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>> Good morning, everyone.
Welcome back to theCUBE's coverage of Snowflake Summit '22
live from Caesars Forum in Las Vegas.
Lisa Martin here with Dave Vellante.
This is day three of our coverage.
We've had an amazing, amazing time.
Great conversations, talking with Snowflake executives,
partners, customers.
We're going to be digging into data mesh with data.world.
Please welcome Jon Loyens, the chief product officer.
Great to have you on the program, Jon.
>> Thank you so much for having me here.
I mean, the summit, like you said, has been incredible.
So many great people, such a good time,
really nice to be back in person with folks.
>> It is fabulous to be back in person.
And the fact that we're on day four, for them,
and this is, the solution showcase is as packed as it is
at 11 in the morning is saying something.
>> Jon: Yeah, usually- >> People are chomping
at the bit to hear what they're doing and innovate.
>> Absolutely, usually those last days of conferences,
everybody starts getting a little tired,
but we're not seeing that at all here.
>> Especially in Vegas, this is impressive.
Talk to the audience a little bit about data.world.
What do you guys do,
and talk about the Snowflake relationship.
>> Absolutely, data.world is
the only true cloud-native enterprise data catalog.
We've been an incredible Snowflake partner,
and Snowflake's been an incredible partner to us,
really since 2018, when we became the first data catalog
in the Snowflake Partner Connect experience.
You know, Snowflake and the data cloud make it so possible,
and it's changed so much in terms of being able to,
you know, very easily transition data into the cloud,
to break down those silos, and to have a platform
that enables folks to be incredibly agile
with data from an engineering and infrastructure standpoint.
data.world is able to provide a layer of discovery
and governance that matches that agility,
and the ability for a lot of different stakeholders
to really participate in the process of data management
and data governance.
>> So, data mesh, basically, Zhamak Dehghani lays out the,
first of all, the the fault domains of existing data
and big data initiatives,
and she boils it down to the fact
that it's just this monolithic architecture
with hyper-specialized teams that you have to go through,
and it just slows everything down, and it doesn't scale,
they don't have domain context,
so she came up with four principles, if I may.
>> Jon: Yep.
>> Domain ownership, so push it out to the businesses,
they have the context, they should own the data.
The second is data as product.
We're certainly hearing a lot about that today, this week.
The third is that...
So that, sounds good, push out the data, great,
but it creates two problems.
Self-serve infrastructure.
Okay, but premises infrastructure should be
an operational detail.
And then the fourth is computational governance.
>> Mm-hmm.
>> So you talked about data catalog.
Where do you fit in those four principles?
>> You know, honestly, we are able to help teams
realize the data mesh architecture.
And we know that data mesh is really,
it's both a process and a culture change.
But then when you want to enact a process
and a culture change like this,
you also need to select the appropriate tools to match
the culture that you're trying to build, the process
and the architecture that you're trying to build.
And the data.world data catalog can really help
along all four of those axes.
When you start thinking first
about let's say, you know, let's take the first one,
you know, data as a product, right?
We even, very meta of us from a metadata management platform
at the end of the day, but very meta of us
when you talk about data as a product.
We track adoption and usage of all your data assets
within your organization, and provide program teams,
and you know, offices of the CDO
with incredible event analytics, very detailed,
that gives them the right audit trail that enables them
to direct very scarce data engineering,
data architecture resources, to make sure that
their data assets are getting adopted and used properly.
On the domain driven side,
we are entirely knowledge graph and open standards based,
enabling those different domains.
We have, you know, incredible joint Snowflake customers
like Prologis, and we chatted a lot about this
in our session here yesterday,
where because of our knowledge graph underpinnings,
because of the flexibility of our metadata model,
it enables those domains to actually model their assets
uniquely from from group to group, without having to
relaunch or run different environments.
Like, you can do that all within one data catalog platform
without having to have separate environments
for each of those domains.
Federated governance, again,
the amount of like, data exhaust that we create,
that really enables ambient governance
and participatory governance as well.
We call it agile data governance.
Really, the adoption of agile and open principles applied
to governance to make it more inclusive and transparent.
And we provide that in a way that confederate
across those domains and make it consistent.
>> Okay, so you facilitate
across that whole spectrum of principles.
