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>> Okay, we're back with Justin Borgman,
CEO of Starburst,
Richard Jarvis is the CTO of EMIS Health,
and Teresa Tung is the cloud first technologist
from Accenture.
We're on to lie number three.
And that is the claim
that today's "Modern Data Stack"
is actually modern.
So (chuckles), I guess that's the lie.
Or, is that
it's not modern.
Justin, what do you say?
>> Yeah, I think new isn't modern. Right?
I think it's the new data stack.
It's the cloud data stack,
but that doesn't necessarily mean it's modern.
I think a lot of the components actually,
are exactly the same as what we've had for 40 years.
Rather than Teradata,
you have Snowflake.
Rather than Informatica,
you have Fivetran.
So, it's the same general stack,
just, y'know, a cloud version of it.
And I think a lot of the challenges
that have plagued us for 40 years still maintain.
>> So, let me come back to you Justin.
Okay, but there are differences, right?
You can scale.
You can throw resources at the problem.
You can separate compute from storage.
You really, there's a lot of money being thrown at that
by venture capitalists,
and Snowflake you mentioned, its competitors.
So that's different.
Is it not? Is that not at least an aspect
of modern dial it up, dial it down?
So what do you say to that?
>> Well, it is.
It's certainly taking, y'know
what the cloud offers
and taking advantage of that.
But it's important to note
that the cloud data warehouses out there
are really just separating their compute
from their storage.
So it's allowing them to scale up and down,
but your data's still stored in a proprietary format.
You're still locked in.
You still have to ingest the data
to get it even prepared for analysis.
So a lot of the same structural constraints
that exist with the old enterprise data warehouse model
on-preem still exist.
Just yes, a little bit more elastic now
because the cloud offers that.
>> So Teresa, let me go to you,
'cause you have cloud-first in your title.
So, what's say you to this conversation?
>> Well, even the cloud providers
are looking towards more of a cloud continuum, right?
So the centralized cloud as we know it,
maybe data lake, data warehouse in the central place,
that's not even how the cloud providers are looking at it.
They have use query services.
Every provider has one
that really expands those queries
to be beyond a single location.
And if we look at a lot of where our-
the future goes, right?
That's going to very much fall the same thing.
There was going to be more edge.
There's going to be more on-premise,
because of data sovereignty, data gravity,
because you're working with different parts of the business
that have already made major cloud investments
in different cloud providers, right?
So, there's a lot of reasons why the modern,
I guess, the next modern generation
of the data stack needs to be much more federated.
>> Okay, so Richard, how do you deal with this?
You've obviously got, you know,
the technical debt, the existing infrastructure,
it's on the books.
You don't want to just throw it out.
A lot of conversation about modernizing applications,
which a lot of times is, you know,
of microservices layer
on top of legacy apps.
How do you think about the Modern Data Stack?
>> Well, I think probably the first thing to say is
that the stack really has to include the processes
and people around the data as well is all well
and good changing the technology.
But if you don't modernize how people use that technology,
then you're not going to be able to, to scale
because just 'cause you can scale CPU and storage
doesn't mean you can get more people to use your data
to generate you more value for the business.
And so what we've been looking at is really changing
in very much aligned to data products and, and data mesh.
How do you enable more people to consume the service
and have the stack respond in a way that keeps costs low?
Because that's important
for our customers consuming this data
but also allows people to occasionally run enormous queries
and then tick along with smaller ones when required.
And it's a good job we did because
during COVID all of a sudden we had enormous pressures
on our data platform to answer really
important life threatening queries.
And if we couldn't scale both our data stack and our teams
we wouldn't have been able to answer those as quickly
as we had.
So I think the stack needs to support a scalable business
not just the technology itself.
>> Well thank you for that.
So Justin let's, let's try to break down what
the critical aspects are of the modern data stack.
So you think about the past, you know, five
seven years cloud obviously has given a different pricing
model. Derisked experimentation,
you know that we talked about the ability
to scale up scale down,
but it's,
I'm taking away that that's not enough.
Based on what Richard just said,
the modern data stack has to serve the business
and enable the business to build data products.
I buy that.
I'm you a big fan of the data mesh concepts,
even though we're early days.
So what are the critical aspects if you had to think about
you know, the, maybe putting some guardrails and definitions
around the modern data stack, what does that look like?
What are some
of the attributes and, and principles there
>> Of how it should look like or, or how
>> Yeah. What it should be?
>> Yeah. Yeah.
Well, I think, you know, in, in Theresa mentioned this in
in a previous segment about the data warehouse
is not necessarily going to disappear.
It just becomes one node,
one element of the overall data mesh.
And I certainly agree with that.
So by no means, are we suggesting that, you know
Snowflake or what Redshift or whatever cloud data
warehouse you may be using is going to disappear,
but it's it's not going to become the end all be all.
It's not the, the central single source of truth.
And I think that's the paradigm shift that needs to occur.
And I think it's also worth noting
that those who were the early adopters
of the modern data stack were primarily digital, native born
in the cloud young companies who had the benefit of
of idealism.
They had the benefit
of starting with a clean slate that does not
reflect the vast majority of enterprises.
And even those companies, as they grow up,
mature out of that ideal state, they go by a business.
Now they've got something
on another cloud provider that has a different data stack
and they have to deal
with that heterogeneity that is just change
and change is a part of life.
And so I think there is an element here
that is almost philosophical.
