(intro music)
>> Welcome back to theCUBE's continuing coverage
of day one of the Snowflake Summit 22,
live from Caesar's Forum in Las Vegas.
I'm Lisa Martin. My co-host for the week is Dave Vellante.
Dave and I are pleased to welcome
Nagaraj Sastry to the program,
the vice president of data and analytics
at HCL technologies.
Welcome, great to have you.
>> Same here, thank you for inviting me here.
>> Isn't it great to be back in person?
>> Oh, love it!
>> The keynote this morning,
I don't know if you had a chance to see it,
standing room only, there was overflow rooms.
People are ready for this
and it was a jam packed morning of announcements.
>> Nagaraj: Absolutely.
>> Talk to us a little bit
about the HCL Snowflake partnership.
Anybody in the audience who may not be familiar with HCL,
give us a little bit of a background,
vision, mission differentiation,
and then that Snowflake duo.
>> Sure, sure.
So, let me first start off with talking about HCL.
We are 11.5 billion dollar organization.
We have three modes of working.
Mode one is everything to do
with our infrastructure business
and application services and maintenance.
Mode two is anything that we do
in the cutting edge ecosystem, whether it is cloud,
whether it is application modernization,
ERPs, SaaS, all of those put together is mode two.
Data analytics is part of our mode two culture.
The whole ecosystem is called Digital Services Business.
And within digital services,
one of the arms is data and analytics.
We are about a billion dollars in terms of revenues
from a data and analytics perspective,
of the 11 billion that I was talking to you about.
And mode three is everything to do
with our software services.
So we have got our own software products
and that's a third of our business.
So, that's about HCL.
So, HCL and Snowflake relationship,
we are a elite partner with Snowflake.
We are one of the fastest growing partners.
We achieved the elite level
within 18 months of us signing up
as a Snowflake partner.
We're close to about 50 plus implementations worldwide,
and about 800 people who are Snowflake professionals
within that HCL ecosystem, large customers that we serve.
>> Lisa: And how long have you been partners?
>> About 18 to 20 months now.
>> Okay.
During the last couple of tumultuous years,
why Snowflake, what was it about their vision,
their strategy, their leadership
that really spoke to HCL as this is a partner for us?
>> So, one of the biggest things that we realized
probably about four years ago was in terms of
you had all the application databases or RDBMSs, MPPs
the Hadoop ecosystems, which were getting expensive,
not in terms of the cost
but in terms of the processing times,
the way the queries were getting created.
And we knew that there is something that is going to come.
>> Dave: And the people. >> And the people.
And we knew that, there will be a hyperscaler
that will come. And of course, Azure was already there.
AWS was there, Google was just picking it up.
And at that point in time, we realized that
there will be a cloud data warehouse
because we had started reading about Snowflake
at that point in time.
So, fast forward a couple of years after that,
and we realized that if we are to be in this business,
the right way of doing it
is by A, partnering with the right tooling company.
And Snowflake brings that to table. We all know that now.
And with what the keynote speakers were also saying, right?
From 150 member team about five years ago in conference
to about 12,000 people now.
So you know that this is the right thing to do
and this is the right place to be at.
So, we devised a methodology in terms of saying that
let's get into the partnership,
let's get our resources trained and certified
on the Snowflake ecosystem,
and let's take a point of view to our customers
in terms of how data migrations and transformations
have to be done in the Snowflake arena.
>> When you think about your modes,
you talked about modes one, two, and three.
I feel like Snowflake touches on each of those,
maybe not so much on the infrastructure and the apps,
but although maybe going forward,
>> Nagaraj: It does. >> Increasingly.
So yeah, my question is,
where do you see Snowflake vectoring into your modes?
>> So it does in both,
in the first two modes and mode three also,
because and I'll give you the reasons why.
Mode one is predominantly
because you can do application development on cloud.
>> Yep. >> On the data cloud now
which basically means that I can have a COTS application
run on Snowflake, eventually. That's the goal.
Second is, in mode two,
because it is a cloud data warehouse, it fits in exactly.
Because the application data is in Snowflake,
I've got my regular data sets within Snowflake,
both are talking to each other,
there is zero lapse time from a user perspective.
>> Yeah, it's a direct tip.
>> And in mode three, the reason why I said mode three
was because software as a service
or software services and products
is because I can powered by Snowflake.
I can implement that.
So that's why, it cuts across our entire ecosystem.
>> The whole thing is called your digital business, correct?
>> Yes. >> Dave: Is that right?
So, this is the next wave of digital business
that we're seeing here. >> Yes.
>> Digital is data.
That's really what it's about.
It's about putting that data to work.
>> So the president of our digital business, Anand Birje
who had done a session in the afternoon today,
he says the D in the digital is data.
>> Dave: He is right.
>> And that's what we are seeing with our customers,
large implementations that we do, in this ecosystem.
There is one other thing
that we are focusing very heavily on
is industrial solutions or industry led solutions.
Like whether it is for healthcare,
whether it's for retail or financial services,
name a vertical and we have got our own capabilities
around industrialized solutions that fit certain use cases.
