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IRINA FAROOQ: Hello, and welcome to Google Cloud Next.
In this session, we'll be talking
about leveraging open source as part of your data cloud
strategy.
By the way of introduction, my name is Irina Farooq,
and I'm one of the product leads for our smart analytics
portfolio.
And joining me today is Anil Madan, corporate vice president
of data platforms at Walmart, who'll
be sharing their journey for transforming their data
platform in one of the largest cloud platforms in the world.
But before we delve into the Walmart story,
I'd love to share a little bit about the importance
of open source and our approach at Google
of helping you leverage it as part of your platform.
As many of us realize, we're living
in the golden age of open source, where
it is now being pervasively used by enterprises
across the globe.
In fact, 99% of Fortune 500 companies
are using open source today.
And the pace of adoption is only accelerating.
As we look at this transformation,
the key is that open source is no longer in a separate corner
of your data platform.
Instead, it needs to be fully integrated
as part of the mainstream platform,
giving your team the flexibility to use the best
tools for the job with centralized security,
governance, management, and an integrated platform.
At Google Cloud, we have long focused on enabling customers
to modernize their open source deployment with Cloud Dataproc,
a managed open source offering.
Customers like Metro, Broadcom, Vodafone,
LiveRamp can use Dataproc to modernize
their large-scale Hadoop and Spark deployments,
changing the way they power their platform,
productivity of their teams, and, ultimately,
innovating in a way they interact with their customers.
As we've been in this journey over the past five
years of helping some of the largest customers in the world
to transform their open source data platforms,
we've learned a lot about what it takes
to manage these deployments.
And last year, we made our special commitment
to Spark and Google Cloud, making
Spark a first-class citizen on GCP
but bringing innovations like serverless
Spark for all of your workloads, from exploration
to data science to data engineering,
bringing it as part of the integrated pervasive experience
on GCP, and then ultimately giving you
the flexibility of consumption in the way that
makes sense for your platform.
This year and this week at Next, we're
innovating further with our Spark offering
by now integrating Spark in BigQuery.
What that means is that, now, your analysts, data scientists
can leverage Spark-stored procedures natively
from within BigQuery, all with the same integrated
billing, integrated security and governance,
and, ultimately, that seamless serverless experience
that they're used to with BigQuery translating end-to-end
from BigQuery to Spark and beyond.
The other innovation we're introducing
is there's a serverless Spark interactive integration
with Vertex AI.
So now, your data scientists can seamlessly
do iterative development on serverless Spark, all
with the same built-in security, authentication, ease of use,
and seamless onboarding to our MLOps platform.
And now, I would love to welcome Anil to the virtual stage
to share the journey his team has been on to help power
the data platform at Walmart.
Welcome, Anil.
ANIL MADAN: First of all, thank you for having me.
I'm really, really excited to share more
on how Walmart is leveraging open source as part
of our cloud data strategy.
As Irina mentioned, my name is Anil Madan,
and I have the privilege of leading the data platforms
team at Walmart Global Tech.
My team is responsible for one of the largest
hybrid multicloud data platforms,
including all of the data services
from operational databases, analytics, machine learning
platforms, A/B testing, and AI/BI tools.
IRINA FAROOQ: Great.
Thank you, Anil.
So why is data important to Walmart?
How does it play a role in the Walmart business overall?
ANIL MADAN: Data is that raw material
that drives every aspect of our retail business.
Data helps us build delightful experiences
for both our customers and our associates and our employees.
And it basically controls every part of our data lifecycle
from online transaction flow to offline big data analytics.
The heart of all of this is our mission and vision
to save people money so that they can live better,
making shopping easy as if you're talking or texting
a friend, ordering what you need when you need so that it can
be delivered within an hour.
So data, in that sense, is powering our entire supply
chain optimization.
It's powering AI/ML, which, in turn, can
help us predict demand, forecast better, and create
real-time inventory intelligence so that we can optimize
for both cost and efficiency.
Meanwhile, computer vision and language models,
which are, again, derived from data,
are helping us create powerful experiences for our customers.
And finally, data is enabling augmented and virtual reality
to help customers try and buy products.
IRINA FAROOQ: It's incredible and so inspirational,
that impact you're trying to drive--
you're driving, actually-- in our everyday lives.
So how do you think about building the cloud platform
to support these ambitious goals?
ANIL MADAN: Yeah, so our approach
is we've basically taken a whole hybrid cloud approach, where
we've assembled public, private, and edge clouds together
in really a reasonable model, which is giving us
scale, flexibility, and elasticity
so we can innovate for our customers.
We call that internally the triplet model,
which is basically an innovative and powerful way for us
to bring in these things together to enable what we call
as our edge cloud, which is 10,000-odd stores across the US
and globally so that we can bring in the computational
power and data closer to our customers and our associates.
Now, the triplet model, what it does with this approach is
the offer is not only burst capacity for us
to react better but also help us with managing the peak consumer
demand, such as holiday shopping season or a great price
on in-demand [INAUDIBLE].
It's also helping us with resiliency, DR,
while it's helping us reduce cost.
I mean, we're looking at about a 10% to 18% cost reduction
to support everyday low prices.
IRINA FAROOQ: That's incredible.
So as you build that cloud vision,
what are some of the key tenets of your data platform strategy?
ANIL MADAN: Yeah, so look, the data platform
actually sits on top of that cloud Infrastructure
which I just talked about.
And it really includes all the tools, frameworks,
and databases and analytical systems.
It includes thousands of databases dealing with millions
of what I would say IOPS, messaging pipes
which move terabytes per second, streaming systems which is,
again, processing billions of events,
and then big data and AI/ML platforms,
which are powering all our BI and predictions.
