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>> Hey everyone, welcome back to theCUBE.
The leader in live tech coverage.
We're coming to you live from Las Vegas, the Venetian Expo.
It's HPE Discover 2023.
Lisa Martin here with Dave Vellante.
Our first day of coverage of three days.
We're going to be having a ton of conversations
as we always do.
theCUBE's canon of content, Dave, just continues.
>> Always comes back to the data, right?
>> It always does come back to the data.
We're going to be having a great conversation
with Brandon Whitelaw who joins us,
VP of Strategic Partnerships at Qumulo.
Brandon, great to have you on the show.
Talk to us about what's going on at Qumulo.
Industry, leadership, give us all that good stuff.
>> Yeah, thanks so much.
It's great to be here.
As I mentioned before, long time listener,
first time caller.
And it's a great show.
It's my first time at Discover.
I've been at other competitive spots exactly here before.
And it's a fantastic, put together show.
Really insightful.
We're loving it.
Great traction with customers coming to the booth
and being able to talk about what we have
related to being able to scale anywhere
and help with the data challenges that customers are facing.
Qumulo now is five years in a row
in Gartner's magic quadrant, in the leader quadrant.
Most innovative in that space.
And it really comes down to predicated
on the ability to help customers
in a purely actual software-defined way
take advantage of hardware innovation
as rapidly as great companies like HPE can put it out.
So we go after their challenges
related to putting their data wherever they need it.
Whether it be, as we heard today,
on the edge, in the data center, or in the cloud,
or where I really think the future is,
which is in a hybrid topology.
>> And that's one of the things
that Qumulo has done early on.
You guys were sort of leaned into that cross-cloud.
It was an early example of what we called super-cloud.
We actually pointed to Qumulo as a consistent experience,
whether you're on-prem or across clouds.
But what about the challenges of unstructured data?
I thought they were all solved by 2023.
What are the issues that customers are having?
Is it just the amount of data just keeps growing,
and the old three Vs, or five Vs, or whatever it was?
What's your take?
>> Volume, velocity, yeah.
I mean, I think the interesting thing about it
is that it continues to evolve more and more
into primary, high-value workloads.
Once upon a time, unstructured data
was kind of your back office IT,
home directories, and file shares.
And for the most part, we see that transitioning
towards better programs to be able to collaborate,
like Office 365, OneDrive, Google Drive, et cetera.
Where we see most coming out now
is customers really struggling
with trying to figure out how to modernize these workloads
to be able to buy the base and rent the peak,
so to speak, of private and public cloud,
and be able to have the same simple,
seamless, scalable experience for that data
anywhere they want to put that data,
where it's demanded of them.
Whether or not they have artificial intelligence,
data scientists wanting to use a particular algorithm
that comes out, Google one day, Amazon the next day,
and they have to have their data there and ready for it,
or they have to have it on-prem
where they know that they can have
a consistent, controlled experience
and leverage all the goodness that GreenLake has to offer.
So we really help customers really be able
to do any of that without compromise.
In fact, I saw it from both sides.
I saw from over a decade of having best-in-class,
on-prem, scale-out technology,
and then in the cloud for three years at AWS,
seeing customers trying to move it there
from the other direction,
and the reality is that it's having to be cloud-smart.
Where does it make the most sense?
And that can change from month to month,
depending on what's going on
in the department you're talking to.
And so to be able to do that without compromise,
not like a toy implementation in the cloud,
but the full, actual experience without compromise
that's effective from a cost, performance,
and scale really helps.
>> And where are these customer conversations these days?
Is this at the sea level in terms of managing
these growing volumes of unstructured data,
being able to have access to the right data,
but from a cost perspective
and from a sustainability perspective,
where are you talking with customers these days?
>> Well, so they're really getting it on both sides.
So core IT's getting squeezed, as always,
to do more with less.
So they want simplicity, they want efficiency,
they want the ability to consolidate more workloads,
to have fewer things to manage.
And on the other side, that same organization
is trying to funnel as much money as possible
into agile, flexible, next-gen workloads
and have those deployed, again,
wherever it makes most sense at that time.
And 94% of companies of enterprise accounts are in cloud
and some 80 some odd percent are multi-cloud.
And the complexities of having
a different experience everywhere
and a different way to manage and store that data
is really, really challenging and that adds to expense.
