(light techno music)
>> Okay, welcome back, everyone
from theCUBE's coverage
of AWS re:Mars here in Las Vegas.
Back in person,
I'm John Furrier, host of theCUBE.
Re:MARS stands or Machine learning,
Automation, Robotics, and Space.
And we're covering all the action two days, day two.
And we're here with Regina Manfredi,
who's the VP of global CSPs,
Cloud Service Providers Alliances with Teradata.
Great to see you.
Cloud service providers or-
>> Cloud services providers, the hyperscalers.
>> Hyperscalers, the big guys.
All the CapEx, Amazon. >> Yes.
>> The big guys.
>> Indeed, thanks for having me.
>> Yeah, Thanks for coming on.
So tell about your role.
So alliances, you're here with AWS.
What's the role with AWS and Teradata?
>> So AWS and Teradata have recently entered
into a strategic collaboration agreement
where we're really focused on building solutions together,
leveraging AWS services,
as well as Teradata's outstanding architecture,
as it relates to the data analytics platform
that we provide for our customers in the cloud today.
And we're really trying to drive better outcomes
for data scientists, business analysts, etc.
>> You know, just recently,
did a CUBE conversation with Teradata,
and I was really surprised to find,
not shocked, but kind of surprised,
the scale of the computation that's going on
in some of the cloud things you're doing.
And you have the legacy on-premises data warehouse
traditional business as well. >> Regina: We do.
>> And there's a huge shift going on.
A lot of the kind of upstarts,
"Oh, data warehouse, old school.
Data warehouse, it's antiquated, old,"
but that's not true.
You guys have a lot of cloud action.
>> We do, we have substantial cloud action
that's occurring with our customers today.
We actually just released earlier this year
an announcement around 1,000 node tests
in the cloud together with AWS,
and had success, no downtime, no failures at all.
And so we're pretty proud about that,
and excited about what that's going to hold
for our customers who need that level of scale.
>> Well, Regina, I got to tell you,
I have a little bit of a confession here.
I'm a cloud data nerd by my training.
And, you know, I've always watched
all the different kind of levels of transformation
with the industry,
and you know, this is going to change that,
that's going to kill that.
Everything's going to be killed and then it never dies,
but it just changes.
Even today, SQL is still like the prominent language,
it's never going to,
in fact it's amplified further
because that's what people like.
So that just proves that things don't always get replaced.
And so I wanted to ask you this
because as we're here at this event at re:MARS,
you have space,
you have all these ambitious positive goals,
and they just need to do some machine learning.
They need some cloud, they need some, they need to have
the solutions. >> Regina: Yes.
They're not going to like get in the weed and say,
"Oh, this is a better Hadoop cluster
than this Kubernetes cluster.
So it's not about sometimes the tech,
it's about the solution.
>> It is, and one of the things that was interesting for us
in our session earlier this week
was the fact that we had so many customers approach us
after that session and say,
"I just need help preparing my data.
Running my models, training my models,
and making sure that they run and can be deployed.
And I don't want to move all this data all the time
and have all this failure rate that I'm experiencing."
And so it was very basic requirements and needs
as people begin into their journey on AI/ML
for their business.
And so it was reaffirming that we're on the right track
and driving the right tools for them.
I want to get your perspective on what you're thinking
about the show,
but first, I want to ask this since you brought that up.
Swami was on stage and he said,
"You can spend your entire time and your career
just trying to figure out what's going on,
machine learning." >> Regina: Yup.
>> "Which open source framework's going to be better
than the other one."
I mean, it's just a lot of work to even figure it out.
We just had the Fiddler's AI CEO on
who worked out all the hyperscalers,
say Facebook tend to, you know, real,
you know, super alpha geek, if you will.
And he was saying,
and we were talking about open source, free software,
integrations are a big part of where cloud scale,
and the value is being captured
for companies and people who are doing projects.
Integrating some managed services,
so this is where I see you, guys,
going right now with Teradata,
having all these cloud services built on the install base.
>> Right. Which is not,
doesn't hurt that at all.
It just only helps it
as they would migrate to cloud,
its integrations,
so you take a little bit of Amazon here,
a little bit of Teradata there.
>> Regina: Absolutely. >> What's your perspective,
what's your reaction to that?
>> So, I agree.
And we think that's part of our secret sauce.
You know, what we want to have
is a data analytics platform in the cloud
that allows data scientists, and architects, etc.,
to bring their own tools.
So whatever they're utilizing today,
we want them to be able to utilize it in vantage,
and make sure that,
A, can drive some efficiencies,
and also, some better, smarter economics,
as it relates to their particular projects.
And so I agree with you 100% ,
and would tell you that we view that
as somewhat our competitive advantage.
It's not about being all proprietary.
We want those integrations,
and we've got dozens of them with AWS, and-
>> Can you give example,
can you give a couple examples of some integrations
that highlight that?