This, in aggregate, is helping both
serve not only those online experiences but also the store
transactions.
And daily, we are ingesting petabytes
of data which hundreds of our scientists and ML engineers
can actually build upon to power these experiences.
Now, at the heart of it, our data strategy
is actually centered around three big tenants.
The first one is data placement and geo-affinity, which is all
about, how do you categorize data
so that you can create geo-affinity?
And you can direct customers to their local triplet, where
you have these triplets in East, West, and Central,
if you really look at US.
And by doing that, what we can enable
is some very powerful experiences
while still enabling--
while still supporting cost optimizations.
For example, a user visiting a dot.com site in New York
would be routed to our East triplet.
Additionally, we've also built automation
so that we can symmetrically deploy these workloads
across these clouds.
The second component or the second tenant to our strategy
is intelligent sync, which is all
about how, in a multicloud environment,
you can acquire, process, and intelligently distribute
data across heterogeneous data stores, which span
both private and public cloud.
So that's a critical piece of our strategy.
And finally, the AI/ML with the advances in CPU, GPU, TPUs,
it's helping us really create some amazing predictive models
which then can be deployed real time through our Element.AI
platform.
So we have an open source-based AI/ML platform
called Element which actually can symmetrically
deploy across all of these clouds to power inference
so that these models can be used in real-time context.
IRINA FAROOQ: So I heard you say open source,
and I couldn't resist asking, what role
does open source play in your overall strategy?
ANIL MADAN: Yeah, so open source is a critical enabler
in how we are deploying data services across public,
private, and the edge clouds.
And our approach is actually using
the best of breed data products across open source
as well as public cloud-provided.
Open source capabilities like Cassandra,
Postgres are powering our databases.
Solar elastic searches are powering all our search
and our operational analytics.
Memcached Redis is powering all our caching.
And then things like Kafka and Spark streaming
are powering our messaging and streaming capabilities.
Now, this is giving us--
this is coupled with things like Spanner, Alloy, Cloud SQL,
and capabilities from other cloud providers
to create what we call as best in class.
These, in conjunction, help our engineers
innovate by using fit-for-purpose product
for their application use cases.
IRINA FAROOQ: Thank you, Anil.
So our teams have partnered so closely on Dataproc.
Can you please share a little more
about the role it has played in modernizing your Hadoop
deployments?
ANIL MADAN: Yeah, absolutely.
Look, I mean, we've gone through a massive 18-month journey
where we have actually moved all of our on-prem workloads
from on-prem Hadoop to the cloud, to GCP.
And Dataproc is one of the highest used services for us
in that space.
It's helped us in three areas now.
It's serving as the backbone for all the pipelines
and how data moves between operational stores
and to the analytical stores.
It's helping us close the feedback loop
through real-time processing and streaming, which
can feed back from operations tools back to serving systems.
And then the third component is prospectively
powering all our machine learning.
So all of Element.AI actually sits on Dataproc
to really create fit-for-purpose workloads across CPU, GPU,
and TPUs, creating that innovation for our scientists
and our machine learning engineers.
IRINA FAROOQ: That's great, Anil.
And what would you say have been the biggest benefits
as you've gone through this very rapid transformation?
ANIL MADAN: Yeah, so the key benefits is it's, first of all,
helped us create an enterprise leg because now,
with this modernization, all data
is in one place with Google Cloud Storage.
The second piece is cost because now we
can run the formal workloads as opposed
to running static workloads, paying for both electricity
and power in our on-prem data centers.
The third component is scale.
So now we can--
things like autoscale give us the elasticity
we need for processing.
So those are three key benefits.
IRINA FAROOQ: That is great to hear, Anil.
So what are you most excited about as the next phase
of your evolution in your data strategy?
ANIL MADAN: Yeah, so I would break that--
I'm excited about a couple of big, broad areas,
first, the big data component, the big data piece.
There are, I think, three key products
which could be very, very exciting for us.
I would say data catalog and governance.
So the work I think you guys are doing
with Dataplex, that could give us that metadata
layer so we can power all of our data catalog
so we can discover and create data quality rules,
understand lineage, and ultimately provide
trustworthy data to our business so that we can shorten
the time for decision-making.
The second component is BigLake, where
you start interoperating workloads between BigQuery
and Dataproc so that you can leverage
the power of a single storage engine to do both.
And then the third component is, how can Vertex AI
complement our Element platform so
that we can bring in the best of breed across these two?
The second piece of what I'm excited about
is in the database space with things like Spanner and AlloyDB
because those could also be gamechanging for us.
IRINA FAROOQ: Thank you, Anil.
And that is very powerful.
How do you think about--
I'm sure lots of people in the audience
are sitting and looking, and they're
in the earlier phase of their own transformations.
What advice would you give them as they
start on their journey to help transform their business?
ANIL MADAN: Yeah, so I would say, look, I mean multi
and hybrid clouds are a reality.
Accept it.
Cost is a critical component.
Don't ignore it.
I mean, you have to look at price performance
across open source and cloud provided.
And the third piece is best of breed gives you choices.
Use that effectively, open source and cloud provided.
Leverage it so that your engineers and scientists
can actually innovate with best products in the industry.
I hope this advice is useful.
And again, thanks, Irina, for really having me
here at Google Next.
IRINA FAROOQ: Thank you so much, Anil,
and leaving us with this innovation message
and really helping folks understand
the journey you've gone through at Fortune 100 scale.
Hope you all enjoyed the talk, and you enjoy
the rest of Google Cloud Next.
Thank you very much, and we'll see you soon.
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