>> So in thinking about the D in digital is really data.
If you think about the operating model for data,
it's obviously evolved,
you mentioned, Hadoop went to the cloud
and all the data went to the cloud.
But today, you've got an application development model,
you got database which is sort of hardened,
and then you've got your data pipeline and your data stack,
and that's kind of the operating model.
There's sort of siloed to a great degree.
How is that operating model changing as a result of data?
>> So I'll answered it in two parts.
Part A is, if you realize
over the years what used to happen is,
you had a CIO in an organization or more a CIO,
and then you had enterprise architecture teams,
application development teams, support teams,
and so on and so forth.
In the last 36 months if you see,
there is an emergence of a new role
which is called the chief data and analytics officer.
So the data and analytics officer
is a role that has been created.
And the purpose of creating that role
is to ensure that organizations will pull out
or cull out resources within the CIO organizations
who are enterprise architects, who are data architects,
who are application architects or security architects,
and bring them into the ecosystem of the data office
from an operating model perspective.
So that innovations can be driven.
Data driven enterprises could be created
and innovations can come through there.
The other part of that is,
the use cases get prioritized when you start innovating.
And then it is a factory model
in terms of how those use cases get built
which is a no brainer in my mind at least.
But, that is how the operating model is coming up
from a people perspective.
From a technology perspective also,
there is an operating model that is emerging.
If you see all the hyperscalers that are there today,
Snowflake with its most latest and greatest announcements.
If you see the way the industry is going,
is everything will be housed into one ecosystem
and the beauty of this entire thing.
You have be able to fathom it effectively, right?
Because if I'm a multi-cloud kind of an environment,
and if I'm on Snowflake, I don't care.
Why? Because I'm Snowflake,
which can work around across the multi-clouds.
So, my data is in one place. Effectively.
>> Yeah. It's interesting what you're saying
about the chief data officer.
The chief data officer, that role
emerged out of the ashes like a phoenix,
of compliance, data quality,
and healthcare, and financial services, and government,
the highly regulated industries.
And then it took a while, but it increasingly became,
wow, this is a really front of the
board level role if you will,
and now you're seeing it, it is integrated with digital.
>> Absolutely. And there is one other point.
If you think about it,
the emergence of the chief data officer came in
because there were issues associated to data quality.
>> Dave: Yeah. >> There were issues associated
to data cataloging, as to how data is cataloged.
And there were issues in terms of trustability of the data.
Now the trustability of the data can be in two places.
One is a data quality, "Hey, bad data."
Garbage in, garbage out.
But then the other aspect of the trustability
is in terms of, "Can I do the seven Cs of data quality
and say that, okay I can hallmark this data as platinum
or gold or silver or bronze or unhallmark data?"
And with Snowflake, the advantage is,
if you have a hallmark data set,
that is say, a platinum or a gold,
and thanks to the virtual warehouse,
the same data set gets penetrated across the enterprise.
That's the beauty with which it comes.
And then of course the metadata aspect of it,
bringing in the technical metadata
and the business metadata together
for the purpose of creating the data catalogs
is another cool thing. And enabled again by Snowflake.
>> What are some of the,
when you're in customer conversations,
some of the myths or misconceptions
that customers historically, typically been making
when it comes to creating a data strategy?
Some of the misconceptions
and then what is your recommendation for those folks
since every company these days to be competitive
has to be a data company?
>> Yeah.
Around data structures,
the whole thought process has to be
hitherto in the past, we used to go with,
from source applications we would gather requirements.
Then we would figure out what sources are there,
do a profiling of the data and then say,
okay the target data model should be this.
>> Dave: Too slow. >> Too slow. Right.
Now, fast forward to the digital transformation,
there is producers of data which is basically
that applications that are being modernized today,
are producers of data.
They're actually telling you
that I'm producing this kind of data,
this is the kind of events that I'm producing,
and this is my structure.
Now the whole deal is
I don't need to figure out what the requirements are.
I know what the use case
the application is going to be helping me with.
So therefore, the entire data model is supported.
But at the same point in time,
the newer generation applications that are getting created
are not only created getting created
in terms of the customer experience,
of course that is very critical,
but they're also taking into account
aspects around metadata.
The technical metadata associated within an application.
The data quality rules or business rules
that are implemented within an application,
all of that is getting documented.
As a result, the whole timeline
from source to profile to model
which used to be X number of days in the past
is X minus at least 20% now or 30% actually.
So that is how the data structures are coming into a play.
Futuristic thought process would be,
there will be producers of data
and there will be consumers of data.
Where is ETL then or ELT then?
There is not going be any ETL or ELT
because a producer is going to say
that I'm producing the data for this.
A consumer says that,
okay I want to consume the data for this purpose.
There they meet through an API layer.
So where is ETL? Eventually going to go away.
>> Well, and those consumers,
if you think about the way it works today,
the data operating model if you will,
the transaction systems,
systems throw off a bunch of exhaust.
They gets thrown over the fence to the analytics system.
They're not operation.
The data pipeline,
the data systems are not operationalized
in a way that they need to be.
