The information economy as a ride. The world is teeming with innovation as new
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at Inside analysis dot Comside Analysis dot com. And now here's your host,
through Eric Kavanaugh. All Right, ladies and gentlemen, Hello, and welcome
back once again to the only coast to coast radio show in the USA.
AY, and that's all about the information economy. Of course, that is
Inside Analysis, Yours truly, Eric Kavanaugh here, and I am very excited
folks to have a preview of something today. So we're gonna do a little
time capsule conversation with an expert in from HPE. He is the gm n
VP over at HPE Esmeral Software, which is some pretty interesting stuff. So
we're gonna talk about AI, what this stuff is, how it works,
what it does, why you should care about it. Of course, there's
a lot of discussion about AI these days, largely because of these large language
models that have come out. AI has been around for decades. It's nothing
new, but the fact that we have this nexus of compute power algorithms,
business use cases and now all this awareness thanks to chat, GPT etc.
Has really forced this to the forefront and it's a big deal and it's going
to reshape just about every part of the IT landscape in some way, shape
or form. So it's important and frankly imperative that we understand what all that
means. And so with that I have Mohan tell us a bit about yourself
and as Roland what's going on over there? Hey? Thanks, ech,
so excited to be on the show. High folks. So my name's Mohan.
I'm the general manager of HPS ASMO Software Business. Fairly new to this
soil, joined about the years ago. And what my business unit does is
we focus on technologies that make it easy for our customers to develop and deply
data and analytics applications. Analytics changing all the way from BI and reporting type
use cases through to large scale data analytics and all the way to AI and
m l UM. We largely target enterprise customers and our mission is to try
and make it easy for our customers to in some sense develop and deply applications
UH to the best of their abilities of such. So where did ASMORL come
from? And what does it encompass? Yep, so sorry sorry, worse
about that. I'm in a conference woman, like you know, my admin,
I just came in, So sorry by that. Uh yeah, So
the questions come from, yes, what was the best world, what's the
what's the mission? And what I always love to know the kernel of the
technology too, So that's that's a great question, eadic and actually as well
precedes my time in the company. Asmall was formed when our leadership decided to
invest in software as a growth opportunity, as well as truly formed by a
whole bunch of startup acquisitions. We acquired companies like Blue Data and my part
alonger. We also acquired companies such as ampool and Cycle, et cetera.
So in some sense, es I believe it derives from like you know,
the emerald color. But we like to think of ourselves as HP's biggest,
a little secret, right, and our mission is largely focused on how we
can how we can basically use software to develop a growth division for the company.
Yeah, I'm very familiar with map bar so for there our audience,
who doesn't know you had cloud Era come out first with this had due to
the highly distributed or they had to distributed file system which was going to solve
all the world's problems using map reduce. I was a bit skeptical about that
from the early days because map produce is very good at doing certain things like
indexing the web, but it's really not purpose built for other kinds of very
common analytics use cases. And so then Horton Works came out. But the
third to the game with a very interesting approach was map r and you had
RDDS, right, resilient data sets or something is that. Can you remind
me what that was? You know? Yes, so you're actually taking me
back in time, and this is truly a time capture story. I think
if if I take a step back here and think about the evolution of technology,
right, because again, when you think about Horton Works, cloud or
do, we're going back to the twenty eleven twenty thirteen where big data was
the fancy new term of such today, right, And we've come a long
way today. So you know, we've basically taken the mapper like acquisition and
we've transformed it into something that we call the data fabric, which is a
much more forward looking view, right, and and big different siators for us
here really were I think when we acquired Mapper we got not just the best
of breed ib but we're also about the best of the talent in a highly
technical space of souce. Here. What we've done over the last several years
is we've refocused the team to move away from the map reduced paradigm, to
move away from the original HDFS roots, to focus on more of the analytic
work clothes and like the substace that our customers care about. So today,
for example, when you look at our data fabric product, we are on
a journey where we would like to become the analytics data plane which supports data
variety of formats. We are long ways away from the DFS map produced days
to where we support our our primary interfaces today our files, objects, and
we've just introduced Calfeck complan streams. More importantly, we see this as a
continuum where we want our customers to have capabilities such as being able to do
graphs, vector databases, tables, et cetera in the same substate of sotium.
Another big change for us with the portfolio has been the fact that while
in the good old days, if you go back to the Haydu based basilia
about how do I create the best possible charterage system where I can basically spread
out my data. We've taken a more like we've taken a more forward looking
view here today where we believe there are many storage solutions available in the market
today, including HP. HP has some great storage assets that we bring to
our customers. We would like to position our data fabric as a layer on
top of existing storage solutions where we want to truly become the analytics data plane.