>> Sure, so right now we've got an integration
with SageMaker today that allows our customers
or data scientists to come in,
prepare the data,
and actually leverage SageMaker
to build and train the models,
and then deploy very quickly and easily
without having to do all the data movement
within their architecture.
>> It's just so fascinating.
I can't wait to have more conversation
with you guys about this
because I just think the world's spinning
in a direction where,
with low code, no code, >> Regina: Yup.
>> you can see code, companion whisperer,
that they have CodeWhisperer they launched today,
they're writing subroutines for machine learning.
And so it's not autocomplete, it's subroutine.
So you're seeing all these advances on the technology.
So it comes back to the building blocks, the integration.
It just seems like going to be like a plug and play.
That's old, were all, are old words.
Mix and match, plug and play, interoperability,
were old words,
like, in the old days.
Now they're becoming more relevant.
What's your take on all that?
>> Yeah, I would agree.
I don't think that we should be competing
against the algorithms, and neither do we.
We want to just actually build out the toolsets
that drive the enablement
based on what a customer's requirements and needs are,
and based on what the investments that they've already made
within their own enterprises.
>> You know, what's interesting about this event,
I love to get your reaction
to what re:MARS means to you
because it's machine learning, automation,
robotics, and space.
Not your typical tech conference.
>> Regina: No. >> Okay, little bit
of a mixed bag there, so to speak.
I love it.
I think it's like super alpha geek, very nerdy,
super nerds are here.
And the topics kind of reflect the future.
For the people that are watching that aren't here,
what's your vibe on the show?
What's your takeaway?
How would you explain what's going on here
from a market perspective,
from a vibe perspective, what's happening?
>> This is my first re:MARS actually,
and I would have to tell you that I feel like
it just, general observation,
a few things, one, the conversations are more meaningful
and we're getting into the meat
of what a data scientist truly needs
in order to be successful in their role
and help drive their enterprise.
That's number one.
So I think, to your point,
we're all kind of geeking out together here.
The other thing that I think is pretty exciting
is the amount of use cases,
and ways in which we are driving impact.
AWS and Teradata driving impact for the business analysts
in the enterprise environment,
but also for the people, their customers.
That's pretty exciting to see.
>> You know, it's interesting.
When I first, was kind of like thinking about the show
and what I was going to expect,
it kind of overexceeded my expectations
in the sense of what I was thinking about IOT,
industrial, and digital innovation.
'Cause that's going to scale.
I think now we're at a tipping point
with machine learning that the industrial, IOT markets
is going to explode 'cause machine learning's ready.
But there was a whole positive,
save the earth angle >> Regina: Yes.
>> that caught my attention. >> Regina: Yes.
You know, the discoveries from space
are going to potentially have impact for the good,
not just a cliche some sustainability messaging.
It was actually real.
>> Right, I think that that's exciting in an area
in which we're excited to explore.
We're doing a lot of work behind the scenes
around sustainability and ESG initiatives
for our customers,
but also for the greater good.
It's about driving outcomes for the greater good
and being responsible with how we approach that.
You know, the other thing I noticed too
from a robotics standpoint,
given I live in California,
is a huge robotics culture there, you know.
It's like bigger than football and baseball,
and some sports.
They provide A and B team and people get cut
from the B team.
There's so much demand to be on the robotics team.
It's not a club, it's a team. >> Regina: Right.
And so, you look at what's going on robotics,
it's so exciting in the sense that if you're young
and you're into tech, this is like-
>> Regina: This is the place to be.
>> I mean, why wouldn't you be hanging out here?
>> Yeah, well, and I visited the booth over
at University of Michigan,
and how they're driving robotics
to help support the human body to go further distances,
and to drive better performance and health for individuals,
and was really impressed with the work that they're doing,
and even saw a use case
and a need where I thought,
you know, I have a quadriplegic sister-in-law,
who I thought,
"Wow, someday, maybe she'll be upright and walking again."
>> John: Yeah. >> And those
were exciting conversations to have
while I was here.
>> The advances on the material management robots
I think is fascinating to see that growth.
Well, let's get back to Teradata real quick
to kind of close out future of what's next.
Obviously, a lot of migration to the cloud happening.
What's the outlook on the landscape
and where do you see it evolving?
Because you're seeing what the hyperscalers are doing,
the cloud service providers,
they're providing the CapEx.
In fact, we coined the term supercloud,
last re:Invent, that's become a thing.
And Charles Fitzgerald would think it's not a thing,
he debates us online all the time on Twitter.
But it's, you can build on top of a CapEx.
>> Regina: Yup.
>> They did all the heavy lifting.
You know, Snowflake, Databricks, the list goes on and on.
So building on top of that to build proprietary advantages
or even just sustainable advantages
is now easier to do.
So superclouds are kind of in play.
So that means whoever's got the playbook can win.
