>> Announcer: Live from midtown Manhattan, it's theCUBE,
covering BigData New York City 2017,
brought to you by SiliconANGLE Media
and its ecosystem sponsors.
>> Okay, welcome back everyone,
we're live in New York City, this is theCUBE's coverage
of BigData NYC, this is our own event for five years now
we've been running it, been at Hadoop World since 2010,
it's our eighth year covering the Hadoop World
which has evolved into Strata Conference, Strata Hadoop,
now called Strata Data, and of course it's
bigger than just Strata, it's about big data in NYC,
a lot of big players here inside theCUBE,
thought leaders, entrepreneurs, and great guests.
I'm John Furrier, the cohost this week with Jim Kobielus,
who's the lead analyst on our BigData and our Wikibon team.
Our next guest is Yaron Haviv, who's with iguazio,
he's the founder and CTO, hot startup here at the show,
making a lot of waves on their new platform.
Welcome to theCUBE, good to see you again, congratulations.
>> Yes, thanks, thanks very much.
We're happy to be here again.
>> You're known in the theCUBE community as
the guy on Twitter who's always pinging me and Dave
and team, saying, "Hey, you know, you guys got to
"get that right."
You really are one of the smartest guys on the network
in our community, you're super-smart,
your team has got great tech chops,
and in the middle of all that is the hottest market
which is cloud native, cloud native as it relates to
the integration of how apps are being built,
and essentially new ways of engineering around
these solutions, not just repackaging old stuff,
it's really about putting things in
a true cloud environment, with an application development,
with data at the center of it,
you got a whole complex platform you've introduced.
So really, really want to dig into this.
So before we get into some of my pointed questions
I know Jim's got a ton of questions,
is give us an update on what's going on
so you guys got some news here at the show,
let's get to that first.
>> So since the last time we spoke, we had tons of news.
We're making revenues, we have customers,
we've just recently GA'ed,
we recently got significant investment from major investors,
we raised about $33 million recently
from companies like Verizon Ventures,
Bosch, you know for IoT, Chicago Mercantile Exchange,
which is Dow Jones and other properties, Dell EMC.
So pretty broad.
>> John: So customers, pretty much.
>> Yeah, so that's the interesting thing.
Usually you know investors are sort of strategic investors
or partners or potential buyers,
but here it's essentially our customers
that it's so strategic to the business,
we want to...
>> Let's go with GA of the projects,
just get into what's shipping, what's available,
what's the general availability, what are you now offering?
>> So iguazio is trying to, you know, you alluded to
cloud native and all that.
Usually when you go to events like Strata and BigData
it's nothing to do with cloud native,
a lot of hard labor, not really continuous development
and integration, it's like continuous hard work,
it's continuous hard work.
And essentially what we did,
we created a data platform which is extremely fast
and integrated, you know has all the different forms
of states, streaming and events and documents
and tables and all that,
into a very unique architecture,
won't dive into that today.
And on top of it we've integrated cloud services
like Kubernetes and serverless functionality and others,
so we can essentially create a hybrid cloud.
So some of our customers they even deploy portions
as an Opix-based settings in the cloud,
and some portions in the edge or in the enterprise
deployed the software, or even a prepackaged appliance.
So we're the only ones that provide
a full hybrid experience.
>> John: Is this a SAS product?
>> So it's a software stack, and it could be delivered
in three different options.
One, if you don't want to mess with the hardware,
you can just rent it, and it's deployed in Equanix facility,
we have very strong partnerships with them globally.
If you want to have something on-prem,
you can get a software reference architecture,
you go and deploy it.
If you're a telco or an IoT player
that wants a manufacturing facility,
we have a very small 2U box, four servers, four GPUs,
all the analytics tech you could think of.
You just put it in the factory
instead of like two racks of Hadoop.
>> So you're not general purpose, you're just
whatever the customer wants to deploy the stack,
their flexibility is on them.
>> Yeah.
Now it is an appliance >> You have a hosting solution?
>> It is an appliance even when you deploy it on-prem,
it's a bunch of Docker containers inside
that you don't even touch them,
you don't SSH to the machine.
You have APIs and you have UIs,
and just like the cloud experience when you go to Amazon,
you don't open the Kimono, you know, you just use it.
So our experience that's what we're telling customers.
No root access problems, no security problems.
It's a hardened system.
Give us servers, we'll deploy it,
and you go through consoles and UIs,
>> You don't host anything for anyone?
>> We host for some customers, including
>> So you do whatever the customer
was interested in doing?
