Live from New York
It's theCUBE!
Covering theCUBE New York City 2018
Brought to you by Silicon Angle Media
and it's ecosystem partners
>> Hey welcome back and we're live in theCUBE
in New York city.
It's our 2nd day of two days of coverage CUBE NYC.
The hashtag CUBENYC
Formerly Big data NYC renamed because it's about big data,
it's about the server,
it's about Cooper _________'s multi-cloud data.
It's all about data, and that's the fundamental change
in the industry.
Our next guest is Yaron Haviv, who's the CTO of
Iguazio, key alumni, always coming out
with some good commentary smart analysis.
Kind of a guest host
as well as an industry participant supplier.
Welcome back to theCUBE. Good to see you.
>> Thank you John.
>> Love having you on theCUBE because you always
bring some good insight and we appreciate that.
Thank you so much.
First, before we get into some of the comments because
I really want to delve into comments that David Richards
said a few years ago, CEO of RenDisco.
He said, "Cloud's going to kill Hadoop".
And people were looking at him like,
"Oh my God, who is this heretic? He's crazy.
What is he talking about?"
But you might not need Hadoop, if you can run server
list spark tentraflow.
You talk about this off camera.
Is Hadoop going to be the open stack of the big data world?
>> I don't think cloud necessary killed Hadoop,
although it is working on that, you know because
you go to Amazon and you know,
you can consume a bunch of services and you don't really
need to think about Hadoop.
I think cloud native serve is starting to kill Hadoop,
cause Hadoop is three layers, you know,
it's a file system,
it's DFS, and then you have server scheduling __________,
then you have applications starting with map produce
and then you evolve into things like Spark.
Okay, so, file system I don't really need in the cloud.
I use Asfree, I can use a database as a service,
as you know, pretty efficient way of storing data.
For scheduling, __________ is a much more generic way
of scheduling workloads and not confined to Spark
and specific workloads.
I can run with Dancerflow,
I can run with data science tools, etc.,
just containerize.
So essentially, why would I need Hadoop?
If I can take the traditional tools people are now evolving
in and using like Jupiter Notebooks, Spark, Dancerflow,
you know, those packages with Coop & Eddy's on top of
a database as a service and some object store,
I have a much easier stack to work with.
And I could mobilize that whether it's in the cloud,
you know on different vendors.
>> Scale is important too.
How do you scale it?
>> Of course, you have independent scaling
between data and computation, unlike Hadoop.
So I can just go to Google, and use Vquery,
or use, you know, ______________ on Amazon or Redchick,
or whatever and automatically scale it down
and then, you know
>> That's a unique position, so essentially,
Hadoop versus Coop & Eddy's is a top-line story.
And wouldn't that be ironic for Google,
because Google essentially created Map Produce and ________
went public, but when we're talking about 2008 timeframe,
2009 timeframe, back when ventures with cloud
were just emerging in the mainstream.
So wouldn't it be ironic Cooper & Eddy's,
which is being driven by Google,
ends up taking over Hadoop?
In terms of running things on Cooper & Eddy's
and cloud eight on Visa Vis on premise with Hadoop.
>> The poster is tend to give this comment about Google,
but essentially Yahoo started Hadoop.
Google started the __________ and couple of years
after Hadoop started, with Google they essentially moved
to a different architecture,
with something called Percolator.
So Google's not too associated with Hadoop.
They're not really using this approach for a long time.
>> Well they wrote the map-produced paper
and the internal conversations we report on theCUBE
about Google was, they just let that go.
And Yahoo grabbed it.
(cross-conversation)
>> The companies that had the most experience
were the first to leave.
And I think it may respect what you're saying.
As the marketplace realizes the outcomes of the
dubious associate with, they will find other ways
of achieving those outcomes.
It might be more depth.
>> There's also a fundamental shift
in the consumption where Hadoop was about
a ranking pages in a batch form.
You know, just collecting logs and ranking pages, okay.
The chances that people have today revolve around
applying a _______ to business application.
It needs to be a lot more concurring, transactional,
real-time ish, you know?
