Okay. Now we're going to dig deeper into HPE Ezmeral
and try to better understand
how it's going to impact customers.
And with me to do that are Robert Christiansen,
who is the Vice President of Strategy
in the office of the CTO and Kumar Sreekanti,
who is the Chief Technology Officer and Head of Software,
both of course, with Hewlett Packard Enterprise.
Gentlemen, welcome to the program. Thanks for coming on.
Good seeing you, Dave.
Thanks for having us.
It's always good to see you guys.
Thanks for having us.
So, Ezmeral, kind of an interesting name, catchy name,
but Kumar, what exactly is HPE Ezmeral?
It's indeed a catchy name.
Our branding team has done fantastic job.
I believe it's actually derived from Esmeralda,
is the Spanish for emarald.
Often it's supposed some very mythical bars,
and they derived Ezmeral from there.
And we all initially when we heard, it was interesting.
So, Ezmeral was our effort to take all the software,
the platform tools that HPE has
and provide this modern operating platform to the customers
and put it under one brand.
So, it has a modern container platform,
it does persistent storage with the data fabric
and it doesn't include as many of our customers from that.
So, think of it as a modern container platform
for modernization and digitazation for the customers.
Yeah, it's an interesting, you talk about platform,
so it's not, you know, a lot of times people say product,
but you're positioning it as a platform
so that has a broader implication.
That's very true.
So, as the customers are thinking of this digitazation,
modernization containers and Microsoft, as you know,
there is, has become the stable all.
So, it's actually a container orchestration platform
with golfers open source going into this
as well as the persistence already.
So, by the way, Ezmeral, I think Emerald in Spanish,
I think in the culture, it also has immunity powers as well.
So immunity from lock-in, (Robert and Kumar laughing)
and all those other terrible diseases,
maybe it helps us with COVID too.
Robert, when you talk to customers,
what problems do you probe for
that Ezmeral can do a good job solving?
Yeah, that's a really great question
because a lot of times they don't even know
what it is that they're trying to solve for
other than just a very narrow use case.
But the idea here is to give them a platform
by which they can bridge both the public
and private environment for what they do,
the application development, specifically in the data side.
So, when yo're looking to bring containerization,
which originally got started on the public cloud
and it has moved its way,
I should say it become popular in the public cloud
and it moved its way on premises now,
Ezmeral really opens the door to three fundamental things,
but, you know, how do I maintain an open architecture
like you're referring to,
to some low or no lock-in of my applications.
Number two, how do I gain a data fabric
or a data consistency of accessing the data
so I don't have to rewrite those applications
when I do move them around.
And then lastly, where everybody's heading,
the real value is in the AI ML initiatives
that companies are really bringing
and that value of their data and locking that data
at where the data is being generated and stored.
And so the Ezmeral platform is those multiple pieces
that Kumar was talking about stacked together
to deliver the solutions for the client.
So Kumar, how does it work?
What's the sort of IP or the secret source behind it all?
What makes HPE different?
Yeah. Continuing on (indistinct)
it's a modern glass form of optimizing the data
and workloads.
But I think I would say
there are three unique characteristics of this platform.
Number one is that it actually provides you both an ability
to run statefull and stateless as workloads
under the same platform.
And number two is, as we were thinking about,
unlike another Kubernete is open source,
it actually add, use you all open-source Kurbenates
as well as an orchestration behind them
so you can actually, you can provide this hybrid thing
that Robert was talking about.
And then actually we built the workflows into it,
for example, they'll actually announced along with it
Ezmeral, ML expert on the customers can actually do
the workflow management around specific data woakload.
So, the magic is if you want to see the secrets
out of all the efforts that has been going
into some of the IP acquisitions
that HPE has done over the years,
we said we BlueData, MAPR, and the Nimble,
all these pieces are coming together
and providing a modern digitization platform
for the customers.
So these pieces,
they all have a little bit of a machine intelligence
in them, you have people,
who used to think of AI as this sort of separate thing,
I mean the same thing with containers, right?