>> Yes. (laughs)
>> So you're flexible, okay.
>> We just want to make money.
>> You're pretty good, sticking to the product.
So on the GA, so here essentially the big data world
you mentioned that there's data layers, like data piece.
So I got to ask you the question,
so pretend I'm an idiot for a second, right.
>> Yaron: Okay.
>> Okay, yeah.
>> No, you're a smart guy.
>> What problem are you solving.
So we'll just go to the simple.
I love what you're doing, I assume you guys are super-smart,
which I can say you are,
but what's the problem you're solving,
what's in it for me?
>> Okay, so there are two problems.
One is the challenge everyone wants to transform.
You know there is this digital transformation mantra.
And it means essentially two things.
One is, I want to automate my operation environment
so I can cut costs and be more competitive.
The other one is I want to improve my customer engagement.
You know, I want to do mobile apps which are smarter,
you know get more direct content to the user,
get more targeted functionality, et cetera.
These are the two key challenges for every business,
any industry, okay?
So they go and they deploy Hadoop and Hive
and all that stuff, and it takes them two years
to productize it.
And then they get to the data science bit.
And by the time they finished they understand
that this Hadoop thing can only do one thing.
It's queries, and reporting and BI, and data warehousing.
How do you do actionable insights from that stuff, okay?
'Cause actionable insights means I get information
from the mobile app, and then I translate it
into some action.
I have to enrich the vectors, the machine learning,
all that details.
And then I need to respond.
Hadoop doesn't know how to do it.
So the first generation is people that
pulled a lot of stuff into data lake,
and started querying it and generating reports.
And the boss said >> Low cost data link
basically, was what you say. >> Yes, and the boss said,
"Okay, what are we going to do with this report?
"Is it generating any revenue to the business?"
No.
The only revenue generation
if you take this data >> You're fired, exactly.
>> No, not all fired, but now
>> John: Look at the budget
>> Now they're starting to buy our stuff.
So now the point is okay, how can I put all this data,
and in the same time generate actions,
and also deal with the production aspects of,
I want to develop in a beta phase,
I want to promote it into production.
That's cloud native architectures, okay?
Hadoop is not cloud,
How do I take a Spark, Zeppelin, you know, a notebook
and I turn it into production?
There's no way to do that.
>> By the way, depending on which cloud you go to,
they have a different mechanism
and elements for each cloud.
>> Yeah, so the cloud providers do address that because
they are selling the package,
>> Expands all the clouds, yeah.
>> Yeah, so cloud providers are starting to have
their own offerings which are all proprietary
around this is how you would, you know,
forget about HDFS, we'll have S3,
and we'll have Redshift for you, and we'll have Athena,
and again you're starting to consume that into a service.
Still doesn't address the continuous analytics challenge
that people have.
And if you're looking at what we've done with Grab,
which is amazing, they started with using Amazon services,
S3, Redshift, you know, Kinesis, all that stuff,
and it took them about two hours to generate the insights.
Now the problem is they want to do driver incentives
in real time.
So they want to incent the driver to go and make more rides
or other things, so they have to analyze the event
of the location of the driver,
the event of the location of the customers,
and just throwing messages back based on analytics.
So that's real time analytics,
and that's not something that you can do
>> They got to build that from scratch right away.
I mean they can't do that with the existing.
>> No, and Uber invested tons of energy around that
and they don't get the same functionality.
Another unique feature that we talk about in our PR
>> This is for the use case you're talking about,
this is the Grab, which is the car
>> Grab is the number one ride-sharing in Asia,
which is bigger than Uber in Asia,
and they're using our platform.
By the way, even Uber doesn't really use Hadoop,
they use MemSQL for that stuff,
so it's not really using open source and all that.
But the point is for example, with Uber,
when you have a, when they monetize the rides,
they do it just based on demand, okay.
And with Grab, now what they do,
because of the capability that we can intersect
tons of data in real time, they can also look at
the weather, was there a terror attack
or something like that.
They don't want to raise the price
>> A lot of other data points, could be traffic
>> They don't want to raise the price
if there was a problem, you know,
and all the customers get aggravated.
This is actually intersecting data in real time,
and no one today can do that in real time
beyond what we can do.
>> A lot of people have semantic problems with real time,
they don't even know what they mean by real time.
>> Yaron: Yes.
>> The data could be a week old,
but they can get it to them in real time.
>> But every decision, if you think if you generalize
round the problem, okay,
and we have slides on that that I explain to customers.
Every time I run analytics, I need to look at
four types of data.
