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>> Okay, welcome back everyone to theCUBEs coverage here
in Las Vegas for AWS re:MARS.
Re:MARS stands for machine learning,
automation, robotics, and space.
Part of the re-show is reinforces security.
And the big show re:Invent at the end of the year
is the marquee event.
Of course, theCUBE is at all three and more coverage here.
We've got a great guest here
Ryan Ries, practice lead, data analytics,
machine learning at Mission Cloud.
Ryan, thanks for joining me.
>> Absolutely.
I'm glad--
>> So we were talking before you came on camera
about Mission Cloud.
It's not a mission as in a space mission.
That's just the name of the company
to help people with their mission
to move to the cloud and we're space show.
You can make that, it's almost like plausible.
I can see a Mission Cloud coming someday.
>> Ryan: Yeah, absolutely.
>> You got the name.
>> We got it.
We're ready.
>> (laughs) You guys help customers get to the cloud.
So you're working with all the technologies
on AWS stack and people who are either lifting
and shifting or cloud native, born in the cloud, right?
>> Absolutely.
I mean, we often see some companies talk about lift
and shift, but we try to get them past that
because often a lift and shift means like,
say you're on Oracle,
you're bringing your Oracle licensing,
but a lot of companies want to innovate and migrate
more than they want to lift and shift.
So that's really what we're seeing in market.
>> You see more migration.
Less lift and shift.
>> Yeah, exactly.
Because they're trying to get out of an Oracle license.
They're seeing if that's super expensive
and you can get a much cheaper product on AWS.
>> What's the cutting op areas right now
that you're seeing with cloud at Amazon.
'Cause Amazon is at their birthday,
DynamoDB just celebrate their 10th birthday.
Where are they in your mind relative to the enterprise
in terms of the services and where this goes next
in terms of the on-premise.
You got the hybrid model, everyone sees that,
but like you got outpost.
Not doing so as good as say EKS
or other cool serverless stuff.
>> I mean, that's a great question.
One of the things that's you see
from AWS is really innovation, right?
They're out there, they have over 400 microservices.
So they're looking at all the different areas you have
on the cloud and that people are trying to use.
And they're creating these microservices
that you string together, you architect them all up
so that you can create what you're looking for.
One of the big things we're seeing right, is with SageMaker.
A lot of people are coming in, looking for ML projects
trying to use all the hype
that you see around that doing prediction.
NLP and computer vision are super hot right now.
We've helped a lot of companies start to build out
these NLP models where they're doing all kinds of stuff.
You use them in gene research,
they're trying to do improvements in drugs and therapeutics.
It's really awesome.
And then we do some e-commerce stuff
where people are just looking at, how do I figure out
what are similar things on similar websites
for search companies.
>> Awesome.
Take me through the profile of your customer.
You have a mix of business.
Can you break down the target,
are they the small, medium size enterprise,
large, all the above?
>> So Mission started working with a lot of startups
and SMBs and then as we've grown
and become a much larger company
that has all the different focus areas,
we started to get into enterprise as well
and help a lot of pretty well known enterprises out there
that are not able to find the staff that they need
and really want to get into the cloud.
>> I wanted to dig into the staffing issues
and also to the digital transformation journey.
IT, we all kind of know what's turning into,
the more dashboards, more automation, DevOps,
cloud native applications.
All good.
And I can see that journey path.
Now the reality is, how do you get people
who are going to be capable of (laughs) doing the ML,
doing the DevOps, DevSecOps but what about cybersecurity?
I mean is a ton of range of issues
that you got to be competent on to kind of survive
in this multi-disciplined world.
The old days of I'm the top of rack switch guy is over.
>> Absolutely.
It's a really good question.
It's really hard.
And that's why AWS has built out that partner ecosystem
because they know companies can't hire enough people
to do that.
If you look at just a migration into a data lake,
on-prem often you had one guy doing it
but if you want to go to the cloud it's like you said, right,
you need a security guy.
You need to have a data architect.
You need to have a cloud architect.
You need to have a data engineer.
So in the old days maybe you needed one guy.
Now you have to have five.
And so that's really why partners are valuable to customers
is we're able to come in, bring those resources,
get everything done quickly and then turn it over.
>> We were talking again,
before we came on camera here live,
you guys have a service led business
but the rise of MSPs, managed service providers is huge.
We're seeing it everywhere
mainly because the cloud actually enables that,
you're seeing it for things like Kubernetes, serverless
certain microservices have certain domain expertise
and people are making a living
providing great managed services.
You guys have managed services.
What's that phenomenon.
Do you agree with it?
Why did that come about?
Does it keep going?
Is it a trend or is it a one trick phony?
>> I think it's a trend.
I mean, what you have, it's the same skills gap, right?
Is companies no longer want that single point of failure.
We have a pool model with our managed services
where your team's working with a group of people.
And so we have that knowledge and it's spread out.
And so if you're coming in
and you need help with Kubernetes
we got a Kubernetes guy in that pool to help you, right.
If you need data, we got a data guy.
