[MUSIC PLAYING]
BRUNO AZIZA: Welcome to another episode of "Data Journeys."
In today's edition, I have the pleasure
of being joined by Sanjeev Mohan.
Everybody knows Sanjeev.
He has been in the field of data and analytics for a long time.
He was a Gartner Vice President and spent hours with customers,
helping them with the most pressing issues that they have.
And right now, a lot of you are talking
about saving costs while maintaining
the pace of your innovation.
And so this is an interesting opportunity,
a lot of trade-offs you've got to think through.
Google Cloud has introduced new capabilities, and auto-scaling,
and compressed storage, and BigQuery additions,
which tends to save a lot of money for customers
and innovate with these new capabilities.
And so in today's episode, we're going
to ask Sanjeev what his takes are on these innovations, what
he's hearing from customers, and how you can capitalize
on these innovations.
Sanjeev, welcome to "Data Journeys."
Let's get right into it.
Tell us about yourself and what you've been up to.
SANJEEV MOHAN: Thank you so much, Bruno.
It's always a pleasure to hang out with you and do stuff.
I remember just two months ago, I landed in Orlando
after my redeye.
And you called me and said, let's do a carcast.
I was literally getting ready to go to my hotel and take a nap.
And instead I got ready and came over to your place.
This is the life that I lead.
And I love every minute of what I do because now I really
get to interact with people from all walks of life in the data
and analytics space, not limited by geographies
or by industries.
And I get to talk to various people.
Just going back to the Data and Analytics Summit
in Orlando in March, I had the honor
and the delight of having lunch with Bryson Koehler.
He is a chief data analytics technology officer at Equifax--
massive team.
And Equifax has done an amazing job of moving from on premises
into the Cloud while maintaining the cost optimization.
And that's what I liked the most.
When I was talking to him, it's like light bulbs
going on inside my head, just learning from his very
practical journey.
I was so impressed that after the lunch, I was like,
you've got to come to my podcast.
I have a podcast channel, it's called "It Depends."
And this story needs to be shared
because so many people want to hear,
what are the best practices?
You hear all kinds of stuff, Cloud is very expensive.
Yeah, it can be if you don't do things right.
But what is the right way to approach it?
For that, I'm going to have Bryson talk about it
on my podcast.
BRUNO AZIZA: So everyone should join that session
of the "It Depends" podcast.
Because Bryson, who is at Equifax,
will share his real-life experience with you.
So I'm really looking forward to that session.
Now, if we go to the innovations that Google Cloud introduced,
you talked about auto-scaling, compressed storage, BigQuery
additions, what is the market reaction that you're seeing,
how customers are optimizing around it,
how they're reacting to it?
How should our audience think about taking advantage
of these innovations to save and innovate at the same time?
SANJEEV MOHAN: Bruno, one of the important things
that I have to draw upon is that what Google introduced
is actually in reply to some of the requests
that I was hearing anyway from customers.
I've had this opportunity to talk to Google Cloud
customers across different parts of the world for many years
now.
And every time, clients were saying that a,
they want the freedom as to what features
they should have and then only pay for those features.
Why pay for all-you-can-eat buffet when you just want one
entree?
So this one-size-fits-all is not necessarily a good idea.
The second thing that I heard our customers was,
along with a feature, we should only pay for the resources
that we consume, and not a fixed flat rate pricing.
And that comes through auto-scaling.
BRUNO AZIZA: So there was a lot there.
And then you spend a lot of time with our customers.
You've got hundreds of partners building solutions
on top of BigQuery.
If you think about auto-scaling, and compressed storage,
and BigQuery additions, how should I
think as a customer about using these technologies separately
or together to optimize for cost savings as well as innovation?
SANJEEV MOHAN: Bruno, the important part here
is, why are you doing all of this,
all the auto-scaling, compressed storage, additions?
Because at the end of the day, it comes to cost.
And when we talk about cost-- see,
this is the interesting thing about Cloud.
On premises, we talked about sunk costs, right?
But in Cloud, cost and performance
are actually the same thing.
And this is a mental shift a lot of people have not made.
So why I say this?
It's because if I can compress my storage
and I can run things now faster, I'm going to save cost.
Because cost is calculated on consumption.
So the architecture and the cost leading to performance
are all just one thing.
So this is why auto-scaling is really important.