And so ultimately, being able to do that
in a more modern approach that reduces cost quite a bit
and bridge the gap between that strategic CIO level
and the storage team who still needs to govern,
secure, and control it,
and provide that real-time file system analytics
on any side of that equation helps across the board.
>> You talk about buy the base, rent the peak.
So I like that.
It sounds very cloud-like.
Or even more so, GreenLake-like.
So do you approach that with partners
or do you also, are you tempted to,
or do you basically do it through
a Qumulo interface with clients?
>> Yeah, so we're 100% partner-driven in how we go to market.
And what we find is that there's actually
two different types of engagements
based on where the customer's at in their journey
of being able to leverage cloud appropriately.
So usually we see the trusted, guided IT professionals
that help resell traditional hardware
that are fantastic partners with HPE,
helping with the majority of that.
And then we also see cloud SI consultants coming in
to try to help figure out the best way
to optimize cloud where necessary.
And because of how we're licensed, this is pure software.
We don't have any proprietary hardware.
It doesn't make it, we can take advantage
of the newest, greatest thing that HPE comes out with
tomorrow, or public clouds.
And it makes it so that you really have
a tremendous amount of agility and flexibility
to help with that.
And then the partners, of course, can help guide it.
Because especially when doing a hybrid topology,
it's almost, as sad as it is to say to be a storage guy,
it's almost never about actually just the storage.
On-prem, it's kind of like Indiana Jones.
Like how do I swap out one thing for the next thing
and don't change anything out?
But with cloud, especially hybrid cloud,
it reminds me of like the Martian.
Like how do you go about figuring out
how to science this thing to be able to make it
so you have it in the right place at the right time
and you find the right topology?
And you need experienced cloud providers
to understand the application side of things.
So we leverage both.
>> I love that you just dropped two awesome movies,
"The Martian" and "Indiana Jones."
The spirit of movies and Easter eggs.
You mentioned one a minute ago,
I mentioned it in the teaser for your segment.
We know scale out, we talk about it all the time.
Qumulo's scale anywhere.
That's the new it girl.
What does that mean?
And why does it matter?
>> We see it as really an evolution.
You guys have seen the ebbs and flows of IT, right?
Of centralization, decentralization,
and all the things that have happened.
I was part of the journey of the beginning of scale out.
So back in the day and going from a scale up filer
to actual scale out.
And that's really the core foundation
that started with Qumulo.
But what we found as an opportunity
is to be able to take that same experience
and just go a level further.
Which is, if I just zoom out a bit from the data center,
and now I'm looking at edge and cloud and multi-cloud,
it's really being able to scale that same experience
and capability in any of those domains.
>> So it's like a true, am I correct,
true global namespace but not confined
within a data center, it's actually.
>> Yeah, so the same experience, the same performance,
the same flexibility and agility
in any of the three major hyperscalers that you get on-prem.
Again, not kind of a toy implementation.
Joking, I kind of sometimes relegate it to like,
I don't know, food that you get at a movie theater.
Generically, you don't go out of your way
to go to the movie theater to eat the nachos there.
>> That is true.
>> But if you're there, you eat it
because you're not allowed to bring other things in.
And that's a lot of the file options in cloud today.
Even from established on-premises vendors.
They're very limited in scale, performance and cost.
I kind of jokingly say if you were to take
any of those solutions, wrap it in a box
and try to sell it on-prem,
you kind of get laughed out the door.
But in our circumstance, it's not.
It's actually just the same.
In fact, in some circumstances,
faster performance in cloud than on-prem.
And with what HPE has done from a flexibility
and agility and cost modeling perspective,
they're really combining the best of both worlds.
And from a technology perspective,
we're a perfect fit there.
>> Why do you think that is?
Is it just, is it the maturity of the stack?
I mean, the storage is hard, right?
You know, it takes years, maybe even sometimes a decade
to mature your stack, we know this.
Is it, but cloud's been around for a while, you know?
And so why do you think it is that their stacks
are somewhat deficient or actually largely deficient
to, you know, not only Qumulo, but pick your storage company
that's been around for a long time.
They've got better product, more functional, more features.
Like you said, they get kicked out of the enterprise
if they actually tried to offer the movie theater food.
>> Yeah, I hope that takes off as an analogy.
So I think what it comes down to is the Tale of Two Cities.