Right, So the value proposition to the customer really is the ability to
manage data in a variety of formats across a variety of data sources, including
all the enterprise great cardrails that you may need. So, for example,
think about movement of data, restricting access to data, doing capabilities like geofencing,
and making sure that you certain types of data don't cross that boundaries.
Were able to do all of those things today and netnet. The goal is
to make it simple for our developers. That's primarily the audience that we catered
to with HP asmal software. It is really to make it easy for our
developers to basically not how to varry about where is the data getting produced,
what's the format it's going to be in all of the stuff. But make
it easy for them to say, hey, here's the data I want to
process, and inside are the ameral inside of wantage rate from the data.
Yeah, so this is very very interesting. I've thought about this stuff for
a long time, right, and so I've been I mean I programmed when
I was I think thirteen years old. I would grow a couple of games,
right, and I've been in the data space now for twenty three years,
so tracking is pretty closely. And I knew it was going to get
big. I knew analytics was going to be huge, even I didn't expect
things to get as big and amazing as they are today. But just to
kind of explain to our audience, we've gone on this journey from data being
persisted in a single database that's your production database which you used to run your
business. Then we had data warehousing because we realized you couldn't really query these
production systems and the business wanted to understand things trends, developments, peaks,
valleys, etc. You couldn't really easily ascertain that stuff without a performance hit
on the production system. So that's why we had data warehouses, right,
and then we built all up cubes on top of that. Well, then
this data lake concept comes out, and it really it's actually after had do
right, So had Duke came out first, and then the data lake concept
comes out, and I remember thinking to myself, are we making the same
mistake again of trying to house all this information in one instance? And granted
it's a cluster, or you can have multiple clusters or different things you can
do, but it just I wonder to myself, is this really making sense?
And really what you want to be able to do is have this you
could even call it a semantic layer, but some abstraction layer that sits on
top of all the persistent data and serves as your orchestration environments. And if
you can do things cleverly, then you can use this for analytics. But
the point is you don't anymore have to strip out all the context in order
to save transactional data and then later analyze it. Right. I mean,
if you look at a lot of the generative AI stuff, it's being used
to make up for the fact that we stripped out the context in order to
get it into these relational models. And it was just a bunch of gymnastics
done to get there. And what you're telling me that I think is very
interesting now is that Esmeral, this data fabric can serve that purpose, it
can be well. Frankly, in two thousand and one, I knew what
data warehouse consultant too talked about having an enterprise backplane, is what he was
referring to, and it was this analytics layer. And so it's like,
wow, here we are. Now you've leveraged map bar but other technologies and
of course some homegrown stuff to create this very rich, fertile, dynamic data
fabric that has the guardrails baked in. I mean, that's soup to nuts,
right. So, Edic, I think you're hitting the NA on the
hitting NA. I think I'm a big fan of yours. I think you
really were attacking this space. So I think you're one of both the most
knowledgetable people in the spaces as a visionary. Here, the way I think
about data really is it is a continuing right. And and the reason we
basically indexed on the techno on the terminology of fabric as well is we see
data as being in a continuous states right. The good ol days, we
had wadehouses, we had databases we have now we have lakehouses. When I
think about data fabrics today, I actually think of them as interconnective measures of
persistent data that we want to like, you know, interoperate with one another.
You bring up context. I think context is critical and context means different
things to different folks. For example, today we have customer you use cases
where in the past they had to replicate data depending on the format the data
is going to be processed. In very simple example, here is very large
financial institution, they had to create multiple copies of the data because guess what,
the data science team can only operate on objects and files, but the
audit and the compliance teams that work on exactly the same data sets want a
more sequel like interface, right, and again, depending on the way that
the type of sequel they had to replic the data a different data stores.
Right. Today, with data fabric what we try to provide to our customers
is the single pane of glass where the data can come in a variety of
formats. And one of the most exciting use cases that I've seen so far
at our customers is their streaming telemetry data. Right. So think of logs
using a CALF interface, the same file does not have to be like in
traditional situations you would want to copy the CALF file into an object store and
then process it as a file, et cetera. Today, with the data
fabric, what happens is the data comes in as a calf CU stream but
automatically. Data Scientist celebrated the process at using an object interface. Right.
So where we are headed to here really is as you said, it's really
worth trying to build out that abstraction that beats the customers for their developer needs,
right without having to vary about things like marshaling data, formatting, data
staging, placement, etc. The goal is to try and make it easy
for customers to be able to analyze data as it comes. Right. Another
big thing we think about here is while you focused on persistent data, we
actually think a lot of data in the future is going to be developed at
the edge and that unlocks a whole bunch of interesting use cases. Again,
so if you think about Jenny, and if you think about LMS, etc.