But now it's getting embedded into the stack.
What is the role of machine intelligence
or machine learning in Ezmeral?
I would take a step back and say,
you know, there's very well the customers,
the amount of data that is being generated
and 95% or 98% of the data is machine generated.
And it does a series of a window gravity,
and it is sitting at the edge
and we were the only one that had edge
to the cloud data fabric that's built to it.
So, the number one is that we are bringing computer
or a cloud to the data that taking the data to the cloud,
right, if you will.
It's a cloud like experience that provides the customer.
AI is not much value to us if we don't harness the data.
So, I said this in one of the blog was we have gone
from collecting the data, to the finding the insights
into the data, right.
So, that people have used all sorts of analysis
that we are to find data is the new oil.
So, the AI and the data.
And then now your applications have to be modernized
and nobody wants write an application
in a non microservices fashion
because you wanted to build the modernization.
So, if you bring these three things,
I want to have a data gravity with lots of data,
I have built an AI applications and I want to have
those three things I think we bring to the customer.
So, Robert let's stay on customers for a minute.
I mean, I want to understand the business impact,
the business case,
I mean, why should all the cloud developers
have all the fun, you've mentioned it,
you're bridging the cloud and on-prem,
they talk about when you talk to customers
and what they are seeing is the business impact,
what's the real drivers for that?
That's a great question
cause at the end of the day, I think the recent survey
that was that cost and performance are still the number one
requirement for this,
just real close second is agility,
the speed at which they want to move
and so those two are the top of mind every time.
But the thing we find Ezmeral, which is so impactful
is that nobody brings together the Silicon, the hardware,
the platform, and all of that stack together work
and combine like Ezmeral does with the platforms
that we have and specifically, we start getting 90, 92,
93% utilization out of AI ML workloads
on very expensive hardware,
it really, really is a competitive advantage
over a public cloud offering,
which does not offer those kinds of services
and the cost models are so significantly different.
So, we do that by collapsing the stack,
we take out as much intellectual property,
excuse me,
as much software pieces that are necessary
so we are closest to the Silicon,
closest to the applications,
bring it to the hardware itself,
meaning that we can interleave the applications,
meaning that you can get to true multitenancy
on a particular platform that allows you
to deliver a cost optimized solution.
So, when you talk about the money side, absolutely,
there's just nothing out there
and then on the second side, which is agility.
One of the things that we know is today
is that applications need to be built in pipelines, right,
this is something that's been established now
for quite some time.
Now, that's really making its way on premises
and what Kumar was talking about with, how do we modernize?
How do we do that?
Well, there's going to be some that you want to break
into microservices containers,
and there's some that you don't.
Now, the ones that they're going to do that
they're going to get that speed and motion, et cetera,
out of the gate and they can put that on premises,
which is relatively new these days to the on-premises world.
So, we think both won't be the advantage.
Okay. I want to unpack that a little bit.
So, the cost is clearly really 90 plus percent utilization.
Yes.
I mean, Kumar, you know, even pre virtualization,
we know that it was like, even with virtualization,
you never really got that high.
I mean, people would talk about it,
but are you really able to sustain that
in real world workloads?
Yeah. I think when you make your exchangeable cut up
into smaller pieces, you can insert them into many areas.
We have one customer was running 18 containers
on a single server and each of those containers,
as you know, early days of new data,
you actually modernize what we consider week run containers
or microbiome.
So, if you actually build these microservices,
and you all
and you have versioning all correctly,
you can pack these things extremely well.
And we have seen this, again, it's not a guarantee,
it all depends on your application
and your, I mean, as an engineer,
we want to always understand all of these caveats work,
but it is a very modern utilization of the platform
with the data and once you know where the data is,
and then it becomes very easy to match those two.
Now, the other piece of the value proposition
that I heard Robert is it's basically an integrated stack.
So I don't have to cobble together
a bunch of open source components,
there's legal implications,
there's obviously performance implications.