So you have kind of legacy vendors
who started with their software stack 20, 30 years ago
that really are in a situation a lot of customers in,
where they look at it and they say,
the best I can do is lift
and shift the existing thing I have
and change as little as possible to put it there.
And because I can't control the underlying hardware stack,
which is actually where I get most of my money anyways,
I'm going to just try to get kind of the same experience,
but, you know, try to fit that square peg
in a round hole as much as possible.
And it's really limited because of that.
On the other end of the scale with the hyperscalers,
they predominantly invested in block and objects.
Now, admittedly, misguidingly thinking
that all the file will just transition
one of those two ways.
Not realizing that with the advent of like AI and ML,
which is predominantly done on file
and if you look at bioformatics and media entertainment,
this is all file and it's all been growing
the whole time cloud's been growing.
In fact, it's the fastest growing segment
of those three while cloud has been growing.
>> Dave: And the largest.
>> Yeah, and so where we see this huge opportunity
is 95% of the unstructured data on-premises is file,
but less than 5% in the cloud is.
And so file's really the final frontier.
It is the anchor tenant, as customers have told me,
the anchor tenant on-prem that is keeping them
from being able to move workloads
because there's just such a gap there.
Now recently, in the last couple years,
the hyperscalers started to really focus on this
through partnerships.
For example, we have the Azure native QLO offering,
which is Azure's only native integrated
portal launch storage opportunity.
And on top of it, we have more coming
related to AWS and Google,
but the ability really presents itself
where block and object being mostly application connected
without humans touching it,
seemed to just be easier, I think, along the way.
And file is complex.
The file system owns a lot of things
that normally a block and object system don't.
And that's why you have so many variants.
Unfortunately, there's not one size fits all there.
And customers have preference and use cases change.
>> No compression algorithm for experience,
as one famous cloud CEO once said.
What about AI?
I mean, given the thrust,
and AI was invented last year, I'm joking, late last year.
But given all the focus on AI
and the marriage of AI and file, as you just pointed out,
how do you guys, Qumulo, HPE, how do you think about that,
and where do you see it going?
>> Well, a couple things.
One, fortunately, although I was not here
to take credit whatsoever for it,
Qumulo was very early in that game
where actually just being a consumer of AI,
so a deep machine learning algorithm
to help optimize how it caches data
between spinning disk and SSD,
just tremendously high cache hits.
That actually is very well translated
into how we operate in the cloud as well.
So 10 years plus of maturing that model
and using it to make the storage better.
So it's actually a really early example
of kind of eating your own dog food in that way.
A 4K heat map of the entire file system's very hard to do
unless you plan to do it from day one.
So that was there.
I think in addition to that,
what we see is a lot of customers now
in this arms race to figure out how to implement
and integrate this into their business
and get access to the right appropriate tools for it.
And a lot of times what it comes down to
is this hybrid topology.
So how do I go test and be agile
and try new things very quickly in the cloud?
And then if it gets production and gets going really big,
sometimes it makes sense to actually pull that back down
on-prem into a green light cloud
where you can control the cost characteristics,
the performance characteristics,
the maybe data sovereignty and locality characteristics.
And so again, having that hybrid topology in the reverse,
I had a customer tell me their incubation chamber
is always cloud.
But the moment the workload goes production,
they bring it on-prem
because they know that it comes down to variability.
If variability's very high, cloud works great.
If variability's low and consistent,
you can almost always save more money back on-prem.
>> I think a lot of people confuse that for repatriation.
It's really just smart, cloud smart, I guess,
is what that is, and there's a lot of that going on.
So do you actually specifically optimize for GPUs?
Have you been doing that for a while?
>> Yeah, I mean, the nice thing is,
as kind of an accidental architectural benefit,
so to speak, before,
this was a massive workload for most customers,
most traditional file systems struggle
with the simultaneous read threads needed
by all these GPU cores.
The core count, even in a 1U HPE box out here,
can be 25, 30,000 cores of CUDA cores looking for threads.
And traditional file systems have a limitation on that.
They hold a memory handle on every single one.
We don't.
And so it allows us to scale
to the absolute tip performance of the whole system
without having that kind of artificial construct
in place there that many other systems
do have a challenge with.
So that's one thing.
The second thing, actually,
where we see a huge amount of opportunity
in Azure and AWS as well, is that, surprisingly to me,
while 99% of AI machine learning on-premise
through a file system, a high-performance NVMe file system,
in cloud, it's almost none of it.
