Welcome back to the CUBE's coverage of HPE's GreenLake announcements.
My name is Dave Vellante and you're watching the CUBE. I'm here with Holger Mueller who is
an analyst at Constellation Research and Matt Maccaux who is the Global Field CTO
of Ezmeral Software at HPE. We're gonna talk data. Gents, great to see you.
Great to be here.
So Holger, what do you see happening in the data market?
Obviously data is hot. You know digital-- I call it the "forced march" to digital.
Everybody realizes, "Wow, digital business, that's a data business.
I got to get our data act together." What do you see in the market
as the the big trends, the big waves.
We're all young enough or old enough to remember when people were
saying data is the new oil and nothing has changed. Data is the key ingredient
which enterprises--which they have to store, which they have to
enrich, which they have to use for their decision making. It's the foundation of
everything. If you want to go into machine learning and iPart,
it's growing very fast right?
We have the capability now to look at all the data in an enterprise
which you were unable 10 years ago to do that.
So data is the main center to everything.
That's even more valuable than oil, I think, because the oil you can
only use once. Data, you can--it's kind of polyglot. I can go in different
directions, and it's amazing, right?
The beauty of little products, right? They
don't get consumed, right? They're fired up, right? And no carbon footprint--no,
wait, wait, we have to think about carbon footprint, different story. So to get to the data,
you have to spend some engergy.
It's simple, right? I mean it really is,
data is a fundamental, it's got to be at the core and so Matt, what are you
guys announcing today? And how does that play into what Holger just said.
What we're announcing today is that organizations no longer need to make a
difficult choice. Prior to today, organizations were thinking if I'm
going to do advanced machine learning and really exploit my data, I have to
go to the cloud. But all my data is still on premises because of privacy
rules, industry rules. So what we're announcing today through GreenLake
Services is a cloud services way to deliver that same cloud-based
analytical capability, machine learning data engineering, through hybrid
analytics. It's a unified platform to tie together everything from data
engineering to advanced data science. And we're also announcing the world's first
Kubernetes native objects store that is hybrid cloud enabled, which means you
can keep your data connected across clouds in a Data Fabric.
Or Dave, as you say, mesh.
Okay, let's--can we dig into that a little bit. So you're
essentially saying that--you're gonna have data in both places, right?
Public cloud, edge, on-prem. And you're saying HPE's announcing a capability to
connect them, I think you use the term fabric. I'm cool, by the way, with the
term fabric, we'll parse that out another time.
I'd love for you to discuss textiles, fabrics.
For me every fabric breaks down to mesh, if you put it under the microscope.
Oh, wow! It's the same thing!
Now that's really--that's too detailed for my brain right this moment.
But you're saying you can connect all those different states
because data by its very nature is everywhere.
You're gonna unify that and what?
That can manage that through sort of a single view?
That's right. So the management is centralized.
We need to be able to know where our data is being provisioned.
But again, we don't want organizations to feel like they have to
make the trade off. If they want to use cloud service A in Azure, and cloud
service B in GCP. Why not connect them together? Why not allow the data
to remain in sync, or not, through a distributed fabric? Because we use that
term fabric over and over again. But the idea is let the data be where it
most naturally makes sense and exploit it. Monetization is an old tool but
exploit it in a way that works best for your users and applications.
In sync or not. That's interesting. So it's my choice.
That's right because the back
of an automobile could be a teeny tiny small edge location.
It's not always going to be in sync until it connects back up with a training facility.
But we still need to be able to manage that
and maybe that data gets persisted to a
core data center. Maybe it gets pushed to the cloud. But we still need to know
where that data is, where it came from, its lineage, what quality it has, what
security we're going to wrap around that. That all should be part of this fabric.
Okay, so you've got essentially a governance model. At least maybe
you're working toward that. Maybe it's not all baked today, but that's the
North Star. This fabric connects single management view,
governed in a federated fashion.
Right. And it's available through the
most common APIs that these applications are already written in.
So everybody today's talking S3. I got to get all of my data. I need to
put it into an object store, it needs to be S3 compatible.
So we are extending this capability to be S3 native,
but it's optimized for performance.
Today when you put data in an object store, it's kind of one-size-fits-all.
Well, we know for those streaming analytical capabilities,
those high performance workloads, it needs to be tuned for that. So how
about I give you a very small object, on the very fastest disk in your data
center and maybe that cheaper location somewhere else. And so we're giving you
that balance as part of the overall management estate.
Holger, what's your take on this?
I mean Frank Slootman says we'll never-- we're not going halfway house,
we're never gonna do on-prem or only in the cloud.
So that basically says, okay, he's ignoring a pretty large market, by choice.
You're not. That's right.
You must love the those words.