So you guys seem to be executing that playbook
of having the installed base,
and then working with AWS
>> Regina: Yes. >> to ride that wave.
Tell us about the migration strategies you're seeing,
and what are your customers doing specifically,
and take us through a customer
that's leaning into the cloud and driving.
>> So when I think about specific customers
that are leaning in,
you know, the first and most important thing
that we're hearing is,
you've got to be able to scale.
I've got 1,000 nodes or 100 nodes, or whatnot.
And so we're addressing that
because we think that there's a place for hybrid cloud.
We think everyone's moving and rushing towards the cloud,
but even one of our competitors last week
announced that there's a place for on-prem,
and we would agree. >> John: Yeah.
>> So that is something that we're really focused on,
and you take, for example, the automotive industry.
We're seeing a lot of work being done together
with our joint customers, AWS and Teradata,
and some of these auto manufacturers
who are experiencing supply chain issues
and challenges today,
and also need to drive better quality control measures
within their own lines,
in the manufacturing lines.
And so we're working together with them
to look at what type of machine learning and AI
can we be leveraging together
as part of the overall solution
to drive those analytics,
and make sure that they have better quality control
>> You know, that's really good insight
about the on-premise thing.
And I think that supports what we're seeing around hybrid.
We see hybrid as a steady state going forward,
period. >> Regina: Yeah.
>> And that will evolve into multi thing.
Multi-cloud, you want to call it,
or superclouds, and more things.
Basically, distributed computing.
So if you look at the edge here,
the edge is just on-premise.
What is the premise?
It's an edge or big device, small device,
data center is a large edge. >> Regina: Right.
>> And so if you're using cloud hybrid,
the distinction kind of goes away.
And I think this is where we'll going to see the winners
emerge in data.
Because remember, you go back to 2010,
Hadoop was the big thing, big data.
And that kind of crashed and burned.
And then now you're seeing Databricks
picking up a lot of that.
Snowflake, you guys are there.
And so it's still going on,
this transformation in data. >> Regina: It is.
And I think hybrid's a huge deal.
What are customers saying around that?
Because I think they're just trying
to figure out cloud scale.
>> I think they're trying to figure out cloud scale,
I think they're also trying to figure out security.
And so, you know, when we're talking to our customers,
that absolutely is critical.
And I would also suggest that the customer base
is really looking for,
"Hey, don't just help me migrate,
I really need to modernize."
And so driving the right use cases
for the customer is important.
>> You know, another thing that you, guys,
have a lot of core expertise in is governance.
And we've seen how that has played
in all the compliance,
and all these conversations are kind of converging.
Do you have closed, do you have open?
Machine learning needs more data,
dow do you protect it?
So that set a hot area that I see as well.
And that's something that's emerging,
'cause cyber's also involved too,
like, you have cyber security threats on code,
so I'm curious to see how that turns out.
What's your perspective on,
what's Teradata's perspective on the security,
open, closed perspective?
Any- >> It's a priority for,
security is a priority for us.
And I don't think that we've officially made
that determination yet, right?
We're still exploring,
and we're going to do whatever our customers require of us.
In terms of an open, closed perspective,
I think we want to be flexible.
Again, like I said before,
it's about being open and supportive
of whatever the customer requirement is
especially across the different industries.
>> Well, Regina, great to have you on theCUBE.
Thanks for coming.
I really appreciate it.
Great insight, great to catch up on Teradata, cloud play.
Very strong move.
I think it's a good one.
Final question I want to ask you though,
is a little bit more about the personnel in the industry,
like, obviously, if you're young,
you're seeing all this space here,
machine learning's not obvious.
I know schools now are training it,
but you start to see new personas come into the workforce.
Where are the gaps?
I mean, obviously, we have a lot of new opportunities,
like, cybersecurity has a lot of job openings.
Is there any observations that you have around
or advice to younger folks coming in,
from a career standpoint?
Because a lot of job openings
are skills that weren't even taught in school.
>> Regina: Right, that's- >> You know.
>> And then you got the women in check,
and you have all kinds of opportunities now
that aren't just engineering, right?
>> Regina: Yes. >> It's not just engineering.
It's computer science,
so there's a whole in-migration of new talent
coming in the industry.
>> Yes, I think maintaining a curious mind
is really critical,
and taking time to invest in learning.
You know, there are so many resources available to us
at our disposal that that don't cost us a dime.
And so my advice to anybody who is curious,
remain curious, dig in, and get some experience,
and don't be afraid to stick your neck out,
and try it.
>> Well, in this conference
we have robots welcome, you know, in this out there.
>> Yeah. (laughs)
>> Regina, thanks for coming out here.
Really appreciate it
>> John, thank you, it's a pleasure.
>> CUBE coverage here in Las Vegas for Amazon re:MARS.
I'm John Furrier, your host.
Stay with more live coverage after this short break.
(upbeat bright music)