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>> Welcome back to theCUBE's live coverage
of AWS Summit in San Francisco, California.
Events are back.
AWS Summit in New York City this summer,
theCUBE will be there as well.
Check us out there.
I'm glad to have events back.
It's great to have of everyone here.
I'm John Furrier, host of theCUBE.
Dr. Matt Wood is with me, CUBE alumni,
now VP of Business Analytics Division of AWS.
Matt, great to see you.
>> Thank you, John. It's great to be here.
I appreciate it.
>> I always call you Dr. Matt Wood
because Andy Jackson always says,
"Dr. Matt, we would introduce you
on the arena." (Matt laughs)
>> Matt: The one and only. >> The one and only,
Dr. Matt Wood. >> In joke, I love it. (laughs)
>> Andy style. (Matt laughs)
I think you had walk up music too.
>> Yes, we all have our own personalized walk up music.
>> So talk about your new role, not a new role,
but you're running the analytics business for AWS.
What does that consist of right now?
>> Sure. So I work.
I've got what I consider
to be one of the best jobs in the world.
I get to work with our customers
and the teams at AWS
to build the analytics services
that millions of our customers use
to slice dice, pivot,
better understand their data,
look at how they can use that data
for reporting,
looking backwards.
And also look at how they can use
that data looking forward,
so predictive analytics and machine learning.
So whether it is slicing and dicing
in the lower level of Hadoop
and the big data engines,
or whether you're doing ETL with Glue,
or whether you're visualizing the data in QuickSight
or building your models in SageMaker.
I got my fingers in a lot of pies.
>> One of the benefits of having CUBE coverage
with AWS since 2013 is watching the progression.
You were on theCUBE that first year
we were at Reinvent in 2013,
and look at how machine learning
just exploded onto the scene.
You were involved in that from day one.
It's still day one, as you guys say.
What's the big thing now?
Look at just what happened.
Machine learning comes in
and then a slew of services come in.
You've got SageMaker,
became a hot seller right out of the gate.
The database stuff was kicking butt.
So all this is now booming.
That was a real generational change
over for database.
What's the perspective?
What's your perspective on that's evolved?
>> I think it's a really good point.
I totally agree.
I think for machine learning,
there's sort of a Renaissance in machine learning
and the application of machine learning.
Machine learning as a technology
has been around for 50 years, let's say.
But to do machine learning right,
you need like a lot of data.
The data needs to be high quality.
You need a lot of compute
to be able to train those models
and you have to be able to evaluate
what those models mean
as you apply them to real world problems.
And so the cloud really removed
a lot of the constraints.
Finally, customers had all of the data that they needed.
We gave them services to be able to label
that data in a high quality way.
There's all the compute
you need to be able to train the models.
And so where you go?
And so the cloud really enabled this
Renaissance with machine learning.
And we're seeing honestly
a similar Renaissance with data and analytics.
If you look back five to ten years,
analytics was something you did in batch,
your data warehouse ran an analysis
to do reconciliation at the end of the month,
and that was it. (John laughs)
And so that's when you needed it.
But today, if your Redshift cluster isn't available,
Uber drivers don't turn up,
DoorDash deliveries don't get made.
Analytics is now central to virtually every business,
and it is central to virtually
every business's digital transformation.
And being able to take that data
from a variety of sources,
be able to query it with high performance,
to be able to actually then start
to augment that data with real information,
which usually comes from technical experts
and domain experts to form wisdom
and information from raw data.
That's kind of what most organizations
are trying to do when they kind of go
through this analytics journey.
>> It's interesting.
Dave Velanta and I always talk on theCUBE about the future.
And you look back,
the things we're talking about six years ago
are actually happening now.
And it's not hyped up statement
to say digital transformation
is actually happening now.
And there's also times
when we bang our fists on the table saying, say,
"I really think this is so important."
And David says,
"John, you're going to die on that hill."
(Matt laughs)
And so I'm excited that this year,
for the first time, I didn't die on that hill.
I've been saying- >> Do all right.
>> Data as code is the next infrastructure as code.
And Dave's like,
"What do you mean by that?"
We're talking about how data gets...
And it's happening.
So we just had an event on our AWS startups.com site,
a showcase for startups,
and the theme was data as code.
And interesting new trends emerging really clearly,
the role of a data engineer, right?