And so, in the early examples of data mesh that I've studied
and actually collaborated with, like with JPMC,
who I don't think, was not using your data catalog,
but HelloFresh, who may or may not be, but I mean,
there are numbers, and I want to get to that.
But what they've done is they've enabled the domains
to spin up their own, whatever, data lakes, data warehouses,
data hubs, at least in concept.
Most of them are data lakes on AWS,
but still, in concept, they want to be inclusive.
And they've created a master data catalog,
and then each domain has its sub-catalog
which feeds into the master.
And that's how they get consistency
and governance and everything else.
Is that the right way to think about it,
or do you have a different spin on that?
>> Yeah, I, you know, I have a slightly different spin on it.
I think organizationally it's the right way
to think about it.
And in absence of a catalog that can truly have
multiple federated metadata models,
multiple graphs in one platform,
I mean, that is really kind of the only way to do it.
With data.world, you don't have to do that.
You can have one platform, one environment,
one instance of data.world that spans all of your domains,
enable them to operate independently,
and then federate across them.
>> So you just answered my question
as to why I should use data.world versus Amazon Glue.
>> Oh, absolutely.
>> I mean, that's awesome that you've done that.
How have you done that? What's your secret sauce?
>> The secret sauce there is really,
and all credit to our CTO, one of my closest friends,
who was a true student of knowledge graph practices
and principles, and really felt that the right way to manage
metadata and knowledge about the data analytics ecosystem
that companies were building was through
federated linked data, right?
So we use standards, and we've built
an open and extensible metadata model
that we call cost KOS, that really
takes the best parts of existing open standards
in the semantics space, things like Schema.org,
DCAT, Dublin Core, brings them together,
and models out the most typical enterprise data assets,
providing you with an ontology that's ready to go.
But because of the graph nature of what we do,
it's instantly accessible without having to
rebuild environments, without having to do
a lot of management against it.
It's really quite something, and it's something
all of our customers are are very impressed with,
and, you know, are getting a lot of leverage out of.
>> And we have a lot of time today,
so we're not going to shortchange this topic.
So one last question, then I'll shut up, Lisa,
and let you jump in.
This is an open standard. It's not open source.
>> No, it's built on open standards.
We also fundamentally believe in extensibility and openness.
We do not want to vertically like, lock you into our platform.
So everything that we have is API-driven, API-available.
Your metadata belongs to you.
If you need to export your graph, you know,
instantly available in open machine readable formats.
That's really, we come from the open data community.
That was a lot of the founding of data.world.
We worked a lot with the open data community,
and we fundamentally believe in that.
And that's enabled a lot of our customers as well
to truly take data.world and not have it be
a data catalog application,
but really an entire metadata management platform,
and extend it even further into their enterprise,
to really catalog all of their assets,
but also to build incredible integrations
to things like corporate search.
You know, having data assets show up
in corporate wiki search along with
all the descriptive metadata that people need has been
incredibly powerful, and an incredible extension
of our platform, that I'm so happy to see our customers-
>> So it's not exclusive to Snowflake,
it's not exclusive to AWS,
you can bring it anywhere, >> Yup.
>> Azure, PCP, anytime.
>> Yeah, you know, where we are, where we love Snowflake,
and look, we're at the Snowflake Summit.
And we've always had a great relationship
with Snowflake though, and really leaned in there
because we really believe Snowflake's principles,
particularly around cloud and being cloud native,
and the operating advantages that it affords companies,
that's really aligned with what we do.
And so Snowflake was really the first
of the cloud data catalogs that we ultimately,
or sorry, first cloud data warehouses
that we integrated with, and to see them transition
to building really out the data cloud has been awesome.
>> Talk about how data.world and Snowflake enable companies
like Prologis to be data companies.
These days, every company has to be a data company,
but they have to be able to do so quickly
to be competitive and to really win.
How do you help them, if we like up level the conversation
to really impacting the overall business?
>> Mm-hmm, that's a great question, and especially right now,
everybody knows, and Prologis is a great example.
They're a logistics and supply chain company
at the end of the day, and we know how important
logistics and supply chain is nowadays.
And for them and for a lot of our customers,
I think one of the advantages of having a data catalog is
the ability to build trust, transparency, and inclusivity
into their data analytics practice.
