>> Narrator: From Palo Alto, California,
it's CUBEConversations with John Furrier.
>> Hello, welcome to our special CUBEConversation
here in Palo Alto, California.
I'm John Furrier, co-founder of SiliconAngle Media
and co-host for the CUBE.
I'm here with Phu Hoang who's the co-founder and
chief strategy officer of DataTorrent.
Great to see you again.
Welcome back >> Thank you so much, John.
>> This CUBEConversation.
So, you're now the chief strategy officer,
which is code words for you are the CEO
and co-founder of the company.
You bring in a pro guy,
Churchwood we know very well, former EMC-er, real pro.
Gives you a chance to kind of get down and dirty
into the organization and get back to your roots
and kind of look at the big picture.
Great management team.
Talk about what your background is,
because I think I want to start there,
because you have an interesting background.
Former Yahoo executive, we've talked before.
Take a minute to talk about your background.
>> Yeah, sure.
You know I think I'm just one of those super lucky engineer.
I got involved with Yahoo way early in 1996.
I think I was the fifth engineer, or so.
I stayed there for 12 years,
ended up running about close to 3,000 engineers,
and had the chance to really experience
the whole growth of the internet.
We build out hundreds of sites worldwide,
so all of engineering team develop
all of those websites throughout the world.
>> You must have a tear in your eye at how Yahoo ended up.
We don't want to go there.
Folks that don't remember Yahoo during the web1.0 days,
it was the beginning of a revolution.
I kind of see the same thing happening,
like blockchain and what's going on now.
A whole new wild west is happening,
but back then you couldn't buy off the shelves.
You had to certainly buy servers, but the software,
you guys were handling kind of a first generation use case.
>> That's right.
>> Folks may or may not know,
but Yahoo really was the inventor of Hadoop.
Doing Hadoop at large scale, honestly ...
MapReduce written by Google, but the rest is,
you guys were deploying a lot of that stuff.
You had to deal with scale and write your own software
for big data, before it was called big data.
>> That's exactly right.
It's interesting, because originally we thought that our job
was really customer-facing website,
and all of the data crunching and massaging that we would
actually be able to use enterprise software to do that.
Very quickly we learned at the pace of scale data that we
were generating that we really couldn't use that software.
We were kind of on our own,
so we had to invent approaches to do that.
The thing we knew a lot was commodity servers on racks.
So, we ended up saying,
"How do I solve this big data processing problem
using that hardware?"
It didn't happen overnight.
It took many years of doing it right, doing it wrong,
and fixing it.
You start to iterate around how to do
distributed processing across many hundreds of servers.
>> It's interesting, Yahoo had the same situation.
And ultimately Amazon ended up having,
cause they were a pioneer.
People dismissed Amazon web services.
Like, "It's just hosting and bare metal on the cloud."
Really what's interesting is that you guys were hardening
and operationalizing big data.
>> That's right.
>> So, I got to ask you the question,
cause this is more of a geeky computer science concept,
but batch processing has been around since the mainframe,
and that's become normal.
Databases, et cetera, software.
But now, over the past 8 years in particular,
as big data and unstructured data
has proliferated in massive scale,
certainly now with internet of things you see it booming.
This notion of real time data in motion.
You have two paradigms out there,
batch processing, which is well known
and data in motion, which is essentially real time.
Self-driving cars ...
Evidence is everywhere, where this is going.
Real time is not near real time.
>> That's right.
>> In nanoseconds, people want results.
This is a fundamental data challenge.
What's your thoughts on this and how does this
relate to how big data will evolve for customers?
>> I think you're exactly right.
I think as big data came,
and people were able to process data,
and understand it better, and derive insights from it,
very quickly for competitive reason, they find out
that they want those insights sooner and sooner.
They couldn't get it soon enough.
So, you have those opposing trends of more and more data,
yet at the same time, faster and faster insight.
Where does that go?
When you really come down to it,
people don't really want to do batch processing.
They do batch processing,
cause that was the technology that they have.
If they have their way, they don't want to just ...
Information is coming into their business.
Customers are interacting
with their products constantly, 24 by 7.
Those events, if you will, that are giving them insights
are happening all the time.
Except, for a long time, they store it into a file.
They wait til midnight, and then they process it overnight.
More and more there are now capabilities
in memory distributed to do that processing as it comes in.
That's one of the big motivations for forming DataTorrent.