There's a lot of excitement today. Again, I think chat GPT,
I think you said it correctly right. AI has been around for a while
but it's just a matter of time and it's a matter of computer research is
to make all the cool technology that has developed a couple of decades ago real
and meaningful. Right, I think we're going through a renaissance here where I
feel like if you look at some of the new world coming out of academia
as well as some of the larger like you know, AI vendors, there's
a shift from I think. I think chat GPT opened the doors to show
us what is the art of possible and chat GPT strength is it takes a
large volume of non contextual data and is able to bring you reasonably good insects.
Right. What more recent research has started to show and what people are
starting to get excited about in the industry is if you have focused data sets
where you have richer quality data, you can build more precise models with smaller
data sets. Right. And from an HPE point of view, we think
this is a huge opportunity. Right. Our being pieces is around making hybrid
real. We are strong believers that I personally, I am a strong believer
that, like you know, when you look at like technologies and at large
language models or the nexternation use cases of AI m L. I think a
lot of the substrate is going to be both. It's going to be outside
the data center, but it's also going to be outside public cloud. It's
going to be at aspects like the edge. And what we're trying to do
with HP Esmeral is we're trying to provide foundations where our customers can build out
these externation of applications. So why we've talked a lot about data fabric,
we have a complementary product that is going to be announced and launched on the
sixteenth of May called Unified Analytics. The idea here is to basically bring to
our customers the best breed open source tools right however, provide them with enterprise
breed guard rates. So think about lifecycle management, single science on security,
patching, etc. HP takes care of all of this. What differentiates are
Unified Analytics offering from what we see in the market today is it's really two
things. One is HP is committed to focusing on true open source, where
we don't create HP folks of popular open source technologies. The second is,
I think a big mission for US is open interfaces and no vendor locking.
So likely basically thinking of data fabric as a dynamic continuum for the datablane.
We think of unified analytics as well as being this curated catalog of the best
of WEAT solutions that help our customers build out the next national solutions. So
all the way from doing LMS to building out like you know, needle in
the haystack, Sparks solutions, our customers are doing all of these things on
utified analytics today. So let me pause here. I've kind of gone around
this. Yeah, this is some good stuff, so if I understand it
correctly, and let's just talk for a second about Kubernetes and container orchestration.
You know, I explain to people like when I went to cubecon you look
around and there are now fifty vendors doing observability. Well, where did they
all come from? I mean they came because Google created this thing called Kubernetes,
which sort of has become the de facto standard for container orchestration, which
is really the new mechanism for holding out enterprise software. Right, I mean
everyone kind of sees that's the direction thing going. But containers, the one
thing they don't contain is data, so they have to connect to some persistent
storage. Is the vision here that this stata fabric then becomes the persistent storage
that something like cuberneties would use. It's it's very workload specific, right,
So you know if you look at the ESMA portfolo. We have also acquired
a company called Blue Data, which was providing a state of the art container
run time. Right. Since then we've basically standardized on cuberities. So while
data Fabric has a native CSI layer and et cetera, we also see this
as a continuer because depending on workload, right, depending on the type of
workload you of containerizing, you have different data requirements. For example, if
you're continerizing a database, right, you want something like a block service like
you need the performance you need in some sense a persistent instance right that you
can basically connect. You need high performance like storage. Right. However,
if you're looking at more of an analytic solution, right, you don't need
the same amount of throughput and eyeops, sots, et cetera here, right,
So odd approach. So this is really we are attacking. So again,
when you think about unified analytics of data Fabric, we do use colubility
separate computer storage HOB. We've taken a more liberal stance here and saying it
really depends on the worklop and supports a bunch of interfaces natively for containers,
but we also play well with other story solutions that may have been works.
Right. So basically, sometimes yes and sometimes no, it just depends upon
the use case and the latency is required and the business case, etc.
But but nonetheless, and let let's kind of dig into this unified analytics too,
because again we created all these problems when we try to solve a problem,
right, I mean that was kind of the challenge here, is that
we keep creating new problems to solve the old problems that we never really solved.
And so the analytics world, you know, again like with data warehouses,
for example, it was mostly transactional data from arke systems for example.
Maybe you got crazy and had some click stream analysis in there. But now
these data lakes are focused on other kinds of data and you look at like
again, just to get back to observability, they're like fifty different vendors who
are ginning up creating all of this observability through different kinds of data inside the
information infrastructure. Basically, right, it's done for performance, it's done for
troubleshooting, it's done for application development, of design, all these different reasons
why you do that stuff. Security obviously, but nonetheless, having a cohesive
view of information assets that's really powerful for being able to generate meaningful analytics that
can help the business. And I'm guessing that's really what's going on here with
this unified analytics approach, is that? Right? Absolutely? Absolutely X.