But what do you see as the public cloud players that
moves on-prem, particularly in this realm.
Well we've seen lots of cloud players
who were only cloud coming back towards on-prem,
what we call the next generation compute platform
where I can move data and workloads between on-premise and,
ideally, multiple clouds, right? Because
I don't want to be locked into public cloud vendors.
And we see two trends.
One trend is the traditional hardware supplier of on-premise has not
scaled to cloud technology in terms of big data analytics.
They just missed the boat for that in the past.
This is changing. You guys are a traditional
player and changing this, so congratulations.
The other thing is there's been no innovation for the on-premise tech stack.
The only technology stack to run modern application has been invested for a
long time in the cloud. So what we see since 2-3 years right? With the first
one being Google with Kubernetes, then GKE on-premise, then Anthos.
By bringing that tech stack with compromises to on-premises,
aknowledging exactly what we're talking about,
that data is everywhere. Data is important.
Data gravity is there. That's just the network's fault, or the networks are
too slow, right? If we could just move everything anywhere we want, like
juggling two balls then we would be in a different place. But that's not
enough investment for the traditional IT players for that stack and the
modern stack being there. And now every public cloud player has an on premise
offering with different flavors, different capabilities.
I want to give you guys Dave's story of kind of history
and you can of course correct and tell me how this
maybe fits into what's happening with customers.
You know, before Hadoop, obviously you had to buy a big Oracle database
and you know, running UNIX,
and you buy some big storage subsystem, if you had any money left over.
You know, you maybe do some actual analytics.
But then Hadoop comes in, lowers the cost,
and then S3 kneecaps the entire Hadoop market.
I wouldn't say that, wouldn't agree.
Because the fascinating thing that Hadoop brought to the enterprise for
the first time--you're absolutely right-- affordable to do that. But it's
not only about affordability because S3 has the affordability, the big
thing is you can store information without knowing how to analyze it.
So stuff like schema on-- Before it was like an Oracle
database, it was a star schema for data warehouse and so on.
You had to make decisions how to store that data
because compute capability, storage capabilities, were too limited, right?
That's what Hadoop blew away.
I agree.
No schema, alright. But then that created data lakes which created data swamps
and then that whole mess. And then Spark comes in and helps clean. Okay fine.
So we're cool with that. But the early days of Hadoop you had, companies would
have a Hadoop monolith. They might, probably had their data catalog in
Excel or Google Sheets. Right. And so now my question to you, Matt, is
there's a lot of customers that are still in that world. What do they do?
They got an option to go to the cloud.
I'm hearing that you're giving them another option.
That's right. So, we know that data is going to move to the cloud,
as I mentioned. So let's keep that data in sync and governed
and secured like you expect. But for the data that can't move, let's bring
those cloud-native services to your data center. And so that's a big part
of this announcement is this unified analytics so that you can continue to
run the tools that you want to today while bringing those next generation
tools based on Apache Spark, using libraries like Delta Lake, so you can go
anything from Tableau through Presto SQL to advanced machine learning in
your Jupiter notebooks on premises where you know your data is secure, and
if it happens to sit in existing Hadoop data lake, that's fine too. We don't
want our customers to have to make that trade off as they go from one to the other.
Let's give you the best of both worlds. Or as they say,
you can eat your cake and have it too.
Okay, so now let's talk about developers on-prem, right?
They kind of--if they really wanted to go cloud native, they
had to go to the cloud. Do you feel like this changes the game?
Do on-prem developers want that capability? Will they lean into that
capability or will they say no, no, the cloud is cool. What's your take?
I love developers, right. But it's about who makes the decision,
who pays the developers.
So the CXOs in the enterprises, they need--that's
exactly why we call the next gen computing platform that you can move
your code assets. It's very hard to build software. So it's very valuable
to an enterprise. I don't want to be limited to one single location of
certain computing infrastructure. Right? Luckily we have Kubernetes to be able
to move that but I want to be able to deploy it on-premise if I have to, want
to be able to deploy in the multiple clouds which are available
and that's the key part and that makes developers happy too because the code you
write is going to run multiple places, so you can build more code, better code
instead of building the same thing multiple places because little compiler
changed here, a little compiler changed there.
Nobody wants to do portability testing
and rewriting and recertifying for certain platforms.
So the head of application development, or application architecture,
and the business are ultimately gonna dictate that, number one. Number two, you're saying the
developers shouldn't care because it can run, you write once run anywhere.
That is the promise and that's the interesting thing which is available now,
capable now, thanks to Kubernetes as a container platform and the abstraction,
which containers provide and that makes everybody's life easier but goes much
more higher than the head of apps. Right? This is the digital
transformation strategy, the next generation application
the company has to build as a response to a pandemic, as a pivot, as digital
transformation, as digital disruption capability.
