like a GPS
one that you put on bags and with your kids,
and it'll be realtime tracking for the school
and also for the individuals.
And they had absolutely zero Hadoop experience
when we got involved with them.
And so we've brought them up,
we've helped them with the application,
we've kicked the wheels off
and now they're going to be sailing.
I would say, in a year's time, they're going to be comfortable
to just ignore us completely, and in the first year,
there's still going to be some handholding
and covering up a bruise as they
fall off the bike every so often.
But that's our job, it's IP, technology,
all about outcomes and all about time to value.
>> And from a differentiation standpoint,
that ability to enable that self service
and kick off the training wheels, is that one of the biggest
differentiators that you find DataTorret has,
versus the Tableau's and the other
competitors on the market?
>> I don't want to say there's no one doing what we're doing,
because that will sound like we're doing something odd.
But there's no one doing what we're doing.
And it's almost like Tesla.
Are they an electric car or are they a platform?
They've spurred an industry on, and Uber did the same thing,
and Lyft's done something and AirBNB has.
And what we've noticed is customer's buying patterns
are very specific now.
Use open source, get up their enterprise-ilities,
and have that level of agility.
Nobody else is really doing that.
The only people that will do that is your contract
with someone like Hortonworks or a Cloudera,
and actually pay them a lot of money to build
the application for you.
And our job is really saying, "No, instead of you paying
"them on professional services, we'll give you the sleeve,
"we'll make it a little bit more opinionated,
"and we'll get you there really quickly,
"and then we'll let you and set you free."
And so that's one.
We have a thing called the Application Factory.
That's the snap on toolset where they can literally
go to a GUI and say, "I'm in the financial market,
"I want a fraud prevention application."
And we literally then just self assemble the stack,
they can pick it up, and then put their input and output in.
And then, as we move forward, we'll have partners
who are building the spoke applications in verticals,
and they will put them up on our website,
so the customers can come in and download them.
Everything is subscription software.
>> Fantastic, I wish we had more time,
but thanks so much for finding some time today
to come by theCUBE, tell us what's new,
and we look forward to seeing you
on the show again very soon.
>> I appreciate it, thank you very much.
>> We want to thank you for watching theCUBE.
Again, Lisa Martin with my co-host George Gilbert,
we're live at our event, Big Data SV, in downtown San Jose,
down the street from the Strata Data Conference.
Stick around, George and I will be back
after a short break with our next guest.
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