And so it just makes it a lot easier where,
hey, I can pay the same as one guy and get a whole team
of like 12 people that can be interchangeable
onto my project.
So I think you're going to see managed services
continue to rise and companies just working in that space.
>> Do you see a new skill set coming
that's kind of got visibility right now
but not full visibility that's going to be needed?
I asked this
because the environment's changing for the better obviously,
but you're seeing companies that are highly valued
like Databricks, Snowflake,
they're getting killed on valuation.
So they've got to have a hard time retaining talent.
My opinion, my opinion probably be true
if you're Databricks,
you can't raise that $45 billion valuation
try to hire senior people.
They're going to be underwater from day one.
So there's going to be a real slow down in these unicorns,
these mega unicorns, decacorns, whatever they're called
because they got to refactor the company
stock equity package to attract people.
So they got to put them on a flat foot.
And then that next question is,
do they actually have the juice,
the goods to go to the new market?
That's another question.
So what I mean, what's your take on, you're in the trenches.
You're in the front lines.
>> Yeah, that's a great question.
I mean, and it's hard for me to think about
whether they have the juice.
I think Snowflake
and Databricks have been great for the market.
They've come in, they've innovated.
Snowflake was cloud native first.
So they built for the cloud.
And what that's done is push all the hyperscalers
to improve their products, right.
AWS has gone through and drastically
over the last three years, improved Redshift,
like I mean it's night and day
from three years ago to today.
>> And you think Snowflake put that pressure on them.
>> Snowflake, absolutely put that pressure on them.
I don't know whether they would've gotten to that same level
if Snowflake wasn't out there stealing market share.
But now when you look at it,
Redshift is much cheaper than Snowflake.
So how long are people going to pay that tax to have Snowflake
versus switching over to native series.
>> Plus got some nice data clean room,
had some nice lock in features.
Only on Snowflake.
The question is, will that last?
I see you're smiling.
Go ahead.
>> Clean room's a concept that was actually made by Google.
I know Snowflake's trying to capture it as their own,
but Google's the one that actually launched
the clean room concept because of marketing and all of that.
>> Google also launched a semantic layer
which Snowflake's trying to copy that.
What does that mean to you
when you hear the word semantic layer, what does that mean?
>> I mean, semantic layer
just is really all about metatags, right?
How am I going through to figure out
what data do I actually have in my data lake
so that I can pull it for whatever I'm trying to do,
whether it's dashboarding or whether it's machine learning.
You're just trying to organize your data better.
>> Ryan, you should be a CUBE host.
You're like a masterclass here in the IT and cloud native.
I got to ask you since you're here,
since we're having the masterclass
of being put in a clinic here,
a lot of clients are confused
between how to handle the control plane and the data plane,
'cause machine learning right now is at an all time high.
You're seeing deep racer.
You're seeing robotic space,
all driving by machine learning.
Swami said it today, the companion coder, right?
The code whisperer, that's only going to get stronger.
So machine learning needs data and feeds on data.
So everyone right now is trying to put data in silos.
'Cause they think, oh, compliance,
you got to create a data plane
and a control plane that makes it highly available
so that can be shared.
Now a lot of people are trying to own the data plane
and some are trying to own the control plane or both.
What's your view on that?
Because I see customers say, look, I want to own my own data,
'cause I can control it.
Control plane, I can maybe do other things.
And some are saying, I don't know what to do
and they're getting forced to take both a control plane
and a data plane from a vendor.
What's your reaction to that?
>> So it's pretty interesting.
I actually was presenting at a tech target conference
this week on exactly this concept, right.
Where we're seeing more and more words out there, right?
It was data warehouse and it was data lake
and it's lake house and it's a data mesh
and it's a data fabric.
And some of the concepts you're talking about
really coming to that data mesh, data fabric space.
And what you're seeing is, data's going to become a product
where you're going to be buying a product and the silos yes.
Silos exist.
But what companies have to start doing is
and this is the whole data mesh concept is,
hey, yes, you finance department you can own your silo
but now you have to have an output product
that's a data product, that every other part of your company
can subscribe to that data product
and use it in their algorithms or their dashboard
so that they can get that 360 degree view of the customer.
So it's really, key that, you work within your business.
Some business are going to have that silo
where the data mesh works great.
Others are going to go--
>> And what do you think about that?
Because I mean my thesis would be,
hey, more data, better machine learning.
Is that the concept?
>> (chuckles) That's a misconception.
>> What's the rationale to share the data
like that in data mesh.
>> So having more of the right data--
>> Here it is.
>> Improves.
Just having more data in general, doesn't improve.
And often the problem is, in the silos you're getting to
is you don't have all the data you want.
I was doing a big project about shipping
and there's PII data when you talk about shipping, right?
Person's addresses, that's owned by one department
and you can't get there.
But how am I supposed to estimate the cost of shipping
if I can't get data from where a person lives.
It's just not doable.
>> So another wrinkle in the equation is latency.
The right data at the right time is another factor.
Is that factored into data mesh
versus these other approaches?
Because I mean, people are streaming data, I get that.