If you think about HP as most software customers, we focus on the Fortune
fifty to the function in two thousand, which largely enterprise teams with sophisticated developers.
Right. What you heard from my developer community is they love open source.
However, bin open source is great for prototyping, there are a lot
of challenges to take a prototype into production, right, So what we're trying
to do with unified Analytics is to try and bring the best of reopen source
innovation to the enterprise force frame. So what we tried to do is we
try to provide a curated set of frameworks around across three or four specific enterprise
workloads. So think about data engineering, so think about airflow, pressedto type
capabilities, think about bi reporting, so we include super set, think about
lot scale analytics. So we have Spark, we have Livy and stuff,
and then we have like you know, the AIMI, which is the more
cutting a kind of capability. So this is where we have a lot of
exciting stuff like we have que flow, we have mlflow ray Feast, etc.
What we try to do from an HP perspective is we try to take
these open source frameworks, make absolutely no changes to the framework, but create
enterprise gate guard rails so an enterprise customer can build a solution without having to
worry about managing versions of Spark called which version of que flows? Right?
Right? That's this is very very cool stuff. And I love the focus
on aggregating and hardening open source technology because you're right, there's a lot of
stuff out there for folks. Don't touch that down, We'll be right back.
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four eight five forty eight. Welcome back to Inside Analysis. Here's your host,
Eric Kavanaugh. All right, folks, time for the only coast to
coast radio show all about the information economy, Inside Analysis. Yours truly,
Eric Kavanaugh here dialing into the SAP Insider event in Vegas. I was hoping
to be there today. I might be there tomorrow if I could make the
Red Sea part and get over there, which might happen. But it's always
a fun event. It's always very dynamic, a lot of fantastic classes education
going on, and of course an exhibit hall with a lot of cool vendors,
and one of my favorite vendors, quite frankly, I'm called pure Storage
is there and we have Ryan Arsenal with us to talk about the kinds of
things they do. And I'll just say real quick, pure Storage is a
storage company. They sell their hardware vendor, they sell flashdores, they sell
all kinds of different things. I'm sure we'll hear about that, but what
I've always been impressed by is they take a very consultative approach. You Typically
with a hardware vendor, you just get the hardware and you're off to the
races and they just want to move products. And that is not the case
with pure storage. These folks really think through solutions and they really provide bridges
to the next phase of enterprises information strategy or information architecture. At least that's
how I look at it. Because things are always changing and companies they have
lots of legacy systems and legacy hardware and applications, etc. It can be
very difficult to kind of break out of those environments to do quote unquote digital
transformation. But pure storage really allows you to kind of expedite that and optimize
what you do. So I'm always excited to talk to these folks. So
Ryan, there you are at the conference. Tell us what's going on out
there. What are some of the cooler things that you're working on these days
to help companies transform? Yeah? Thanks, Eric, I appreciate the introductions.
So, yeah, we're out here in Vegas. We love this SAP
Insider show. Right. So my job up here storage on a field solution
architect. I specialize in SAP accounts here in the AMA because UM and really
My job is really to evangelize storage is not a commodity anymore. Right,
storage is not just a bunch of bytes that you store in the side of
a data center and as long as it's up and running, the lights are
blinking green and you have storage. That's what That's how people think of storage.
Right. And I was so. I was an SAP customer for twelve
years. Myself implemented Hana, and I was the same way. Right,
storage as long as I had it and it was up and running, and
somebody wanted to write ab up at two in the morning, that it was
there, they could do it. I didn't realize the benefits of a storage
right and pure storage we do, we do come at it differently. Right.
We are a software company, I'd say, more than we are a
hardware company. It's really the software around our hardware array that that makes it
so beneficial to the customers. SAP customers, especially because they're getting more and
more out of their storage. Right, they're using these data services on top
of their Haunta database to do things like replication and data mobility and snapshots and
back up and data protection, ransomware, of protection. I can go on
and on about the things that our customers are doing because our storage enables us.
Yeah, that's very cool stuff. And maybe let's get into some examples
of how that works. Because what gets me excited thinking about pure storage and
the kind of solutions you build is that you have this keen understanding of when
a company needs hot data, medium, warm data, cold data. There
are different use cases and of course different price points and different speeds too.
Right, sometimes you need really fast data, sometimes you don't, And you
really want to think through these things, especially these days when let's face it,
you know, I haven't talked to you guys in probably a year,
and in that year, it's something called observability just exploded all over the planet,
all over this industry. I mean, when you go to Quca and
there are like forty five vendors doing observability, you know, like, where
do these vendors come from. They're all over the place, and it's because
they're storing different kinds of data, different kinds of machine data, doing observability
of when pipelines go down, when data is not right coming in somewhere.