>> I want to get to DataTorrent in a minute,
but I want to get some of these trends,
cause I think they're important to kind of
put together the two big pieces of the puzzle, if you will.
One is, you mentioned batch processing in real time.
The companies, historically, have built their infrastructure
and their operations IT, and whatever, around that,
how storage was procured and deployed.
Now with IOT and the edge of the network becoming huge,
it's a big deal.
So, data in motion, it's pretty much well agreed upon
amongst most of the smart people, this is a big issue.
Let me throw a little wrench in the equation.
Cloud computing kind of changes the security paradigm.
There's no perimeter anymore,
so there's no door you can secure, no firewall model.
Once you get in, you're in.
That's where we've seen a lot of attacks
on ransomware and a lot of cyber attacks.
The penetration is everywhere.
Now there's APIs and everything.
When you bring cloud into it,
and you bring in the fact that you've got data in motion,
what is the challenge for the customer?
How do top architects get their arms around this?
What's the solution? What's your vision on that?
>> Well, I will start by saying it's a hard problem.
I think you're absolutely right.
I think we're still in the phase where
the problems are very visible about how do you solve this.
I think we're still, as an industry,
figuring out how to solve it,
cause you're right, the security issue ...
Security is not this one point tool.
It's an entire ecosystem process for doing that.
The cloud opens up all of those opportunities
for fraud and so on.
It's still an ongoing challenge.
I think the trend of memory becoming cheaper and cheaper,
so that things are done more in memory and less in storage
could actually help a bit on that.
But overall, security internal, external processes are ...
>> It's a moving train.
>> Yeah, it's moving. Exactly.
>> Let me ask you about the big other
trend to throw on top of this.
This is really kind of where you see a lot of the activity,
although some will claim that the app store is
not seeing as many apps now as they used to be.
Certainly the enterprises,
massive growth and application development.
So, ready-made apps with DevOps and Cloud have built
a whole culture of infrastructure as code,
which is essentially saying that I'm going to build apps
and make the infrastructure kind of invisible.
You're seeing a lot of apps like that,
called ready-made apps, however you want to call it.
Those are the things.
How are you guys at DataTorrent handling
and supporting that trend?
>> We are right smack in the middle of exactly that trend.
One of theses that we had was that big data's hard.
Hadoop is hard. Hadoop is now 12 years old.
Lots of people are using Hadoop, trying Hadoop,
but yet it's still not something
that is fully operationalized and easy for everybody.
I think that part of that is big data's hard,
distributed processing is hard, how to get all that working.
There were two things we were focusing on.
One was the real time thing.
The other one was, how do we make this stuff
a lot easier to use?
So, we focus a lot on building tools on top of
the open source engine, if you will,
to kind of make it easy to use.
The other one is exactly that, ready-made apps.
As we continue to learn in working with our customers,
and starting to see the patterns,
putting kind of, bigger functional block together,
so that it's easier to kind of build
these big data application at this next layer.
Machine learning, rule engines, whatever not.
How do you piece that together in a way that is
80 percent done, so that the customer only has
a little bit, the last mile.
>> So, you guys want to be the tooling for that?
>> Yeah, I think so.
I think you have to.
This stuff, you have to kind of go through the whole six
layer of what it takes to get the final business value out.
You're not going to have the skillset to do it.
The more we can abstract and get it to the top, the better.
>> Every company's got their own DNA.
Intel has Moore's Law.
You're the co-founder of DataTorrent.
What's the DNA of your company, as the founder?
Talk about what's the, what do employees
you try to instill into your culture
that is the DNA that you want to be known for?
>> Interesting.
So, I start out sort of on the technical or product side.
Actually, our DNA is all about ops.
We think that, especially in big data,
there's lots of ways to do prototypes
and get some proof of concept going..
Getting that to production, to run it 24 by 7,
never lose data, that really has been hard.
Our entire existence is around how to truly build
24 by 7, no data, fast application.
All of our engineers live and breathe how to do that well.
>> Ops is consistent with stability.
It's interesting, Silicon Valley's going through
its own transformation around programmers
and the role of entrepreneurship.
It's interesting, in the enterprise,
they always kind of were like, "Oh, no big deal."
Because at the end of the day they need stuff
to run at five 9. These are networks.
The old saying that Mark Zuckerberg used to have is,
"Move fast and break stuff."
They've changed their tune to,
"Move fast and be a hundred percent reliable."