Like an SRE, what an SRE did for cloud,
you have a new data engineering role
because of the developer onboarding
is massively increasing, exponentially,
new developers.
Data science scientists are growing,
but the pipelining and managing
and engineering as a system,
almost like an operating system.
>> Kind of as a discipline.
>> So what's your reaction
to that about this data engineer, data as code?
Because if you have horizontally scalable data,
you've got to be open,
that's hard (laughs), okay?
And you got to silo the data
that needs to be siloed for compliance and reason.
So that's a big policy around that.
So what's your reaction to data's code
and the data engineering phenomenon?
>> It's a really good point.
I think with any technology project
inside of an organization,
success with analytics or machine learning,
it's kind of 50% technology
and then 50% cultural.
And you have often domain experts.
Those could be physicians
or drug design experts,
or they could be financial experts
or whoever they might be,
got deep domain expertise,
and then you've got technical
implementation teams.
And there's kind of a natural, often repulsive force.
I don't mean that rudely,
but they just don't talk the same language.
And so the more complex a domain
and the more complex the technology,
the stronger their repulsive force.
And it can become very difficult
for domain experts to work closely
with the technical experts
to be able to actually get business decisions made.
And so what data engineering does
and data engineering is, in some cases a team,
or it can be a role that you play.
It's really allowing those two disciplines
to speak the same language.
You can think of it as plumbing,
but I think of it as like a bridge.
It's a bridge between the technical implementation
and the domain experts,
and that requires a very disparate range of skills.
You've got to understand about statistics,
you've got to understand about the implementation,
you got to understand about the data,
you got to understand about the domain.
And if you can put all of that together,
that data engineering discipline
can be incredibly transformative
for an organization because it builds
the bridge between those two groups.
>> I was advising some young computer science
students at the sophomore, junior level
just a couple of weeks ago,
and I told them I would ask someone at Amazon this question.
So I'll ask you, >> Matt: Okay.
since you've been in the middle of it for years,
they were asking me,
and I was trying to mentor them
on how do you become a data engineer,
from a practical standpoint?
Courseware, projects to work on,
how to think, not just coding Python,
because everyone's coding in Python,
but what else can they do?
So I was trying to help them.
I didn't really know the answer myself.
I was just trying to kind of help
figure it out with them.
So what is the answer, in your opinion,
or the thoughts around advice
to young students who want to be data engineers?
Because data scientists is pretty clear on what that is.
You use tools, you make visualizations,
you manage data, you get answers
and insights and then apply that to the business.
That's an application.
That's not the standing up a stack
or managing the infrastructure.
So what does that coding look like?
What would your advice be to
folks getting into a data engineering role?
>> Yeah, I think if you believe this,
what I said earlier
about 50% technology, 50 % culture,
the number one technology to learn
as a data engineer is the tools in the cloud
which allow you to aggregate data
from virtually any source
into something which is incrementally
more valuable for the organization.
That's really what data engineering is all about.
It's about taking from multiple sources.
Some people call them silos,
but silos indicates that the storage
is kind of fungible or undifferentiated.
That's really not the case.
Success requires you to have really purpose built,
well crafted, high performance,
low cost engines for all of your data.
So understanding those tools
and understanding how to use them,
that's probably the most important technical piece.
Python and programming and statistics
go along with that, I think.
And then the most important cultural part,
I think is... It's just curiosity.
You want to be able to, as a data engineer,
you want to have a natural curiosity
that drives you to seek
the truth inside an organization,
seek the truth of a particular problem,
and to be able to engage
because probably you're going to some choice
as you go through your career about
which domain you end up in.
Maybe you're really passionate about healthcare,
or you're really just passionate
about transportation or media,
whatever it might be.
And you can allow that to drive
a certain amount of curiosity.
But within those roles,
the domains are so broad you kind of
got to allow your curiosity to develop
and lead you to ask the right questions
and engage in the right way with your teams,
because you can have all
the technical skills in the world.
But if you're not able to help the team's truth seek
through that curiosity,
you simply won't be successful.
>> We just had a guest, 20 year old founder,
Johnny Dallas who was 16 when he worked at Amazon.
Youngest engineer- >> Johnny Dallas
is a great name, by the way.
(both chuckle) >> It's his real name.