It's nothing to do with Hadoop, okay?
So that's why you'll see more and more workers,
mobilizing different black server functions, into
service pre-canned services, etc.
And Coop & Eddy's playing a good role here
is providing the trend.
Transport for migrating workloads across cloud providers,
because I can use _________, the Google Coop & Eddys,
or Amazon Coop & Eddy's, or Azure Coop & Eddys,
and I could write a similar application and deploy it
on any cloud, or on Clam on my own private cluster.
It makes the infrastructure agnostic really
application focused.
>> ____________ about Cooper & Eddys we heard on theCUBE
earlier, the VP of Project BlueData said that Coop & Eddy's
ecosystem and community needs to do a better job
with Stapla, they nailed Stapflalis,
Stafle application support is
something that they need help on.
Do you agree with that comment,
and then if so, what alternatives do you have for
customers who care about Stafe?
>> They should use our product
(laughing)
>> (mumbling) Is Coop & Eddy's struggling there?
And if so, talk about your product
>> So, I think that our challenge is rounded that
there are many solutions in that.
I think that they are attacking it from a different approach
Many of them are essentially providing some block storage
to different containers on really cloud 90.
What you want to be able is to have multiple containers
access the same data.
That means either sharing through file systems,
for objects or through databases because one container
is generating, for example, ingestion or __________.
Another container is manipulating that same data.
A third container may look for something in the data,
and generate a trigger or an action.
So you need shared access to data from those containers.
>> The rest of the data synchronizes
all three of those things.
>> Yes because the data is the form of state.
The form of state cannot be associated with the same
container, which is what most of where
I am very active and sincere in those committees,
and you have all the storage guys in the committees,
and they think the block story just drag solution.
Cause they still think like virtual machines, okay?
But the general idea
is that if you think about Coop & Eddy's
is like the new OS, where you have many processes,
they're just scattered around.
In OS, the way for us to share state between processes
an OS, is whether through files,
or through databases, in those form.
And that's really what
>> Threads and databases as a positive engagement.
>> So essentially I gave maybe two years ago,
a session at CueCon in Europe about what we're doing
on storing state.
It's really high-performance access from those
container processes to our database.
Impersonate objects, files, streams or time series data, etc
And then essentially, all those workloads
just mount on top of and we can all share stape.
We can even control the access for each
>> Do you think you nailed the stape problem?
>> Yes, by the way, we have a managed service.
Anyone could go today to our cloud, to our website,
that's in our cloud.
It gets it's own Coop & Eddy's cluster, a provision within
less than 10 minutes, five to 10 minutes.
With all of those services pre-integrated with Spark,
Presto, ______________, real-time, these services functions.
All that pre-configured on it's own time.
I figured all of these-
>> 100% compatible with Coop & Eddy's,
it's a good investment
>> Well we're just expanding it to Coop & Eddy's __________,
now it's working on them, Amazon Coop & Eddy's,
EKS I think, we're working on AKS and GK.
We partner with Azure and Google.
And we're also building an ad solution that is essentially
exactly the same stock.
Can run on an edge appliance in a factory.
You can essentially mobilize data and functions
back and forth.
So you can go and develop your work loads, your application
in the cloud, test it under simulation,
push a single button and teleport the artifacts
into the edge factory.
>> So is it like a real-time Coop & Eddy's?
>> Yes, it's a real-time Coop & Eddy's.
>> If you _______like the things we're doing,
it's all real-time.
>> Talk about real-time in the database world
because you mentioned time-series databases.
You give objects store versus blog.
Talk about time series.
You're talking about data
that is very relevant in the moment.
And also understanding time series data.
And then, it's important post-event, if you will, meaning
How do you store it? Do you care?
I mean, it's important to manage the time series.
At the same time, it might not be as valuable as other data,
or valuable at certain points and time,
which changes it's relationship to how it's stored
and how it's used.
Talk about the dynamic of time series..
>> Figured it out in the last six or 12 months that
since real-time is about time series.
Everything you think about real-time censored data,
even video is a time-series of frames, okay
And what everyone wants to do is
just huge amount of time series.