The context, the event, okay, what happened, okay.
The second type of data is the previous state.
Like I have a car, was it up or down or
what's the previous state of that element?
The third element is the time aggregation,
like, what happened in the last hour,
the average temperature, the average, you know,
ticker price for the stock, et cetera, okay?
And the fourth thing is enriched data,
like I have a car ID, but what's the make,
what's the model, who's driving it right now.
That's secondary data.
So every time I run a machine learning task
or any decision I have to collect all those
four types of data into one vector,
it's called feature vector,
and take a decision on that.
You take Kafka, it's only the event part, okay,
you take MemSQL, it's only the state part,
you take Hadoop it's only like historical stuff.
How do you assemble and stitch a feature vector.
>> Well you talked about complex machine learning pipeline,
so clearly, you're talking
about a hybrid >> It's a prediction.
And actions based on just dumb things,
like the car broke and I need to send a garage,
I don't need machine learning for that.
>> So within your environment then,
do you enable the machine learning models
to execute across the different data platforms,
of which this hybrid environment is composed,
and then do you aggregate the results of those models,
runs into some larger model that drives
the real time decision?
>> In our solution, everything is a document,
so even a picture is a document, a lot of things.
So you can essentially throw in a picture,
run tensor flow, embed more features into the document,
and then query those features on another platform.
So that's really what makes this continuous analytics
extremely flexible, so that's what we give customers.
The first thing is simplicity.
They can now build applications,
you know we have tier one now, automotive customer,
CIO coming, meeting us.
So you know when I have a project,
one year, I need to have hired dozens of people,
it's hugely complex, you know.
Tell us what's the use case, and we'll build a prototype.
>> John: All right, well I'm going to
>> One week, we gave them a prototype,
and he was amazed how in one week we created
an application that analyzed all the streams from the data
from the cars, did enrichment, did machine learning,
and provided predictions.
>> Well we're going to have to come in and test you on this,
because I'm skeptical,
but here's why. >> Everyone is.
>> We'll get to that, I mean I'm probably not skeptical
but I kind of am because the history is pretty clear.
If you look at some of the big ideas out there,
like OpenStack.
I mean that thing just morphed into a beast.
Hadoop was a cost of ownership nightmare
as you mentioned early on.
So people have been conceptually correct
on what they were trying to do,
but trying to get it done was always hard,
and then it took a long time to kind of figure out
the operational model.
So how are you different,
if I'm going to play the skeptic here?
You know, I've heard this before.
How are you different than say OpenStack or Hadoop Clusters,
'cause that was a nightmare, cost of ownership,
I couldn't get the type of value I needed,
lost my budget.
Why aren't you the same?
>> Okay, that's interesting.
I don't know if you know but I ran a lot of development
for OpenStack when I was in Matinox and Hadoop,
so I patched a lot of those
>> So do you agree with what I said?
That that was a problem?
>> They are extremely complex, yes.
And I think one of the things that first OpenStack
tried to bite on too much,
and it's sort of a huge tent,
everyone tries to push his agenda.
OpenStack is still an infrastructure layer, okay.
And also Hadoop is sort of a something in between
an infrastructure and an application layer,
but it was designed 10 years ago,
where the problem that Hadoop tried to solve
is how do you do web ranking, okay,
on tons of batch data.
And then the ecosystem evolved into real time,
and streaming and machine learning.
>> A data warehousing alternative or whatever.
>> So it doesn't fit the original model of batch processing,
'cause if an event comes from the car or an IoT device,
and you have to do something with it,
you need a table with an index.
You can't just go and build a huge Parquet file.
>> You know, you're talking about complexity
>> John: That's why he's different.
>> Go ahead.
>> So what we've done with our team,
after knowing OpenStack and all those
>> John: All the scar tissue.
>> And all the scar tissues, and my role was also
working with all the cloud service providers,
so I know their internal architecture,
and I worked on SAP HANA and Exodata and all those things,
so we learned from the bad experiences,
said let's forget about the lower layers,
which is what OpenStack is trying to provide,
provide you infrastructure as a service.
Let's focus on the application,
and build from the application all the way to the flash,
and the CPU instruction set,
and the adapters and the networking, okay.
That's what's different.
So what we provide is an application and service experience.
We don't provide infrastructure.
If you go buy VMware and Nutanix, all those offerings,
you get infrastructure.
Now you go and build with the dozen of dev ops guys
all the stack above.
You go to Amazon, you get services.
