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>> Hey everyone, welcome back to Las Vegas.
This is theCUBE's live coverage of day one
of Snowflake Summit '22 fourth annual.
We're very happy to be here.
A lot of people here,
Lisa Martin with Dave Vellante.
Dave's always great to be at these events with you,
but man, this one is shot out of the cannon from day one.
>> Data, data, data,.
>> Data, data, data.
That's what you heard of here first.
We have two guests joining us next.
Please welcome, Matthew Scullion,
who's an alumni of theCUBE, CEO and founder of Matillion,
and Harveer Singh, Chief Data Architect
and Global Head of Data Engineering from Western Union.
Welcome gentlemen.
>> Thank You.
Great to be here.
>> We're going to unpack the Western Union story in a second,
I love that, but Matthew, I wanted to start with you.
Give the audience who might not be familiar
with Matillion an overview, your vision,
your differentiators,
your joint value statement with Snowflake.
>> Of course.
Well, first of all,
thank you for having me on theCUBE, again.
Matillion's mission is to make the world's data useful
and we do that by providing a technology platform
that allows our customers to load, transform,
synchronize and orchestrate data
on the Snowflake data cloud
and on the cloud in general.
We've been doing that for a number of years.
We're co-headquartered in the U.K and the U.S,
hence my daft accent,
and we work with all sorts of companies,
commercial scale, large enterprises, particularly
including of course,
I'm delighted to say our friends at Western Union.
So that's why we're here today.
>> And we're going to talk about that in a second
but I want to understand what's new
with the data integration platform
from Matillion's perspective.
Lots of stuff coming out, give us an overview.
>> Yeah, of course, it's been a really busy year
and it's great to be here at Snowflake Summit
to be able to share some of what we've been working on.
You know, the Matillion platform
is all about making our customers as productive as possible,
in terms of time to value insights
on their analytics, data science,
AI projects, like get you to value faster,
and so the more technology we can put in the platform
and the easier we can make it to use,
the better we can achieve that goal.
So this year we've shipped a product that we call MDL 2.0,
that's enterprise focused,
exquisitely easy to use batch data pipelines.
So customers can load data even more simply
into the Snowflake data cloud.
Very excitingly, we've also launched Matillion CDC
and so this is an industry first cloud native
writer head log based change data capture.
I haven't come up with a shorter way of saying that,
but enterprise customers need this technology
and it's been around for years,
but mostly pre-cloud technology
that's been repurposed for the cloud,
and so Matillion has rebuilt that concept for the cloud,
and we launched that earlier this year,
and of course we've continued to build out
the core Matillion ETL platform
that today over a thousand
joint Snowflake Matillion customers use
including Western Union, of course.
We've been adding features there,
such as Universal Connectivity
and so a challenge that all data integration vendors have
is having the right connectors for their source systems.
Universal Connectivity allows you to connect
to any source system without writing code point and click.
We shipped that as well.
So it's been a busy year.
>> Exquisitely simple.
Sorry, I love that he said that
and it also sounded great with your accent.
I didn't want to point that out.
>> Thank you.
>> Yeah, it's excellent.
Harveer, talk about your role at Western Union
and what you've seen in terms of the evolution
of the data stack.
>> So in the last few years,
well, we knew a little bit of Western Union.
A 170 year old company,
pretty much everybody knows what Western Union is.
>> Dave: Right?
Driving an interesting synergy from what Matthew says,
when data moves, money moves.
That's what we do.
When he moves the data, we move the money.
That's the synergy between,
you know, us and the organization that support us
from data movement perspective.
So what I've seen in the last few years
is obviously a shift towards the cloud,
but, you know, within the cloud itself,
obviously there's a lot of players as well,
and we as customers have always been wishing
to have a short, smaller footprint of data
so that the movement becomes a little lesser.
You know, interestingly enough,
in this conference, I've heard some very interesting stuff
which kind of helping me to bring that footprint down
to a manageable number,
to be more governed,
to be more you know,
effective in terms of delivering more end results
for my customers as well.