By adopting agile principles, by adopting a data mesh,
you're able to extend your data analytics practice
to a much broader set of stakeholders, and to involve them
in the process while the work is getting done.
One of the greatest things about agile software development
when it became a thing in the early 2000s was
how inclusive it was, and that inclusivity led to
a much faster ROI on software projects.
And we see the same thing happening in data analytics.
People, you know, we have amazing data scientists
and data analysts coming up with these insights
that could be business changing,
that could make their company significantly more resilient,
especially in the face of economic uncertainty.
But if you have to sit there
and argue with your business stakeholders
about the validity of the data,
about the techniques that were used to do the analysis,
and it takes you three months to get people to trust
what you've done, that opportunity's passed.
So how do we shorten those cycles?
How do we bring them closer?
And that's really a huge benefit that,
like Prologis has realized, just tightening that cycle time,
building trust, building inclusion,
and making sure ultimately, humans learn by doing,
and if you can be inclusive, it even increases things
like, that we all want to help,
'cause Lord knows the world needs it,
things like data literacy.
>> Yeah.
>> Right?
>> So data.world can inform me
as to where on the spectrum of data quality
my data set lives, so I can say, okay, this is usable,
shareable, you know, >> Exactly.
>> Gold standard, versus, fix this.
>> Jon: Right. >> Okay.
>> Yep. Yeah.
>> Okay, and you could do that with one data catalog,
not a bunch of...
>> Yeah.
And trust is really a multi-faceted
and multi-angle idea, right?
It's not just necessarily data quality
or data observability, we have incredible partnerships
in that space, like our partnership with Monte Carlo,
where we can ingest all their like,
amazing observability information,
and display that in a really, like,
a really consumable way in our data catalog.
But it also includes things like the lineage,
who touched it, who was involved in the process of it?
Can I get a question answered quickly about this data?
What's it been used for previously,
and do I understand that?
It's so multifaceted that you have to be able to really
model and present that in a way that's unique
to any given organization, even unique within domains
within a single organization.
>> So you're not, I didn't mean to suggest you're
a data quality supplier, >> Oh no, absolutely.
>> But you partner with them,
and then you become the master catalog.
>> Right, exactly. >> It's brilliant, I love it.
And you just raised your series C, 15 million?
>> We did, yeah.
You know, really lucky to have incredible investors
like Goldman Sachs, who led our series C.
It really, I think, communicates the trust that they have
in our vision, and what we're doing,
and the impact that we can have on organization's ability
to be agile and resilient around data analytics.
>> Enabling customers to have that single source of truth is
so critical, you talked about trust,
that is, >> Absolutely.
>> It's no joke. >> Absolutely.
>> That is critical, and there's a tremendous amount
of business impact, positive business impact,
that can come from that.
What are some of the things that are next for data.world
that we're going to see?
>> Oh, ah.
You know, I love this,
we have such an incredibly innovative team
that's so dedicated to this space
and the mission of what we're doing.
We're out there trying to fundamentally change
how people get data analytics work done together.
One of the big reasons I founded the company was
I really truly believe that data analytics needs to be
a team sport, it needs to go from, you know,
single player mode to team mode.
And everything that we've worked on
in the last six years has leaned into that.
Our architecture being cloud native, we do, we've done
over a thousand releases a year, that nobody has to manage,
you don't have to worry about upgrading your environment.
It's a lot of the same story that's made Snowflake so great.
We are really excited to have announced in March
at our own summit, and we're rolling this suite of features
out over the course of the year,
a new package of features that we call data.world Eureka,
which is a suite of automations, and you know,
knowledge driven functionality that really helps you
leverage a knowledge graph to make decisions faster,
and to operationalize your data in the data ops way
with significantly less effort.
>> Big, big impact there.
Jon, thank you so much for joining Dave and me,
unpacking what data.world is doing with the data mesh,
the opportunities that you're giving
to customers in every industry.
We appreciate your time,
and congratulations on the news and the funding.
>> Yeah, thank you.
It's been a true pleasure, thank you for having me on.
And I hope you guys enjoy the rest of the day
and your other guests that you have.
>> Thank you, we will.
All right, for our guest and Dave Vellante, I'm Lisa Martin.
You're watching theCUBE's third day of coverage
of Snowflake Summit '22, live from Vegas.
Dave and I will be right back with our next guest,
so stick around.
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