Just they're not the most optimized in terms of
the implementation because they also have
dozens of independent projects that each one
takes a VM and starts writing some
>> But they're still a good service,
but you got to put it together.
>> Yeah right.
But also the way they implement,
because in order for them to scale
is that they have a common layer, they found VMs,
and then they're starting to build up applications
so it's inefficient.
And also a lot of it is built on 10-year-old
baseline architecture.
We've designed it for a very modern architecture,
it's all parallel CPUs with 30 cores, you know,
flash and NVMe.
And so we've avoided a lot of the hardware challenges,
and serialization, and just provide and abstraction layer
pretty much like a cloud on top.
>> Now in terms of abstraction layers in the cloud,
they're efficient, and provide a simplification experience
for developers.
Serverless computing is up and coming,
it's an important approach, of course we have
the public clouds from AWS and Google
and IBM and Microsoft.
There are a growing range of serverless computing
frameworks for prem-based deployment.
I believe you are behind one.
Can you talk about what you're doing at iguazio
on serverless frameworks for on-prem or public?
>> Yes, it's the first time I'm very active
in CNC after Cloud Native Foundation.
I'm one of the authors of the serverless white paper,
which tries to normalize the definitions
of all the vendors and come with a proposal
for interoperable standard.
So I spent a lot of energy on that,
'cause we don't want to lock customers to an API.
What's unique, by the way, about our solution,
we don't have a single proprietary API.
We just emulate all the other guys' stuff.
We have all the Amazon APIs for data services,
like Kinesis, Dynamo, S3, et cetera.
We have the open source APIs, like Kafka.
So also on the serverless, my agenda is trying to promote
that if I'm writing to Azure or AWS or iguazio,
I don't need to change my app.
I can use any developer tools.
So that's my effort there.
And we recently, a few weeks ago,
we launched our open source project,
which is a sort of second generation of
something we had before called Nuclio.
It's designed for real time
>> John: How do you spell that?
>> N-U-C-L-I-O.
I even have the logo >> He's got a nice slick here.
>> It's really fast because it's
>> John: Nuclio, so that's open source
that you guys just sponsor and it's all
code out in the open?
>> All the code is in the open, pretty cool,
has a lot of innovative ideas on how to do
stream processing and best,
'cause the original serverless functionality
was designed around web hooks and HTTP,
and even many of the open source projects
are really designed around HTTP serving.
>> I have a question.
I'm doing research for Wikibon
on the area of serverless, in fact we've recently
published a report on serverless,
and in terms of hybrid cloud environments,
I'm not seeing yet any hybrid serverless clouds
that involve public, you know, serverless like AWS Lambda,
and private on-prem deployment of serverless.
Do you have any customers who are doing that
or interested in hybridizing serverless
across public and private?
>> Of course, and we have some patents I don't want to
go into, but the general idea is,
what we've done in Nuclio is also the decoupling of the data
from the computation, which means that things
can sort of be disjoined.
You can run a function in Raspberry Pi,
and the data will be in a different place,
and those things can sort of move, okay.
>> So the persistence has to happen
outside the serverless environment,
like in the application itself?
>> Outside of the function, the function acts as
the persistent layer through APIs, okay.
And how this data persistence is materialized,
that server separate thing.
So you can actually write the same function
that will run against Kafka or Kinesis or Private MQ,
or HTTP without modifying the function,
and ad hoc, through what we call function bindings,
you define what's going to be the thing driving the data,
or storing the data.
So that can actually write the same function
that does ETL drop from table one to table two.
You don't need to put the table information
in the function, which is not the thing that Lambda does.
And it's about a hundred times faster than Lambda,
we do 400,000 events per second in Nuclio.
So if you write your serverless code in Nuclio,
it's faster than writing it yourself,
because of all those low-level optimizations.
>> Yaron, thanks for coming on theCUBE.
We want to do a deeper dive, love to have you
out in Palo Alto next time you're in town.
Let us know when you're in Silicon Valley for sure,
we'll make sure we get you on camera
for multiple sessions.
>> And more information re:Invent.
>> Go to re:Invent.
We're looking forward to seeing you there.
Love the continuous analytics message,
I think continuous integration is going through
a massive renaissance right now,
you're starting to see new approaches,
and I think things that you're doing
is exactly along the lines of what the world wants,
which is alternatives, innovation,
and thanks for sharing on theCUBE.
>> Great. >> That's very great.
>> This is theCUBE coverage of the hot startups here
at BigData NYC, live coverage from New York,
after this short break.
I'm John Furrier, Jim Kobielus, after this short break.