And so you know, with this new Kuberneties enabled world, it's incredibly more
complex and Obviously, the business just wants simplicity, right, So that's something
that you guys are working on. Yeah, absolutely, Yeah, So we
have we have a number of partnerships both inside the container and outside the container
world. Right. We obviously started partnering with VMware first. Right, we
have some of the best integrations with VMware that I've seen. I pushed VMware
with all my customers making sure that they're getting the most out of their hardware.
Right. The VMware makes it a lot easier. But then we come
with their hardware. And then you mentioned you mentioned data tearing a little bit,
right, So the data teering solution that we have up here, we
are really fast, right, even our lower end storage arrays. We built
off flash from the ground up. We took a bet on flash ten years
ago when people were laughing at us saying flash is never going to replace disc,
and guess what we are replacing disc now in the data center. And
it's our goal to make every spinning piece of hardware disappear from every every data
center in the world. When it comes to SAP and Hanna, though,
um Hana is a great platform, but it can be a very expensive platform.
Right, you're looting loading every bite data up into memory and memories not
cheap. HONA licensing isn't cheap. So one of the things I've been working
at a lot of my customers are on is that data tearing solution. Right.
Why are you storing your entire database in memory, even data that you
may not ever access or maybe maybe you access to once a year, or
maybe it's more technical data that your basis team access is infrequently. Right,
there's no reason to store that in memory. And NSTEAM, which is native
storage extension, it's a HANA piece of functionality, a great way to be
able to tear your data and tell Hanna, you know what, I only
need the most critical, most access data in my memory layer. That's where
I need to speed. And why not use a really fast storage layer to
store your warm data, don't load it into memory. And hannas smart enough
to go get that data when it needs to. And we've had a lot
of success with us because of our speed. Customers get more aggressive on how
much data they're tearing out, saving on growth, saving on infrastructure, memory
costs, and even sometimes saving on licensing because a lot of times SAP well
license based on how big your database is. And production. Well, and
see that's such an excellent point and why the consultative approach really helps because you
folks understand that and you know where the pitfalls are, you know where the
inflection points occur. And that's the kind of thing absent some good consulting,
a company will step into not fully appreciating, and then six months, nine
months down the road, they're going, oh, geez, look at these
bills. Now you're looking at some data migration project, which is going to
be a separate issue. So it really pays to kind of understand which trajectory
is going to have what results, right, Sure, Sure, and I
like that you brought up the migration, right. We wanted to disrupt storage.
We were sick of vendors going to their customers after three years of selling
them storage array and say, hey, the storage is three years old.
We're going to jack your maintenance, you know, by thirty percent, and
then two years later saying, hey, now your storage it's five years old.
You need to buy another one. And then you pick it up,
you move it out of the data center, you bring this new one in,
you do that giant data migration project, which it's it's not easy,
right, that's a that's a hard thing to do when, especially when there's
upwards a patabytes of data that you need to move. We change the game,
like we we can non disruptively upgrade our storage in place while running at
one hundred percent performance. You can upgrade to the latest and greatest hardware without
ever taking a downtime. We don't, we don't. We don't make you
throw away that old array you do when you buy Pure sport storage, when
you do your data migration onto Pure, the last data migration you ever have
to do. Wow, that's a big that's a pretty big promise. That's
some cool stuff. And you know what I had mentioned before, which again
gets me very excited about your approach, is that a solution from pure storage
can be like a stepping stone into a whole new dynamic or a whole new
paradigm for how you do your business. So you think about going from on
prem to the cloud. I always joke we went from hybrid cloud to multi
cloud in like a week or two weeks because people realize, all right,
it's not just going to be on prem in this one cloud. It's going
to be this array of cloud solutions, whether it's Amazon Google, Microsoft,
Rax based oracles getting serious about this stuff now too, and and so that's
a big deal. And you folks can kind of provide that stepping stone to
get to the ultimate destination here, but to do so incrementally and you know,
be able to preserve your systems and your workflows. And that's all very
important stuff, right, I mean, the operations people don't never want to
change anything unless they really really have to. And I think market dynamics are
such now that you got to change some stuff. And so this is a
good outlet. I mean, at least talk to you folks to work through
the consultative process to figure out ways right right right. And Eric, the
twelve year customer of SAPs, I ran a platform. I ran the basis
team, the performance team, the integration team. I know what it's like
to run an SAP application. Not easy. If I had pure storage when
I was running it, I probably would have shaved a few years off my
life, saved stay shaving a few years off my life? Right, Um,
So I get it, right, I get what these customers are going
through, and I'm trying to evangelize that yes, your storage can do more
for you. And you nailed it, right, we can. We can
be that stepping stone, you know, get your SAP on Pure Storage and
then we'll grow with you. Right. SAP has got a very aggressive roadmap
to get into Kuberneties and do more um container based approaches. We have data
service solutions. We acquired a company called port Works about two years ago.