In fact, almost all of it is built,
again, with the tools they had at the time,
with staging data and object,
and then holding it, sorry, an object,
and then staging it into the local NVMe disk.
Which, by the way,
they're paying per second for that GPU server.
It's the most expensive byte of data
you can buy in the cloud
is that local disk in that GPU server,
and they're waiting sometimes hours
to load data into it to run the query.
So having a shared high-performance file system
that can scale the whole data set across multiple GPUs
actually accelerates time to learning,
or reduces time to learn, so we can move on to inference
and other stages of that workload.
Really helps out quite a bit.
>> And I can get that in the cloud.
If I want to access the Qumulo stack, I can do that, right?
Whether it's cloud or on-prem. >> Yeah. Absolutely.
Same exact experience, native sync between the two.
Like I said, you can train in cloud
if it made sense for that period of time,
and then bring it back on-prem for inference,
or use the most recent models.
Anywhere that data needs to be
to get the access to those tools.
>> So the premise that HPE's putting forth, there's a couple,
but basically they're LLMs,
that they announced LLMs in GreenLake,
is essentially people are going to want to train privately.
It's going to be more efficiently done
with HPE supercomputers, right?
Presumably you want a piece of that action.
I know, what are your thoughts on that?
>> Yeah, look, I think it makes sense
because surprisingly the number one challenge
with these models in hyperscalers today
is access to the GPUs.
There is such a massive challenge
in just getting time on these things,
and the cost is through the roof,
as you can imagine, because they can, because of the demand.
And so I think with supply chains now
getting back to where they should be,
and things along these lines,
people are realizing that being able to
kind of experiment and try new models
and community access in the cloud,
but when it really comes time to put that thing to 100%
and just go and train that model as hard as you can,
some of the hardware innovation that the HPE has available
just makes total, total sense,
and really makes it also to where,
you know, look, I'm sure the first time
people went through some training runs in the cloud,
and then you get the bill at the end,
it was a bit of a, you know.
Yeah, to have a pre-budgeted IT experience,
again, it's a differentiation.
Whether on-prem or in cloud, we're flat rate pricing.
There's no hidden expenses.
You're not charged per read or write like a lot of things.
And so it really aligns value to where it matters most,
which is, how do you just accelerate outcomes?
>> Last question, Brandon.
Take us out with your favorite customer story
that you think really articulates the value
of what Qumulo is delivering,
especially with its partnership with HPE.
And if you can drop an analogy, your game is strong,
I encourage it.
>> You know, one of the ones that's most dear to my heart
is actually working with a children's hospital in Ohio,
where they, unlike regular hospitals,
actually have a much longer retention for medical imaging.
And they also wanted a way to be able to get access
to AI machine learning models to be able to help
with early tumor detection and things like this.
And for HIPAA requirements,
it's got to be their own data set.
And so they really wanted the ability to,
they're not masters of IT, per se.
Their focus is obviously helping kids.
They needed to be able to find cost-effectiveness
in storing the data, at the same time,
use that to train their models
so they can get better at science.
And being able to go between Azure and on-prem
with GreenLake has allowed them to move that data
where it is stage-appropriate when it's needed
to not slow down radiology, not slow down surgery,
but also train models to develop new ways
to find specifically to their demographic,
their kids, that they're trying to help.
And it's just fantastic to see the results already.
And you'll hear a lot more about it,
I think, publicly by the end of this year,
but it's a great partnership.
>> Great partnership.
You just talked about how that really shines the light
on the availability, the agility, the flexibility,
but also on those business outcomes.
And when you look at it in healthcare,
the business outcomes are life and death.
Thank you so much, Brandon,
for joining Dave and me on the program,
talking to us about what's new with Qumulo,
the HPE partnership and the outcomes
that you're helping customers achieve.
Great to have you.
>> Thanks for calling in. >> Thank you so much.
I appreciate it.
>> All right.
We want to thank you for watching this segment
for Brandon Whitelaw and Dave Vellante.
I'm Lisa Martin.
But don't go anywhere,
because our next segment features a CUBE alumni,
Greg Ernst from Intel.
He's going to be joining us, talking about HPE and Intel,
and what a successful AI deployment entails.
You're watching theCUBE, the leader in live tech coverage.
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