So Matillion has been a great partner for us
from our cloud adoption perspective.
During the COVID times,
we are, you know, multi-channel organization.
We have retail stores as well, our digital presence,
but people just couldn't go to the retail stores,
so we had to find ways to accelerate our adoption,
make sure as our systems are scaling
and making sure that we are delivering
the same experience to our customers
and that's where, you know,
tools like Matillion came in
and really, really partnered up with us
to kind of bring it up to the level.
>> So talk specifically about the stack evolution,
'cause I have this sort of theory
that everybody talks about injecting data
and machine intelligence and AI
and machine learning into apps,
but the application development stack
is like totally separate from the the data analytics,
and the data pipeline stack,
and the database is somewhere over here as well.
How is that evolving?
Are those worlds coming together?
>> Some part of those worlds are coming together,
but where I still see the difference
is your heavy lifting will still happen on the data stack.
You cannot have that heavy lifting on the app
because if once the apps becomes heavy,
you'll have trouble communicating
with the organizations, you know.
You need to be as lean as possible in the front end
and make sure things are curated,
things are available on demand as soon as possible,
and that's why you see all these API driven applications
are doing really, really well
because they're delivering those results
back to leaner applications much faster.
So I'm a big proponent of,
yes, it can be hybrid
but majority of the heavy lifting
still needs to happen down at the data layer,
which is where I think Snowflake plays a really good role.
>> And APIs are the connective tissues.
>> API is the connective tissues, yes.
>> Also I think, you know, in terms of the data stack,
there's another parallel
that you can draw from applications, right?
So technology is when they're new,
we tend to do things in a granular way,
we write a lot of code,
we do a lot of sticking of things together
with plasters and sticky tape,
and it's the purview of high end engineers
and people enthusiastic about that, to get started.
Then the business starts to see the value in this stuff
and we need to move a lot faster
and technology solutions come in
and this is what the the data cloud is all about, right?
The technology getting out of the way
and allowing people to focus
on higher order problems of innovating around analytics,
data applications, AI, machine learning.
You know, that's also where Matillion sits
as well as other companies.
In this modern enterprise data stack,
is technology vendors are coming in
allowing organizations to move faster
and have higher levels of productivity.
So I think that's a good parallel
to application development.
>> And just follow up on that,
when you think about data prep
and you know, all of focus on data quality,
you've got a data team, you know,
in the data pipeline, a very specialized,
maybe even hyper specialized data engineers,
data quality engineers, data analysts, data scientists,
and they serve a lot of different business lines.
They don't have the business context typically,
so it's kind of this back and forth.
Do you see that changing in your organization
or are the lines of business taking more responsibility
for the data and addressing that problem?
>> It's like you die by thousand paper cuts,
so you just die, right?
That's the kind.
>> Yeah, right.
>> Because if I say it's good to be federated,
it comes with its own flaws,
but if I say, if it's good to be decentralized
then I'm the guy to choke, right?
And in my role, I'm the guy to choke.
So I've selectively tried
to be a pseudo federated organization,
where I do have folks reporting into our organization,
but they sit close to the line of business
because the business understands data better.
We are working with them hand in glove.
We have dedicated teams that support them
and our problem is we are also regional,
we are in 200 countries.
So the regional needs are very different than our U.S needs.
Majority of the organizations
that you probably end up talking to,
have like very U.S focused.
More than 50% of revenue is international.
So we are dealing with people who are international.
Their needs for data, their needs for quality
and their needs for the delivery of those analytics
and the data is completely different,
and so we have to be a little bit more closer
to the business than traditionally,
some organizations feel that they need to.
>> Is there need for the underlying infrastructure
and the operational details that as diverse
or is that something
that you bring standardization to the team?