It's like, do you believe
in an absolute ideal where I can just fit everything
into one place or do I believe in reality?
And I think the far more pragmatic approach is
really what data mesh represents.
So to answer your question directly, I think it's adding
you know, the ability to access data that lives outside
of the data warehouse, maybe living in open data formats
in a data lake or accessing operational systems as well.
Maybe you want to directly access data that lives
in an Oracle database
or a Mongo database or, or what have you.
So creating that flexibility to really future proof yourself
from the inevitable change that you will
you won't encounter over time.
>> So thank you.
So Theresa, based on what Justin just said, I
I might take away there is it's inclusive
whether it's a data mart, data hub,
data lake, data warehouse, just a node on the mesh.
Okay. I get that.
Does that include Theresa on, on Preem data?
Obviously it has to.
What are you seeing
in terms of the ability to,
to take that data mesh concept
on Preem I mean most implementations I've seen
and data mesh, frankly really aren't, you know
adhering to the philosophy there.
Maybe,
maybe it's data lake and maybe it's using glue.
You look at what JPMC is doing, HelloFresh,
a lot of stuff happening on the AWS cloud
in that, you know, closed stack, if you will.
What's the answer to that Theresa?
>> I mean,
I think it's a killer case for data mesh.
The fact that you have valuable data sources on Preem,
and then yet you still want to modernize and take the best
of cloud.
Cloud is still, like we mentioned,
there's a lot of great reasons for it
around the economics and the way ability to tap
into the innovation that the cloud providers are giving
around data and AI architecture.
It's an easy button.
So the mesh allows you to have the best of both world.
You can start using the data products on Preem,
or in the existing systems that are working already.
It's meaningful for the business.
At the same time,
you can modernize the ones that make business sense
because it needs better performance.
It needs, you know, something that is, is cheaper or
or maybe just tapping
into better analytics to get better insights, right?
So you're going to be able to stretch
and really have the best of both worlds.
That, again, going back to Richard's point,
that is meaningful by the business.
Not everything has to have that one size fits all
set a tool.
>> Okay. Thank you.
So Richard, you know, talking about data as product
wonder if we could give us your perspectives here
what are the advantages of treating data as a product?
What, what role do data products have
in the modern data stack?
We talk about monetizing data.
What are your thoughts on data products?
>> So for us,
one of the most important data products that we've
been creating is taking data that is healthcare data
across a wide variety of different settings.
So information about patients, demographics about their
their treatment, about their medications and so on,
and taking that into a standards format that can be utilized
by a wide variety
of different researchers
because misinterpreting that data
or having the data not presented
in the way that the user is expecting means
that you generate the wrong insight and in any business
that's clearly not a desirable outcome
but when that insight is so critical
as it might be in healthcare or some security settings
you really have to have gone
to the trouble of understanding the data, presenting it
in a format that everyone can clearly agree on.
And then letting people consume in a very structured
managed way, even if that data comes from a variety
of different sources
in the first place.
And so our data product journey has really begun
by standardizing data across a number
of different silos through the data mesh.
So we can present out both internally and
through the right governance externally to,
to researchers.
>> So that data product through whatever APIs is
is accessible, it's discoverable, but it's obviously
got to be governed as well.
You mentioned appropriately provided to internally.
>> Yeah. >> But also, you know,
external folks as well.
So the, so you've, you've architected that capability today?
>> We have
and because the data is standard
it can generate value much more quickly and we can be sure
of the security
and value that that's providing,
because the data product isn't just about formatting
the data into the correct tables,
it's understanding what it
means to redact the data or to remove certain rows from it
or to interpret what a date actually means.
Is it the start of the contract or the start
of the treatment or the date of birth of a patient?
These things can be lost in the data storage
without having the proper product management
around the data to say in a very clear business context
what does this data mean,
and what does it mean to process this data
for a particular use case.
>> Yeah, it makes sense.
It's got the context.
If the, if the domains on the data, you know
you got to cut through a lot of the,
the centralized teams, the technical teams that
that data agnostic, they don't really have that context.
All right, let's end.
Justin.
How does Starburst fit into this modern data stack?
Bring us home.
>> Yeah. So I think for us
it's really providing our customers with, you know
the flexibility to operate and analyze data that lives
in a wide variety of different systems.
Ultimately giving them that optionality, you know
and optionality provides the ability to reduce costs
store more in a data lake rather than data warehouse.
It provides the ability for the fastest time to
insight to access the data directly where it lives.
And ultimately with this concept
of data products that we've now, you know
incorporated into our offering as well
you can really create and, and curate, you know
data as a product to be shared and consumed.
So we're trying to help enable the data mesh, you know
model
and make that an appropriate compliment to
you know,
the modern data stack that people have today.
>> Excellent. Hey, I want to thank Justin, Teresa,
and Richard for joining us today.
You guys are great.
Big believers in the
in the data mesh concept, and I think, you know
we're seeing the future of data architecture.
So thank you.
Now, remember,
all these conversations are going to be available
on the cube.net for on demand viewing.
You can also go to starburst.io.
They have some great content on the website and
they host some really thought provoking interviews
and they have awesome resources.
Lots of data mesh conversations over there
and really good stuff in,
in the resource section.
So check that out.
Thanks for watching the "Data Doesn't Lie...
or Does It?" made possible by Starburst data.
This is Dave Vellante for the Cube,
and we'll see you next time.
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