I would imagine that resonates
and particularly with the enterprise buyer
because they don't have the time to do all this integration.
That's a very good point.
So there is an interesting question that enterprises,
they want to have an open source so there is no lock-in,
but they also need help to implement and deploy
and manage it because they don't have the expertise.
And we all know that the IKEA desk
has actually brought that API,
the past layer standardization.
So what we have done is we have given the open source
and you arrive to the Kubernetes API,
but at the same time orchestration, persistent stories,
the data fabric, the AI algorithms,
all of them are bolted into it and on the top of that,
it's available both as a licensed software on-prem,
and the same software runs on the GreenLake.
So you can actually pay as you go and then we run it
for them in a colo or, or in their own data center.
Oh, good.
That was one of my latter questions.
So, I can get this as a service pay by the drink,
essentially I don't have to install a bunch of stuff
on-prem and pay it
perpetualized...
There is a lot of containers and is the reason
and the lapse of service
in the last discover and knowledge gone production.
So both Ezmeral is available,
you can run it on-prem, on the cloud as well,
a congenital platform, or you can run instead on GreenLake.
Robert, are there any specific use case patterns
that you see emerging amongst customers?
Yeah, absolutely.
So there's a couple of them.
So we have a, a really nice relationship
that we see with any of the Splunk operators
that were out there today, right?
So Splunk containerized, their operator,
that operator is the number one operator,
for example, for Splunk in the IT operation side
or notifications as well as on the security operations side.
So we've found that that runs highly effective
on top of Ezmeral, on top of our platforms
so we just talked about, that Kumar just talked about,
but I want to also give a little bit of backgrounds
to that same operator platform.
The way that the Ezmeral platform has done
is that we've been able to make it highly active, active
with HA availability at nine, it's going to be at five nines
for that same Splunk operator on premises,
on the Kubernetes open source,
which is as far as I'm concerned,
a very, very high end computer science work.
You understand how difficult that is, that's number one.
Number two is you'll see just a spark workloads as a whole.
All right.
Nobody handles spark workloads like we do.
So we put a container around them
and we put them inside the pipeline of moving people
through that basic, ML AI pipeline
of getting a model through its system, through its trained,
and then actually deployed to our ML ops pipeline.
This is a key fundamental for delivering value
in the data space as well.
And then lastly, this is, this is really important
when you think about the data fabric that we offer,
the data fabric itself doesn't necessarily have to be bolted
with the container platform,
the container, the actual data fabric itself,
can be deployed underneath a number of our, you know,
for competitive platforms who don't handle data well.
We know that,
we know that they don't handle it very well at all.
And we get lots and lots of calls for people saying,
"Hey, can you take your Ezmeral data fabric
"and solve my large scale,
"highly challenging data problems?"
And we say, "yeah,
"and then when you're ready for a real world,
"full time enterprise ready container platform,
"we'd be happy to prove that too."
So you're saying you're, if I'm inferring correctly,
you're one of the values as you're simplifying
that whole data pipeline and the whole data science,
science project pun intended, I guess.
(Robert and Kumar laughing)
That's true.
Absolutely.
So, where does a customer start?
I mean, what, what are the engagements like?
What's the starting point?
It's means we're probably one of the most trusted
and robust supplier for many, many years
and we have a phenomenal workforce
of both the (indistinct),
world leading support organization,
there are many places to start with.
One is obviously all these salaries
that are available on the GreenLake,
as we just talked about,
and they can start on a pay as you go basis.
There are many customers
that actually some of them are from the early days
of BlueData and MAPR,
and then already running and they actually improvise
on when, as they move into their next version
more of a message.
You can start with simple as well as container platform
or system with the store, a computer's operation
and can implement as an analyst
to start working.
And then finally as a big company like HPE
as an everybody's company, that finance it's services,
it's very easy for the customers to be able to get
that support on day to day operations.
Thank you for watching everybody.
It's Dave Vellante for theCUBE.
Keep it right there for more great content from Ezmeral.