>> So the best part about the cloud that happened to us,
is exactly that,
because at one point of time,
I had infrastructure in one country,
I had another infrastructure sitting in another country,
regional teams making different decisions
of bringing in different tools.
Now I can standardize.
I will say, Matillion is our standard for doing ETL work,
if this is the use case,
but then it gets deployed across the geographies
because the cloud helps us
or the cloud platform helps us to manage it,
sitting down here.
I have three centers around the world,
you know, Costa Rica, India, and the U.S.
I can manage 24/7 sitting here, no problem.
>> So the underlying infrastructure is global
but the data needs are dealt with locally?
>> Yep.
>> One of the part of the question,
I just think that Harveer
is super well positioned funds for you,
which is around that business domain knowledge
versus technical expertise,
'cause again, early in technology journeys
things tends to be very technical
and therefore only high end engineers can do it,
but high end engineers are scarce, right?
>> Dave: Right.
>> And also, I mean we survey
our hundreds of large enterprise customers
and they tell us they spend two thirds of their time
doing stuff they don't really want to do,
like reinventing the wheel, basic data movement
and low order stuff,
and so if you can make those people more productive
and allow them to focus on higher value problems
but also bring pseudo technical people into it,
overall, the business can go a lot faster,
and the way you do that is by making it easier.
That's why Matillion is a low-code/no-code platform
but Harveer and Western Union are doing this, right.
>> I can't compete with AWS and Google to hire people,
so I need to find people who are smart
to figure the products that we have to make them work.
I don't want them to spend time on infrastructure,
I don't want them to spend time
on trying to manage platforms.
I want them to deliver the data,
deliver the results to the business,
so that they can build and serve their customers better.
So it's a little bit of a different approach,
different mindset.
I used to be in consulting for 17 years,
I thought I knew it all.
(Lisa laughs)
But it changed overnight when I owned all of these systems,
and I'm like,
I need to be a little bit more smarter than this,
I need to be more proactive
and figure out what my business needs
rather than what just from what technology needs.
It's more what the business needs
and how I can deliver that needs to them.
So simple analogy, you know,
I can build the best architecture in the world.
It's going to cost me an arm and leg
but I can't drive it because the pipeline is not there.
So I can have a Ferrari, but I can't drive it.
It's still capped at 80 miles an hour.
So rather than building one Ferrari,
let me have 10 Toyotas or 10 Facettis,
which will go further along and do better for my customers.
>> So how do you see this all, we heard about the data cloud,
we heard about the marketplace, data products,
now application development inside the data cloud,
how do you see that affecting
not so much the productivity of the data teams,
I don't want to necessarily say that,
but the value that customers like you can get out of data?
>> So data is moving closer to the business.
That's the value I see,
because you are injecting the business
and you're injecting the application
much more closer to the data
because in the past, it was days and days of,
you know, churning the data to actually clear results.
Now the data has moved much closer,
so I have a much faster turnaround time.
The business can adapt and actually react much, much faster.
It took us like 16 to 30 days to deliver,
you know, data for marketing.
Now I can turn it down in four hours.
If I see something happening, I'll give you an example,
the war in Ukraine happened,
we had to shut down operations in Russia,
Ukraine is cash swamped, there's no cash in Ukraine.
We have cash.
We roll out campaign, $0 money, transferred to Ukraine
within four hours of the war going on.
That's the impact that we have.
>> Massive impact. >> It's a huge impact
>> Especially with such a macro challenge
going on in the world.
Thank you so much
for sharing the Matillion Snowflake partnership story,
how it's helping Western Union
really transform into a data company.
We love hearing stories of organizations
that are 170 years old,
that have always really been technology focused
but to see it come to life so quickly
is pretty powerful, guys.
Thank you so much for your time.
>> Thanks for having us
>> Thank you.
>> For Dave Vellante and our guests, I'm Lisa Martin.
You're watching theCUBE's live coverage
of Snowflake Summit '22 live from Las Vegas.
Stick around.
We'll be back after a short break.
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