This is the trend that the enterprises
will always put out there.
How do companies stay and maintain that ops integrity
and still be innovative without a lot of
command and control, and compliance restrictions?
How do they experiment with this
data tsunami that's happening and maintain that integrity?
>> My answer to that is, I think, as an industry,
we have to build products and tools to allow for that.
Some of that is processes inside a company,
but I think a lot of that can be productize.
The advances in that big data processing layer,
and how to recover, get new containers,
and do all the right things, allow for the application
developer not to have to worry about many of those segments.
I think technology exists out there for tools to be
developed to deal with a lot of that.
>> I love talking with entrepreneurs
and you're the co-founder of DataTorrent.
Talk about the journey you've been on from the beginning.
You have a new CEO, which as the CEO,
you want to lighten the load up a little bit.
It gets bigger, you got to have HR issues,
things are happening.
You're putting culture in place and
trying to scale out and get a groove swing.
Certainly Uber could've taken a few tips from your playbook,
as bringing in senior management.
You did it at the right time.
Talk about your journey, the company,
and what people should know about DataTorrent.
>> We're just a bunch of guys that are just still trying
to make a contribution to the industry.
I think we saw an opportunity to really help people move
towards big data, move towards real time analytics,
and really help them solve some really hairy problems
that they have coming up with data.
From a skillset and personally, I think kind of
my particular strength was really about that initial vision.
Be able to build out a set of capabilities,
and maybe get a first set of half a dozen wins,
and really prove point.
To sort of make it into a machine that has all the right
marketing tools, and business development tools, and so on.
It will be great to bring in someone like Guy,
who has done that many, many times over,
and has been super successful at that,
to take us to the next level.
>> Takes a lot of self awareness, too.
You probably had your moments where,
should you stay on, be the CEO ...
But, what are you doing now, cause you get down
and you can get into the products.
Are you doing a lot more product tinkering?
Are you involved the road map?
What's your involvement day-to-day now?
>> I love it, cause it's exactly what I enjoy most,
which is really interacting with customers and users
and really continue to hone in on the product market fit.
And continue to understand, what are the pain points?
What are the issues? And, how can we solve it?
All coming from, not so much a services mentality,
but a product mentality.
>> At the cloud ops, too. That's a big area.
So, what's the big problem that you solve for the customers?
What's the big, hairy problem?
>> Really easy, how to productize,
how to operationalize this data pipeline that they have,
so that they can truly be accepting real live business data
that they are getting in, and giving them the insight.
>> Been a lot of talk about automation and AI, lately.
Obviously, it's a buzzword.
Wikibon just put out a report called True Private Cloud
that shows all the automation's actually going at
and replacing non-differentiated labor,
which actually the racking and stacking gear.
Moving to values, actually is going to be
more employment on that side.
Talk about the role of automation in the data world,
because if you just think about the amount of data
that companies like Facebook and Yahoo take in,
you need machine learning. You need automation.
What is the key to automation in a lot of these
new emerging areas around large data sets?
>> It's so funny, yesterday I was driving.
I was listening to a KQED segment,
and they were talking about in its next phase,
AI and machine learning is going to do sort of
the first layer of all the reporting.
So, you actually have reporters
doing much more sophisticated reporting,
cause there's an AI layer that has a template
of what are the questions to answer,
and they can just spill out all the news for you.
>> Paid by cryptocurrency.
>> Yeah.
I think machine learning and AI will be everywhere.
We will continue to learn, and it will continue to
get better at doing more and more things for us, so that
we get to kind of play at that creative, disruptive layer,
while it does all the menial tasks.
I think it will touch every part of our civilization.
The technology is getting incredible.
The algorithms are incredible.
The power, the computing power to allow for that
is getting exponential.
I think it's super interesting that
the engineers are super interested in it.
Everything we do now revolves around ...
When we talk about the analytics layer at real time,
it's all about machine learning scoring
and how to, rules and all that.
>> Great to have you here on the CUBEConversation.
Give you the last word.
Give a quick plug about DataTorrent.
What should your customers know about you guys?
Why should they call you?
>> We're a company solely focused on
bringing big data applications to production.
We focus on making sure that as long as you understand
what you want to do with data, we can make it super fast,
super reliable, super scalable. All that stuff.
>> Co-founder of DataTorrent here
and the CUBEConversation here in Palo Alto.
I'm John Furrier. Thanks for watching.
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