We're seeing a lot of that.
But talk about getting data fast enough
before the decisions are made.
Is that an issue or is this just BS?
>> I'm going with BS.
So people talk about real time real.
Real time's great if you need it,
but it's really expensive to do.
Most people don't need real time.
They're really looking for, I need an hourly dashboard
or I need a daily dashboard.
And so pushing into real time
is going to be an added expense that you don't really need.
>> But cyber maybe, is that not maybe need real time.
>> Well, cybersecurity, yeah.
I mean there's definitely certain applications
that you need real time.
>> But don't over invest in fantasy
if you don't need, an hour is fine, right?
>> If you're a business
and you're looking at your financials,
do you need your financials every second?
Is that going to do anything for you?
>> Got it.
And so this comes back down to data architecture.
So the next question I ask,
'cause I had a great conversation
with the Fiddler AI CEO earlier,
and he was at Facebook and then Pinterest,
he was a data architect
and he built everything he said themselves.
We were talking about all the stuff that's available now,
are all the platforms and tools available
to essentially build the next Facebook
if someone wanted to from scratch.
I mean hypothetically thought exercise.
So the ability to actually ramp up
and code a complete throwaway and rebuild
from the ground up is possible.
>> Absolutely.
>> And so the question is, okay, how do you do it?
How long would it take?
I mean, in an ideal scenario,
make some assumptions here, you got the budget,
you got the people,
how long to completely roll out a brand new platform.
>> Now it's funny you asked that
because about a year ago
I was asked that exact same question by a customer
that was in the religious space
that basically wanted to build a combination of Facebook,
Netflix and Amazon all together for the religious space,
for religious goods and church sermons.
We estimated for him about a year
and about $9 million to do it.
>> I mean, that's a A round these days, series A.
So it's possible.
>> Absolutely.
>> So enterprises, what's holding them back,
just dogma, process, old school legacy,
or are people taking the bold move to take more aggressive,
swiping out old stuff and just completely rebuilding?
Or is it a talent issue?
What's the enterprise current mode of reset?
>> I think it really depends on the enterprise
and their aversion to risk.
(John laughs)
Some enterprises and companies are really out there
wanting to innovate.
I mean there's companies,
an air conditioning company that we worked with
that's totally, Nest was eating all their business.
So they came in and created a whole IoT division,
to chase that business, that Nest stole from them.
I think often a company's not necessarily going to innovate
until somebody comes in and starts stealing their lunch.
>> Ryan, Andy Jassy talked about this two re:Invents ago.
And then Adam Saleski said the same thing this year
on a different vector
but kind of building on what Andy Jassy said.
And it's like,
you could actually take new territory down faster.
You don't have to kill the...
I'm paraphrasing.
You don't have to kill the old to bring in the new,
you can actually move on new ideas
with a clean sheet of paper
if you have that builder mindset.
And I think that to me is where I'm seeing.
And I'd love to get your reaction because,
if you see an opportunity to take advantage
and take territory and you have the right budget, time
and people, you can get it.
>> Ryan: Oh absolutely.
>> It's gettable.
So a lot of people have this fear of,
oh, that's not our core competency.
And they're the frog and boiling water. (laughs)
>> My answer to that is I think part of it's VCs, right?
VCs have come in and they see the value of a company
often by how many people you hire, right.
Hire more people and the value is going to go up,
but often is a startup you can't hire good people.
So I'm like, well why are you going to go hire
a bunch of random people?
You should go to a firm like ours that knows AWS
and can build it quickly for you
'cause then you're going to get to the market faster
versus just trying to hire a bunch of people in,
is what happens.
>> Ryan, I really appreciate you coming on.
I'd love to have you back on theCUBE again sometime.
Your expertise and your insights are awesome.
Give a commercial for the company,
what you guys are doing, who you're looking for,
what you want to do, hiring or whatever your goals are.
Take a minute to explain what you guys are doing
and give a quick plug.
>> Awesome.
So Mission Cloud, we're a premier AWS consulting firm.
If you're looking to go to AWS or you're in AWS
and you need help and support,
we have a full team, we do everything.
Resell, MSP, professional services.
We can get you into the cloud, optimize you,
make everything run as fast as possible.
I also have a full machine learning team,
since we're here at re:MARS, we can build you models.
We can get them into production,
can make sure everything's smooth.
The company's hiring.
We're looking to double in size this year.
So look me up on LinkedIn, wherever, happy to take over.
>> You mentioned theCUBE, you get a 20% discount.
(Ryan laughs)
He's like, no, I don't approve that.
Thanks for coming on theCUBE.
Really appreciate it.
Again, machine learning, Swami said on stage,
this can be a full time job just tracking
just the open source projects.
Never mind all the different tools and like platform.
So I think you're going to have a good tailwind
for your business.
Thanks for coming on theCUBE.
Appreciate it.
>> Thanks a lot John.
>> Ryan Ries here on theCUBE.
I'm John Furrier, more live coverage here at re:MARS 2022,
after this short break.
Stay with us.
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