>> Hey everyone.
Welcome back to theCUBE.
Lisa Martin, with Dave Vellante.
We're here in Las Vegas with Snowflake at the Snowflake
Summit 22. This is the fourth annual.
There's close to 10,000 people here.
Lots going on.
Customers, partners, analysts, press media, everyone talking
about all of this news, we've got a couple
of guests joining us.
We're going to unpack Snowpark.
Torsten Grabs, the Director of Product Management
at Snowflake and Joe Nolte,
AI and MDM architect at Al legist Group.
Guys, welcome to the program.
>> Thank you.
>> Thank you so much for having us.
>> Isn't it great to be back in person?
>> It is.
>> Oh, wonder, yes!
>> Yes.
Joe, talk to us a little bit about Allegis group.
What do you do?
And then tell us a little bit about your role specifically.
>> Well, Allegis group is a collection of operating
companies that do staffing.
We're one of the biggest staffing companies
in North America.
We have a presence in AMEA and in the APAC region.
So we work to find people jobs, and we help get 'em staffed
and we help companies find people
and we help individuals find people.
>> Incredibly important these days
>> Excuse me?
>> Incredibly important these days.
>> It is. It very is. Very important.
>> Tell me a little bit about your role.
You are the AI and MDM architect.
You wear a lot of hats.
Okay, so I'm a architect
and I support both of those verticals within the company.
So I work, I have a set of engineers
and data scientists that work with me on the AI side
and we build data science models and solutions
that help support what the company wants to do. Right?
So we build it to make business processes faster,
and more streamlined, and we really see Snowpark
and Python helping us to accelerate that
and accelerate that delivery.
So we, we're very excited about it.
>> Explain Snowpark for, for people.
I mean, I look at it as this, this wonderful sandbox.
You can bring your own developer tools in,
but, but explain in your words what it is.
>> Yeah, so, we got interested in
in Snowpark because increasingly the feedback was
that everybody wants to interact with Snowflake though
sequeal with other languages that they would prefer to use
including Java, Scala, and of course, Python, right?
So then this led down to the, our work into Snowpark
where we're building an infrastructure
that allows us to host other languages natively
on the Snowflake compute platform.
And now here, what we just announced is Snowpark
for Python in public preview.
So now you have the ability to natively run Python code
on Snowflake, and benefit from the thousands
of packages and libraries that the open source community
around Python has contributed over the years.
And that's a huge benefit for data scientists,
>> It is
>> ML practitioners and data engineers because those are the
languages and packages that are popular with them.
So yeah, we very much look forward to working
with the likes of you and other data scientists and
and data engineers around the Python ecosystem.
>> Yeah, and Snowpark helps reduce the
architectural footprint.
It makes the data pipelines a little easier
and less complex.
We had a pipeline and it works on DMB data.
And we converted that entire pipeline from Python running
on a VM to directly running down on Snowflake.
Right. We were able to eliminate code,
because you don't have to worry about multi-threading.
Right? Because we can just set the warehouse size
through a task.
No more multi-threading. Throw that code away.
Don't need to do it anymore.
Right? We get the same results, but the architecture to
run that pipeline gets immensely easier,
because it's a store procedure that's already there,
and implementing that calling
to that store procedure is very easy.
The architecture that we use today uses six
different components just to be able to run
that Python code on a VM within our ecosystem to make sure
that it runs on time and scheduled and all of that.
Right? But with Snowflake, and Snowpark,
and Snowflake Python, it's two components.
It's the store procedure and our ETL tool calling it.
>> Okay. So you've simplified that, that stack,
>> Yes.
>> and eliminated all the other stuff
that you had to do that now Snowflake's doing.
Am I correct that you're actually taking the application
development stack and the analytics stack
and bringing them together?
Are they merging?
>> I don't know.
I think in a way. I'm not real sure how I would
answer that question to be quite honest.
I think with Stream Lit, there's a little bit
of application that's going to be down there.
So you could maybe start to say that.
I'd have to see how that carries out and what we do,
and what we produce to really give you an answer to that.
But yeah, maybe in a little bit.
>> Well, the reason I asked you is
because you talk, we always talk about injecting data
into apps, injecting machine intelligence and ML and AI
into apps, but there are two separate stacks
today, aren't they?
>> Certainly the two are getting closer to Python.
>> Python gets a little better.
>> So explain that. Explain how that...
>> Just like in the keynote, right?
The other day with Chre
when she showed her sample application, you can start to see
that 'cause you can do some data pipe lining,
and data building, and then throw that into
a training module within Python
right down inside of Snowflake, and have it sitting there,
and then you can use something like Stream Lit to
expose it to your users. Right?
We were talking about that the other day.
About how do you get an ML
in AI after you have it running in front of people?
We have a model right now that is a predictive
and prescriptive model of one of our top KPIs.
Right? And right now we can show it to everybody
in the company, but it's through a Jupiter notebook.
How do I deliver it?
How do I get it in the front of people
so they can use it?
Well, with what we saw was Stream Lit. Right?
It's a perfect match, and then we can compile it;
It's right down there on Snowflake,
and it's completely easier time to delivery
to production, because since it's already part of Snowflake
there's no architectural review. Right?
As long as the code passes code review,
and it's not poorly written code,
and isn't using a library that's dangerous. Right?
It's a simple deployment to production.
So because it's encapsulated inside
of that Snowflake environment,
we have approval to just use it however we see fit.
>> That code review has to occur irrespective of, you know
whatever you're running it on.
Okay. So I get that,
but it's a frictionless environment you're saying, right?
What would you have had to do prior to Snowflake
that you don't have to do now?
>> Well, one, it's a longer review process to allow me to
push the solution into production. Right?
Because I have to explain to my InfoSec people,
right? My other...
>> Not trusted?