They are the lead data service provider for cut these platforms, both in the
data center and in the cloud. Right, so we have that solution there.
Um. All these data services I talked about, they come with the
purchase of an array. Right. We don't nickel and dime for different things.
We don't charge it for snapshots or replication. You get everything when you
buy Pure Storage now and in the future. Right as we release cool new
innovations, which is what we do. We innovate. Right as we release
new cool new things. You just upgrade your OS. Long as you're in
a maintenance content contract, upgrade your OS and you get access to those and
this software that we built to manage the on premise array, you can pick
it up and drop it into AWS and Azure and get some of those same
enterprise data services in a cloud based model, no kidding. So it's got
a cloud native architecture is what you're basically saying. Absolutely, it makes it
easier to move those workloads because of because you're running OROS on top of your
data, both on premise and in the cloud, you can move things back
and forth seamlessly, and we duplicate everything, so you're no longer paying expensive
egress charges. And yeah, very easy to get out of the cloud if
you're running on pure storage as well. That is that is such a huge
each point. I'm remembering this now from the last time I took a briefing.
So you have your own operating system for data is kind of how it
works, right, And so if a client uses your technology for their storage
arrays and they've gotten on Prime, they've got Cloud. Because you can do
the management in your OS, you can avoid these egress costs and thus give
a lot more flexibility and I'm guessing too, visibility into the performance of the
data depending upon where it is. Is that right, absolutely, we have
we have one single glass pane of glass to be able to see both your
on TREM storage and your cloud storage and be able to manage that. Yeah,
that's that's very interesting too. And then of course across this multi cloud
world. You know, since you've had a lot of experience with VMware and
now we're looking at this whole new Kubernetti is enabled world, and just be
curious to hear your thoughts on kind of where that's all going. Because VMware
is obviously a very strong, prodigious company, has been around a long time.
It's done a lot of amazing things. When you look at what they
do, you look at what Kubernetti's does. It's not the same thing,
but it's certainly similar. It's certainly trying to kind of optimize for the cost
of things and the usage of the hardware. Where do you see that going
from? You know, from a customer perspective. If I'm looking at VMware
kubernetties, do I have to choose one of the other? How would I
do that? Yeah? So, I mean VMware obviously has has the solution,
right, they have the virtualized solution. M I remember when I was
in my early IT days, we had a number of projects to get virtualized
then and we did that. Um, Kubernetes is that next step, right?
I think I SAP has been a little cautious with it first. Um,
you know, Hannah is not something you can contain our eyes yet,
but we're hoping that's coming. And some of a lot of their new applications,
you know, like you hear about the Business Technology Platform and things like
data intelligence to be able to do more u IoT type monitoring and make real
time decisions in your RP using IoT data. Those are all containerized, right
and we see a huge future in that, which is why we bought the
support Work solution, because having data storage on top of VMware was one thing.
When it comes to Kubernettes, that's a whole another ball game, and
we realize that early, which is why we bought part works and it's it
is. I've played around with it. It is just a fantastic container data
storage solution that we think is really going to help companies as they make that
journey into into containers. Yeah. No, that's a really really good point,
and I think it's a very clever move on your part. And just
to explain to the audience for the folks who don't know yet, because a
lot of folks are still kind of brushing up on this. You know,
VMware, like SAPs, an old sort of monolithic application. It's a very
large base of code that does lots of different things, right, Whereas kubernettes
the idea is containers where it's little teny tiny processes that get orchestrated from the
engine, from the platform. Right. So it's good to here that SAP
is getting serious about that because to your point, I'm pretty sure it is
the future. I don't see it. I don't think we're going back to
the monolithic. We are doing things. We're going to have to work with
the monolith for a long time. But you know, at the end of
the day, you have to think it's going to be a bit more efficient
with these ephemeral processes. What do you think, Absolutely, yeah, I
totally agree. I totally agree. Um. You know, we recall at
Pure we have a concept of legacy applications and you know, new cloud based
applications, and I think we're starting to see those things converge. Right.
Um. To your point, SAP is always going to be a giant,
monolithic beast, right, you got to run your business somewhere, But all
these ancillary systems that customers have that maybe they built in dot net on top
of sequel server over the day, is those are going to become more cloud
based. And cloud eficient and containerize. So we need to be able to
do both. You need to be able to provide data for both. And
that's that's where pure storage comes in. Yeah, and it seems to me,
I think we're got to about a minute left here. It seems to
me that you do have this well ace in the whole and that you've got
this OS for data and operating system for data which can talk to Kubernetes,
which can talk to SAP, which you can talk to all these applications.