I see a lot of organizations basically modernizing
by building some kind of abstraction to their back-end systems,
modernizing it through cloud native and saying, hey,
as you were saying, Holger,
run it anywhere you want, or connect to those cloud apps, or connect across clouds,
connect to other on-prem apps and eventually out to the edge.
Is that what you see?
It's so much easier said than done, though. Organizations
have struggled so much with this, especially as we start talking about
those data intensive admin workloads.
Kubernetes in Hadoop? Ugh!
Up until now, organizations haven't been able to deploy those services.
So what we're offering as part of these GreenLake unified analytic services,
a Kubernetes runtime.
It's not ours. It's top of branch, open source.
And, open source operators like Apache Spark bringing Delta Lake libraries
so that if your developer does want to use cloud native tools
to build those next generation advanced analytics applications,
but prod is still on premises?
They should just be able to pick that code up and because we are deploying 100% open
source frameworks the code should run as-is.
So it seems like the strategy is to
basically build--now that's what GreenLake is, right?
It's a cloud, it's like "There, here's your options,
use whatever you want."
Well, it's your cloud. That's what's so important about GreenLake
is it's your cloud
in your data center or co-lo with your data, your tools and your code.
And again, we know that organizations are going to go to a multi or hybrid cloud location
and through our management capabilities
we can reach out if you don't want us
to control those, not necessarily, that's okay,
but we should at least be able to monitor and audit
the data that sits in those other locations, the
applications that are running. Maybe I register your GKE cluster,
I don't manage it, but at least through its central pane of glass,
I can tell the head of applications
what that person's utilization is across these environments.
You know, and you said something, Matt, that struck--
resonated with me, which is, this is not trivial.
I mean, not simple to do.
You see a lot of customers or companies, what they're doing--vendors--
they'll wrap their stack in Kubernetes,
shove it in the cloud, it's essentially a hosted stack, right?
And you, you're kind of taking a different approach.
You're saying, hey, we're essentially building a cloud
that's going to connect all these estates.
And the key is, you're gonna have to keep--
and you are, I think that's part of the reason why we're here--
announcing stuff very quickly.
A lot of innovation has to come out to satisfy that demand
that you're essentially talking about.
Because we oversimplified things with containers.
Because containers don't have what matters for data and
what matters for enterprise, which is persistence.
Might have to be able to turn my persistence down,
or I don't know when I'm going to use the data,
but it has to stay there.
And that's not solved in the container world by itself.
And that's what's coming now. The heavy lifting is done by people like HPE to
provide the persistence of the data across the different deployment platforms.
And then there's just the need to modernize my on-premise platform.
I can't run on a server which is two or three years old, right?
It's no longer safe if it doesn't have trusted identity.
All the good stuff that you need these days, right?
It cannot be operated remotely or whatever it happens there.
But two or three years is long enough for a server to have run their course.
You're a software guy. You hate hardware anyway.
Hardware isn't necessarily evil.
You abstract that hardware complexity.
It's like TSA. I want to go somewhere,
but I have to go through TSA.
But that's a key point. Let me buy a service.
If I need compute give it to me and if I don't I don't want to hear about it. Right.
That's kind of the direction that you're headed.
That's right, and--
That's what you're offering.
That's right. And specifically the services.
So, GreenLake has been offering infrastructure,
virtual machines IaaS, as a service. And we want to stop talking about that underlying
capability because it's a dial tone now. What organizations and these
developers want is the service. Give me a service or a function,
like I get in the cloud,
but I need to get going today. I need it within my security parameters.
Access to my data, my tools, so I can get going as quickly as possible.
And then beyond that we're going to give you that cloud billing practices
because just because you're deploying a cloud native service,
if you're still still being deployed via CapEx,
you're not solving a lot of problems.
So we also need to have that cloud billing model.
Holger, we'll give you the last word. Bring us home.
It's very interesting
to have the cloud qualities of subscription based pricing,
maintained by HPE as the cloud vendor
from somewhere else and that gives you that flexibility.
And that's very important because data is essential
to enterprise processes and there's three reasons why data doesn't go to the cloud, right?
We know that.
It's privacy residency requirements,
there's no cloud infrastructure in the country,
it's performance, because network likely plays a role,
especially for critical apps.
And then there's, "not invented here".
Remember Charles Phillips saying
how old the CIOs are now,
I don't know if they're going to go to the cloud or not, right?
So it's not invented here. These are the things which keep data on premise,
you know that load and HPE is coming on with--
It's physics, it's laws, it's politics and sometimes it's cost.
It's just sometimes too expensive to move and migrate. Guys, thanks so much.
Great to see you both.
Dave it's always a pleasure.
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