It sounds like
a football player. >> That's awesome.
>> Rock star.
Johnny CUBE, it's me.
But he's young and he was saying...
His advice was just do projects.
>> Matt: And get hands on. Yeah.
>> And I was saying, hey,
I came from the old days
where you get to stand stuff up
and you hugged on for the assets
because you didn't want to kill the project
because you spent all this money.
And he's like, yeah, with cloud you can shut it down.
If you do a project that's not working
and you get bad data no one's adopting it
or you don't like it anymore,
you shut it down, just something else.
>> Yeah, totally.
>> Instantly abandon it and move on to something new.
That's a progression. >> Totally!
The blast radius of decisions
is just way reduced.
We talk a lot about in the old world,
trying to find the resources
and get the funding is like,
all right, I want to try out
this kind of random idea
that could be a big deal for the organization.
I need $50 million and a new data center.
You're not going to get anywhere.
>> And you do a proposal, working backwards, documents
all kinds of stuff. >> All that sort of stuff.
>> Jump your hoops. >> So all of that is gone.
But we sometimes forget that a big part of that
is just the prototyping
and the experimentation
and the limited blast radius
in terms of cost, and honestly,
the most important thing is time,
just being able to jump in there,
fingers on keyboards, just try this stuff out.
And that's why at AWS, we have...
Part of the reason we have so many services,
because we want, when you get into AWS,
we want the whole toolbox
to be available to every developer.
And so as your ideas develop,
you may want to jump from data that you have
that's already in a database
to doing realtime data.
And then you have the tools there.
And when you want to get into real time data,
you don't just have kinesis,
you have real time analytics,
and you can run SQL against that data.
The capabilities and the breadth
really matter when it comes to prototyping.
>> That's the culture piece,
because what was once a dysfunctional behavior.
I'm going to go off the reservation
and try something behind my boss' back,
now is a side hustle or fun project.
So for fun, you can just code something.
>> Yeah, totally.
I remember my first Hadoop projects.
I found almost literally a decommissioned
set of servers in the data center that no one was using.
They were super old.
They're about to be literally turned off.
And I managed to convince the team
to leave them on for me for another month.
And I installed Hadoop on them
and got them going.
That just seems crazy to me now
that I had to go and convince anybody
not to turn these servers off.
But what it was like when you-
>> That's when you came up with Elastic MapReduce
because you said this is too hard,
we got to make it easier. >> Basically yes.
(John laughs)
I was installing Hadoop version Beta 9.9 or whatever.
It was like, this is really hard.
>> We got to make it simpler.
All right, good stuff.
I love the walk down memory Lane.
And also your advice. Great stuff.
I think culture is huge.
That's why I like Adam's keynote at Reinvent,
Adam Selipsky talk about Pathfinders and trailblazers,
because that's a blast radius impact
when you can actually have innovation organically
just come from anywhere.
That's totally cool. >> Matt: Totally cool.
>> All right, let's get into the product.
Serverless has been hot.
We hear a lot of EKS is hot.
Containers are booming.
Kubernetes is getting adopted,
still a lot of work to do there.
Cloud native developers are booming.
Serverless, Lambda.
How does that impact the analytics piece?
Can you share the hot products
around how that translates? >> Absolutely, yeah.
>> Aurora, SageMaker.
>> Yeah, I think it's...
If you look at kind of the evolution
and what customers are asking for,
they don't just want low cost.
They don't just want this broad set of services.
They don't just want those services
to have deep capabilities.
They want those services to have
as lower operating costs over time as possible.
So we kind of really got it down.
We got built a lot of muscle,
a lot of services about getting up
and running and experimenting
and prototyping and turning things off
and turning them on and turning them off.
And that's all great.
But actually, you really only
in most projects start something once
and then stop something once,
and maybe there's an hour in between
or maybe there's a year.
But the real expense in terms of time
and operations and complexity
is sometimes in that running cost.
And so we've heard very loudly and clearly
from customers that running cost
is just undifferentiated to them.
And they want to spend more time on their work.
And in analytics,
that is slicing the data,
pivoting the data, combining the data,
labeling the data, training their models,
running inference against their models,
and less time doing the operational pieces.
>> Is that why the service focuses there?
>> Yeah, absolutely.
It dramatically reduces the skill required
to run these workloads of any scale.