And obviously Snowflake's trying to change that.
>> Nagaraj: So data >> That's a big change. Please.
>> Nagaraj: Yeah. Sorry. Didn't mean to cut you off.
My apologies. >> No, no.
>> So data operations is a very, very critical aspect.
And if you think about it holistically,
we used to have ETL pipelines, ELT pipelines,
and then we used to have queries being written
on top of Teradata or MPPs and Hadoop and all of that
and reporting tools that would have
number of reports that were created
and certain self-service BI reports into the ecosystem.
Now, when you think in terms of a cloud data warehouse,
what is happening is,
the way you are architecting your solution today
in terms of data pipelines,
those data pipelines are self-manageable or self-healing.
Do not need the number of people
where there was no documentation
in terms of what ETL pipelines were written in the past
on certain ETL tools
or why something is failing.
Nobody knew why something was failing
because these are age old code.
But take it forward today, what happens is,
organizations are migrating from on-prem to cloud
and to the cloud data warehouse.
And the overall cost of ownership is decreasing.
The reason is
the way we are implementing the data pipelines,
the way the data operations are being done in terms of,
even before a pipeline is kicked or kicked in,
then, there is a check process to say
whether the source application is ready or not ready.
So such things, small, small things,
which are part and parcel
of the entire data operations' life cycle
are taking the center stage.
As a result, self healing mechanisms are coming in.
And because of those self healing mechanisms,
metrics are being captured.
As a result, you know exactly where to focus on
and where not to focus on.
As a result, the number of resources needed to support
gets reduced. Cost of ownership is low.
>> Much higher trust, self-service infrastructure,
data context in the hands of business users.
Data is now more discoverable, it's governed,
so you can now create data products more quickly.
So, speed and scale become extremely important.
>> Absolutely. And in fact, one of the things
that is changing is the way search is getting implemented.
Hitherto in the past, you created an index
and then, the data is searchable.
But now, it is contextual search.
Can I contextualize the entire search?
Can I create a machine learning algorithm
that will actually say that, okay, Nagaraj as a persona
was looking for this kind of data
and then Nagaraj as a persona comes back again
and looks for some different kind of data?
Can the machine learning algorithm go and figure out,
okay, what is going on in Nagaraj's mind?
What is he trying to look at?
And then, improve the the whole learnability
of the entire algorithm.
That's how search is going to also
get into a change kind of a scenario.
>> Excellent, Nagaraj.
Thank you so much for joining us
talking about data modernization at speed and scale,
HCL, what you're doing, what you're doing with Snowflake,
sounds like incredible power that you're enabling
and we're only just scratching the surface.
I have a feeling there's a lot more under there
that you guys are going to uncover.
>> Sure. So, we have a tool or an accelerator.
We call it an accelerator in the HCL parlance
but just actually a tool.
So, when you think about data modernization onto Snowflake,
it is predominantly migrating the data set
from your existing ecosystem onto Snowflake.
That is one aspect of it.
The second aspect of it is
the modernization of the ETL or ELT pipelines.
The third aspect associated to the data
that is there within this these ecosystems
is the reconciliation.
Older application. Sorry.
Older legacy platform. Snowflake.
Legacy platform gives me result X.
Does Snowflake give me result X?
That kind of a reconciliation has to be done.
Data reconciliation and testing.
And then the fourth layer associated
is the reporting and visualization.
So these four layers are part and parcel
of something that we call as ADvantage Migrate.
ADvantage Migrate will convert your Teradata model
into a Snowflake understandable data model automatically,
whether its Teradata, whether is Oracle Exadata,
Greenplum, Hadoop, you name a ecosystem,
we have the mechanism to convert a data model
from whatever it is
into Snowflake readable, understandable data model.
The second aspect is the ETL, ELT pipeline.
Whether you want to go from Informatica to DBT
or Informatica to something else
or data stage to something else, doesn't matter.
There is an algorithm or there is a tool
which is called the ETL pipeline.
We call it gateway suit.
Gateway suit actually converts the code.
It reads the code that is there on the left hand side
which is the legacy code, understands the logic,
it reverse engineers and understands the logic.
And then what it does is,
we use that understanding or that logic
that has been culled out into spark code or DBT
or any other tool of your choice from a customer standpoint.
That's the second layer.
Third layer I talked about, which is basically data testing.
Automated data testing and data reconciliation.
And the last but not the least is the reporting.
Because older ways of reporting and visualization
vis-a-vis is current day reporting and visualization,
which is more persona based.
The art of visualization is something different
in this aspect.
Come over to our booth at 2114
and you'll see ADvantage Migrate in the works.
>> ADvantage Migrate. There you go.
Nagaraj, thank you so much for joining us on the program
and unpacking HCL.
Giving us really that technical dissection
of what you guys are doing in together with Snowflake.
We appreciate your time.
>> Thank you. My pleasure.
>> Dave: Thank you.
>> For our guest and Dave Vellante.
This is Lisa Martin,
live from the show floor of Snowflake Summit 22.
Dave and I will be right back
with our final guest of day one in just a minute.
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