At the end of the day, you've got applications that need data as and
when it's required, and they don't want too much more than that. They
just want the data that they need to get something done. Right, So
if you can abstract out the management of that data and extract out that layer,
there are so many things. Like you said, it will be the
last migration you ever do. That is not a small thing, because data
migrations kind of suck, right, There's just a horrible thing to do.
A number of things can go wrong. You fields get truncated, you know,
certain feels still get mapped properly, and you don't notice. And even
your testing didn't find it. I mean, there are lots of things didn't
go wrong. So if you can solve it, solve the Darwin problem right
real quick. I'm with you, Eric, I agree. I'm going to
get you a free T shirt. I like that. I appreciate that.
Well. Listen, we were talking to Ryan Arsenal of Pure Storage, one
of my favorite companies. That will be right back. You're listening to Inside
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Welcome back to Inside Analysis. Here's your host, Eric Kabanaugh. All
right, folks, back here on Inside Analysis with the legendary Bob Muglia,
former CEO of Snowflake. He's on a lot of boards. He's been doing
a lot of cool stuff for a long time. Now. Here's a very
cool fossil in his office there in his home office that I've seen before.
Crocodile. Yeah, oh cool. They've been around a long time. That
guy still here, that guy's like a one hundred and seventy million years old.
I as always say, I wish, I hope I could look that
good at that age, right exactly, Well, you were finishing a point,
and then we want to talk about this social contract. Go ahead,
yeah, and it's really related to the social contract. You know. What
I was talking about was as these as these artificial intelligence, as it continues
to get smarter, how should we think about it? And here again,
Asthmov gave us guidance, you're not just for the short term and how to
how to work with the agents that we have today and we're becoming the next
few years, but in the long run, as we begin to have artificial
general intelligence that is as smart as we are, or even becomes much much
smarter than we are over time. And that was as he recognized. In
his latter part of his career, he wrote some robot novels. His final
robot novels, he talked about robots who had essentially achieved a GI status and
recognize that the three laws were insufficient, and so they created the robots themselves
created a fourth law. Um. They called it the Zeroth law um as
m'fcut the Zeroth law, which is that a robot must not harm humanity or
allow humanity to come to harm, putting it at a higher level, recognizing
that that that they you know that it's not just about an individual person and
an individual encounter with somebody, but it's really how does this device interact with
society as a whole. And certainly when we think now today, while I
don't believe as M've ever thought about superintelligence the same way he did do today,
his robots had positronic brains in it that couldn't be replicated the way our
digital brains can be. They can be copied in a second, and then
they can share information mation instantly as well. Um that leads that has some
interesting potential. Yet he did give us guidance about how we need to create
really a contract, a social contract as people working with these devices that we're
creating, recognizing that that you know, we will want to coexist with them
over a long period of time. We do need to respect them and they
need to respect us, and we need to find ways where we can benefit
over time and leverage these tools that we've created to benefit society. And I
think the potential is amazing because we've got the reduction of the cost of intelligence
together with knowledge. If we can, if we can can continue to do
things that make humanity more and more productive, it just allows society to flourish
in ways that it's never for us before. Yeah, and you brought up
a couple interesting points throughout the show about the ways that AI will help,
and I think again, in the spirit of knowing how to use the tool
effectively. You can use a tractor effectively, or can use a tractor ineffectively.
It's the same thing here. And what we want to be able to
do is help people understand how these algorithms can solve for very mundane but serious
problems like checking the books, for example, auditing your books. I think
before too long you're going to have the option to just click a button say
automate and QuickBooks will go through up. Here's what we think your tax return
should look like, and you say yes or no and just let it go.
That's coming pretty soon, I think, because if you think about there
are registries now of all the different kinds of businesses in the country. This
is a restaurant, that's a bank, this is a nightclub, this is
an office tower, for example, that can be pretty easily aligned to forms
on your schedule, see for example, around your tax forms. I'm kind
of surprised that hasn't happen enough yet, but I think it's because it is
such a serious task. But think about how that just drives people crazy every
year to do their taxes. The irs, the irs IT systems are the
worst on the plat unfortunately, Well there is that, there is government and
so but back to government, right. So I've been a big advocate for
transparency and governance, and I lobbied for transparency and federal spending eighteen years ago,
and amazingly it happened, like good things happen, and they pass this
act, the Transparency Act twenty thousand and five. But now I think we
need transparency of process and of systems so we can see the whole thing and
then you can see where the money goes. Because when people can see,
there is trust. If you can't see, if you have a black box,
there's not so much trust. What do you think? I totally agree.
I mean, I'm a huge fan of transparency. First of all.