And it dramatically reduces
the undifferentiated heavy lifting
because you get to focus more of the time
that you would have spent on the operations
on the actual work that you want to get done.
And so if you look at something
just like Redshift Serverless,
that we launched a Reinvent,
we have a lot of customers
that want to run the cluster,
and they want to get into the weeds
where there is benefit.
We have a lot of customers
that say there's no benefit for me,
I just want to do the analytics.
So you run the operational piece,
you're the experts.
We run 60 million instant startups every single day.
We do this a lot. >> John: Exactly.
We understand the operations-
>> I just want the answers. Come on.
>> So just give me the answers
or just give me the notebook
or just give me the inference prediction.
Today, for example, we announced
Serverless Inference.
So now once you've trained
your machine learning model,
just run a few lines of code
or you just click a few buttons
and then you got an inference endpoint
that you do not have to manage.
And whether you're doing one query against
that end point per hour
or you're doing 10 million,
we'll just scale it on the back end.
I know we got not a lot of time left,
but I want to get your reaction on this.
One of the things about the data lakes
not being data swamps has been,
from what I've been reporting
and hearing from customers,
is that they want to retrain
their machine learning algorithm.
They need that data,
they need the real time data,
and they need the time series data.
Even though the time has passed,
they got to store in the data lake.
So now the data lake's main function
is being reusing the data to actually retrain.
It works properly.
So a lot of post mortems turn
into actually business improvements
to make the machine learnings smarter, faster.
Do you see that same way?
Do you see it the same way?
>> Yeah, I think it's really interesting
>> Or is that just... >> No, I think
it's totally interesting
because it's convenient
to kind of think of analytics
as a very clear progression
from point A to point B.
But really, you're navigating terrain
for which you do not have a map,
and you need a lot of help
to navigate that terrain.
And so having these services in place,
not having to run the operations
of those services,
being able to have those services
be secure and well governed.
And we added PII detection today.
It's something you can do automatically,
to be able to use any unstructured data,
run queries against that unstructured data.
So today we added text queries.
So you can just say, well,
you can scan a badge, for example,
and say, well, what's the name on this badge?
And you don't have to identify where it is.
We'll do all of that work for you.
It's more like a branch
than it is just a normal A to B path, a linear path.
And that includes loops backwards.
And sometimes you've got to get
the results and use those
to make improvements further upstream.
And sometimes you've got to use those...
And when you're downstream,
it will be like, "Ah, I remember that."
And you come back
and bring it all together. >> Awesome.
>> So it's a wonderful world for sure.
>> Dr. Matt, we're here in theCUBE.
Just take the last word
and give the update while you're here
what's the big news happening
that you're announcing here at Summit
in San Francisco, California,
and update on the business analytics group.
>> Yeah, we did a lot of announcements in the keynote.
I encourage everyone to take a look at,
that this morning with Swami.
One of the ones I'm most excited about
is the opportunity to be able
to take dashboards, visualizations.
We're all used to using these things.
We see them in our business intelligence tools,
all over the place.
However, what we've heard from customers
is like, yes, I want those analytics,
I want that visualization,
I want it to be up to date,
but I don't actually want to have to go from my tools
where I'm actually doing my work
to another separate tool
to be able to look at that information.
And so today we announced
1-click public embedding for QuickSight dashboard.
So today you can literally as easily
as embedding a YouTube video,
you can take a dashboard
that you've built inside QuickSight,
cut and paste the HTML,
paste it into your application and that's it.
That's what you have to do.
It takes seconds.
>> And it gets updated in real time.
>> Updated in real time.
It's interactive.
You can do everything
that you would normally do.
You can brand it,
there's no power by QuickSight button
or anything like that.
You can change the colors,
fit in perfectly with your application.
So that's an incredibly powerful way
of being able to take an analytics capability
that today sits inside its own little fiefdom
and put it just everywhere.
Very transformative.
>> Awesome.
And the business is going well.
You got the Serverless detail win for you there.
Good stuff.
Dr. Matt Wood, thank you for coming on theCUBE.
>> Anytime. Thank you.
>> Okay, this is theCUBE's coverage
of AWS Summit 2022 in San Francisco, California.
I'm John Furrier, host of theCUBE.
Stay with us for more coverage
of day two after this short break.
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