They want to cross-correlate it, because for example,
you think about stock tickers you know,
the stock has an impact from news feeds or Twitter feeds,
or of a company or a segment.
So essentially, what they need to do is something called
multi-volume analysis of multiple time series
to be able to extract some meaning,
and then decide if you want to sell or buy a stock,
as in vacation example.
And there is a huge gap in the solution in that market,
because most of the time series databases were designed for
operational databases, you know, things that monitor apps.
Nothing that injects millions of data points per second,
and cross-correlates and run real-time AI analytics.
Ah, so we've essentially extended because we have a
programmable database essentially under the hoop.
We've extended it to support time series data
with about 50 to 1 compression ratio,
compared to some other solutions.
You know we've break with the customer,
we've done sizing,
they told them us they need half a pitabyte.
After a small sizing exercise, about 10 to 20 terabytes
of storage for the same data they stored in _____________
for 500 terabytes.
No huge ingestion rates, and what's very important,
we can do an in-flight with all those cross-correlations,
so, that's something that's working very well for us.
>> This could help on smart mobility.
Kenex 5G comes on, certainly.
Intelligent edge.
>> So the customers we have,
these cases that we applied right now
is in financial services,
two or three main applications.
One is tick data and analytics, everyone wants to be smarter
learning on how to buy and sell stocks or manage risk,
the second one is infrastructure,
monitoring, critical infrastructure,
monitoring is SLA monitoring is be able to monitor network
devices, latencies, applications, you now, transaction rate,
or that, be able to predict potential failures or escalation
We have ___________ because we have about three Telco
customers using it for real-time time.
Series analytics are metric data, cybersecurity attacks,
congestion avoidance, SLA management, and also _________.
Fleet management, file linking, they are also essentially
feeding huge data sets of time series analytics.
They're running cross-correlation and AI logic,
so now they can generate triggers.
Now apply to Hadoop.
What does Hadoop have anything to do
with those kinds of applications?
They cannot feed huge amounts of datasets,
they cannot react in real-time,
doesn't store time-series efficiently.
>> Hapoop
(laughing)
>> You said that.
>> Yeah. That's good.
>> One, I know we don't have a lot of time left.
We're running out of time, but I want to make sure
we get this out here.
How are you engaging with customers?
You guys got great technical support.
We can vouch for the tech chops that you guys have.
We seen the solution.
If it's compatible to Coop & Eddy's,
certainly this is an alternative
to have really great analytical infrastructure.
Cloud native, goodness of your building,
You do PFC's, they go to your website,
and how do you engage, how do you get deals?
How do people work with you?
>> So because now we have a cloud service,
so also we engage through the cloud.
Mainly, we're going after customers _________________,
or from webinars and activities on the internet,
and we sort of follow-up with those customers, we know
>> Direct sales?
>> Direct sales, but through lead generation mechanism.
________ activity, Amazon, Azure,
>> Partnerships with Azure and Google now.
And Azure joint selling activities.
They can actually resale and get compensated.
Our solution is an edge for Azure.
Working on similar solution for Google.
Very focused on retailers.
That's the current market focus of
since you think about stores that have
a single supermarket will have more than a 1,000 cameras.
Okay, just because they're monitoring shelves in real-time,
think about Amazon go, kind of replication.
Real-time ____________ management.
You cannot push a 1,000 camera feeds into the cloud.
In order to analyze it then decide on _______level.
Proactive action, so, those are the kind of applications.
>> So bigger deals, you've had some big deals.
>> Yes, we're really not a raspberry pie-kind of solution.
That's where the bigger customers
>> Got it.
Yaron, thank you so much. The CTO of Iguazio
Check him out.
It's actually been great commentary.
The Hadoop versus Coop & Eddy's narrative.
Love to explore that further with you.
Stay with us for more coverage after this short break.
We're live in day 2 of CUBE NYC.
Par Strata, Hadoop Strata, Hadoop World.
CUBE Hadoop World, whatever you want to call it.
It's all because of the data.
We'll bring it to ya.
Stay with us for more after this short break.
(upbeat music)