In creating transparency and as we create these artificial intelligence agents that are working with
us. Is one of the reason why open source is such an important component
in associated with that, but very much in the solutions that get created as
well. Um. I'm a big believer, for example, that one of
the biggest things that can happen to reduce the cost of medical bills in this
country is total transparency of pricing. The only thing in our lives where which
is a consumer we purchase where we have no transparency in prices. Right,
you buy a house, you know what that costs. You buy a car,
you know what that costs. Anything you go to the grocery store,
but you have no idea when you go into hospital what that's going to cost.
And and uh and things like this can really help because it's not clear
that that that the outcomes match the cost structures. In fact, sometimes they're
inversely related to the COLT right. So and again I think these agents can
help to uh provide visibility to it. But it all comes down to people
and what people want. Um, I'm convinced there isn't transparency because they're in
the healthcare system, because they're entities that don't want transparency. And yeah,
there are forces that don't want to and see that the point to be made
here it seems to me you've got just about three minutes left here, is
that the transparency will come. I promise you you look at like the Pentagon
papers for example, or these various documents that get surfaced and get out there.
Transparency will come at some point, So will you be prepared for it?
And I think that's a bit of an impetus on the powers that be
to get that ball rolling. What do you think, well, I mean,
I think in general we're in a world where the Internet has been the
biggest impact on transparency since the beginning of mankind. I think the ability to
people see anything. You can go anywhere you want. You don't like what
this site is saying, go to another site, and I think AI will
be like that as well. AI will be another source that people can go
to, another trusted source. We're already, as I say, with these
early answer bots that we're starting to see, you know, being able to
create trusted sources that people can go to that are new and different and and
I think that that's only going to expand over time, and in general that
AI can open up society in a way that it's never that it's never been.
But again, it's all going to come back to people and I and
as I say, this is a tool. AI is a tool. And
when we talk about AI doing this or AI doing that, remember it's the
it's a person behind that AI that's getting it to do what it's doing.
They're not off doing something on their own. That may happen in ten years,
but that's not happening now. What's happening now is people are using them
for everything, and as I say, they'll use them for good, bad,
and evil. And it's up to society to build the right level of
governance, this idea of a social contract that I think starts now but will
continue to become progressively more important as AI takes a bigger and bigger role in
our lives. Yeah, that's right. And folks, the book we're talking
about is called The Data Preneurs, The Promise of AI and the Creators Building
Our Future by Bob Muglia and Steve Hamps. Look that up on Amazon.
We'll be sending out an email probably next week with a link to all that.
And a last thought to close, I mentioned that term black box.
You know. I asked chat GBT which database it used and it demured it
said, well, I'm not allowed to tell you that, and that's not
transparency, right. I mean, I think that part of the legislation of
the contract that we're going to get here out of this and something will happen,
really should be an emphasis on transparency. Elon Musk of course made part
of the Twitter algorithm public. He open source that he's talked about going for
total transparency. I think that is of crucial importance because the feed. And
I'd love to talk to you some other time about this other topic of the
media and getting trust in media, and getting accuracy in media reports and being
able to kind of gauge things. We have all these fact checkers these days,
But the feed used to come from ABC and CBS and NBC. In
the New York Times, it was very static. It was either on your
TV or in your newspaper. Then, of course we had the bloggers.
The Internet came along, and then social media came in and sort of co
opted that feed. But they can troll the feed, so whether it's Facebook
or Instagram or YouTube or whatever, they're in control that fee for their purposes,
which of course is to make money or gain influence in some fashion.
I think transparency is going to be the answer to save us from a lot
of that stuff. What do you think? I'm a big fan. I
agree with that having multiple choices for people is really important. I've personally believe
that if all you're watching is one news channel or getting reading one newspaper,
you're only seeing part of the picture. Is regardless of which one it is
you're only seeing, you're only seeing half or less of the picture, and
you need to get news and information from a wide variety of different sources.
Fortunately, that's relatively easy to do today that there are a wide variety of
sources on the Internet, but people still have to take it upon themselves to
do it. Now. AI may make that easier over time, may help
us to get a breadth of different perspectives. I could spend an hour talking
about about what truth is and this idea of pursuing truth because there are multiple
truths in There are very few things that are well. Some scientific things are
generally regarded as true, but most things in our society there is disagreement about
and that has to get surfaced, and that also must be surfaced to these
AI bots so that they have that information as they're forming their summaries in their
opinions. I'm totally believe that the more information that's out there and the more
transparent we can make this, the more of these bots that created, the
better we are all off. I don't think it should be held in the
hands of a few people. I think it needs to be available to society
as a whole. That's right, the marketplace of ideas. Look at all
those ideas out there. Let the cream rise to the top, folks,
look this book up online. Bob Maglia, the datapreneurs, the promise of
AI, and the creators building our future. You have been listening to Inside
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