>> Well, well don't use that word.
>> No?
>> There are checks and balances in everything that we do.
>> It has to be verified and...
>> Yeah. That's all it it's part of the, the
what I like to call the good bureaucracy. Right?
Those processes are in place
to help all of us stay protected.
>> It's the checklist?
>> Yeah. That's all it is.
>> It's like flying a plane...
>> But that checklist gets smaller.
And sometimes it's just one box now with Python
through Snowpark, running down on the Snowflake platform.
And that's, that's the real advantage,
because we can do things faster.
Right? We can do things easier. Right?
We're doing some mathematical data science right now,
and we're doing it through Sequel, but Python will open that
up much easier and allow us to deliver faster,
and more accurate results, and easier.
Not to mention we're going to try to bolt
on the hybrid tables to that afterwards.
>> Oh, we're to talk about that.
So can you, and I don't, I don't need an exact metric,
but when you say faster?
Talking 10% faster, 20% faster, 50% faster?
>> It really depends on the solution.
>> Well, gimme a range of, of the worst case, best case.
>> I, I really don't have that.
I don't, I wish I did.
I wish I had that for you, but I don't have it for you.
>> I mean, obviously it's meaningful.
>> It is meaningful.
>> Has a business impact.
>> I think what it will do, is it will speed
up our work inside of our iterations,
so we can then, you know, look at the code sooner.
Right? And evaluate it sooner,
measure it sooner, measure it faster.
>> So is it fair to say that as a result, you can do more?
>> Yeah. That's fair to say.
We'll be able do more.
Well, and, it will enable more of our people,
because they're used to working in Python.
>> Can you talk a little bit about,
from an enablement perspective, let's go
up the stack to the folks at Allegis who are
on the front lines helping people get jobs.
What are some of the benefits that having Snowpark
for Python under the hood.
How does that facilitate them being able to get access
to data to deliver what they need to, to their clients?
>> Well, I think what we would use Snowflake for a Python
for there is when we're building them tools to let them know
whether or not a user or a piece of talent
is already within our system. Right? Things like that.
Right? That's how we would leverage that.
But again, it's also new. We're still figuring
out what solutions we would move to Python.
We are, we have some targeted, like we're..
I have developers that are waiting for this and they're,
and they're in private preview now,
they're playing around with it.
They're ready to start using it.
They're ready to start doing some analytical work on it
to get some of our analytical work out of, out of GCP.
Right? Because that's where it is right now.
Right? But all the data's in Snowflake,
but we need to move that down now and take the data out.
The data wasn't in Snowflake before,
so the dashboards are up in GCP,
but now that we've moved all of that data down in to
Snowflake, the team that did those analytical dashboards,
they want to use Python,
because that's the way it's written right now.
So it's an easier transformation,
an easier migration off of GCP, and get us into Snow.
Doing everything in Snowflake. Which is what we want.
>> So you're saying you're doing the visualization in GCP.
Is that right?
>> It's just some dash boarding. That's all.
>> Not even visualization? You won't even give me that? Okay.
>> Cause it's not visualize. It's just some dash boardings
of numbers and percentages and things like that.
It's no graphic to it.
>> And it doesn't make sense to run that in Snowflake?
In GCP? You could just move it into AWS or, or...?
>> No. Well what we'll be able to do now is
all that data before was in GCP,
and all that Python code was running in GCP.
We've moved all that data out of GCP,
and now it's in Snowflake, and now we're going to work
on taking those Python scripts that we thought we
were going to have to rewrite differently.
Right? Because Python, wasn't available.
Now that Python's available, we have an easier way
of getting those dashboards back out to our people.
>> Okay. But you're taking it out of GCP,
putting it to Snowflake. Where? Anywhere?
>> Well, so we'll build those dashboards,
and they'll actually be... they'll be displayed
through Tableau. Which is our enterprise tool for that.
>> Yeah. Sure. Okay.
And then when you operationalize it it'll go...
>> But the idea is it's an easier pathway for us to
migrate our code, our existing code that's in Python, down
into Snowflake. Have it run against snowflake.
Right? Because all the data's there.
>> Because it's not a going out and coming back in,
it's all integrated.
>> We want our people working on the data in Snowflake.
We want... that's our data platform.
That's where we want our analytics done.
Right? We don't want them done in other places.
Want to get all that data down.
And we've, over our data cloud journey,
we've worked really hard to move all
of that data that we use out of existing systems on Prem,
and now we're attacking the data that's in GCP,
and making sure and it's down.
And it's not a lot of data.
And we, we fixed it with one data pipeline
Exposes all that data down in Snowflake now.
And we're just migrating our code down to work
against the Snowflake platform. Which is what we want.
>> Why are you excited about hybrid tables?
What's that potential?
Hybrid tables I'm excited about because we...
So some of the data science that we do inside
of Snowflake produces a set of results,
and their recommendations.
Well, we have to get those recommendations back
to our people. Back into our talent management system.
And there's just some delays.
There's about an hour delay of delivering that data
back to that team.
Well, with hybrid tables,
I can just write it to the hybrid table,
and that hybrid table can be directly accessed
from our talent management system, be
for the recruiters, and for the hiring managers
to be able to see those recommendations in near real time.
And that's the value.
>> Yep. We learned that access to real time data
in recent years is no longer a nice to have.
It's like a huge competitive differentiator
for every industry, including yours.
Guys, Thank you for joining David and me on the program
talking about Snowpark for Python,
what that announcement means,
how Allegis is leveraging the technology.
We look forward to hearing what comes when it's GA.
>> Yeah.
>> We're looking forward to it.
>> Alright guys thank you.
For our guests and Dave Vellante, I am Lisa Martin.
You're watching theCUBE's coverage of
Snowflake Summit 22.
Stick around. We'll be right back with our next guest.