Because in auto-scaling, I am not setting a fixed price
for what I do.
Sometimes I may not even know.
What if I get it wrong?
Then my query will just--
either I won't be able to run it because I
put a hard limit on it, or the costs will go through the roof.
But with auto-scaling, I have that control.
And that control is what we were missing.
And I find that is really important.
I have control over how I scale, what additions, i.e.
What features do I pay for.
And the third part is my architecture
using compressed storage makes my workloads very efficient.
So it's a combination of all of these things
that we have to think through as one package.
BRUNO AZIZA: So there's a lot in what you just said here.
You spend a lot of time with our customers.
You've had hundreds of partners building solutions
on top of BigQuery.
When I think about additions, particularly the fact
that I can mix and match additions, as a customer,
how should I think about my workloads?
What are some of the key parameters I need to look at?
SANJEEV MOHAN: Bruno, there are only
a fixed number of things that at a high level
that people want to do.
Within an organization, some people
want to run reports, dashboards.
So they want those to run fast.
So that so they need query acceleration.
And depending upon which addition
you pick, you get that feature.
So that is one type of a workload.
But the next workload could be a machine-learning workload,
where maybe I want to train my model.
For that, I need a certain capabilities.
I don't necessarily want things to be spanning clouds,
for instance.
So that's a third type of workload.
Some workloads require multi-cloud access.
So this is how customers should think
about what is the query pattern, or what kind of workload
do I have?
And then they should marry their workload
through the type of addition which best suits their needs.
BRUNO AZIZA: Excellent.
And that's how they really figure out how to save money
with the flexibility they get.
And you combine that with auto-scaling and compressed
storage, you could get a lot out of the platform.
Now, like we said, you spend a lot of time with customers.
And there's a lot of guidance there
around scaling or auto-scaling.
How is Google Cloud's auto-scaling different
from what you see in the marketplace today?
SANJEEV MOHAN: The first thing that I want to say
is that Google's auto-scaling is not a t-shirt size based.
So it's based on slots.
And so it's more, I would say, finer-grained
than a t-shirt size.
T-shirt size-- and we talked about it earlier.
If I have 16 nodes and I need just a tad bit more,
I go to the next t-shirt size, which doubles everything.
And now I'm wasting my resources.
So I like the fact that, a, it's auto-scaling.
So it's not going to different size.
Instead, it's just going up by consumption.
And secondly, it's more fine-grained control.
So I'm saving on the cost and the resources that
are going to be provisioned as my needs increase.
BRUNO AZIZA: And the system does it for you.
So you almost don't have to worry about it.
You can put the thresholds that you want to manage within.
And then the system figures it out
for you, which is really modern and unique.
Now, we talk a lot about people moving
to these modern capabilities.
And for some people, it's a mindset shift as well.
What are the best practices, for people listening to us,
around cost-saving, optimization, and performance,
when they also are under pressure to deliver
more innovation into the marketplace?
SANJEEV MOHAN: Bruno, there is a very interesting thing
on Wikipedia, a page.
It's called Jevons principle, or Jevons law--
J-E-V-O-N, Jevons law.
It is from 100 years ago.
And what this guy in the UK found
was that as things get more efficient,
the resource usage goes up, not down.
So the idea that Google Cloud is making
my workloads more efficient actually
translates into more workloads.
The last thing that I want my clients to do
is to say that we are facing economic downturn, so stop
all innovation and let's start conserving cash because we are,
guaranteed, going to come out of it.
And you know what happens when the tide goes out,
you find out who's unprepared.
So we don't want to be in that position.
So when organizations ask me, OK, so what should
we do differently?
So I'm like, OK, the first thing that you
may want to consider is create a center of excellence
for FinOps.
Why am I saying this?
It's because there is this notion, which
we've brought over from our on-premises days,
that cost control is a CFO's job.
But who is consuming the cost?
Is it the CFO?
Absolutely not.
The CFO is just sitting in his or her air-conditioned office,
going through dashboards.
The developers are the ones who are
causing the cost consumption.
So FinOps is actually a responsibility
for everybody on the team.
And this understanding, this culture,
and what to do to be aware of where my cost is being incurred
and how to save it, this has to come from the center
of excellence on FinOps.
And that is very refreshing because everyone should
know what they're consuming.
Cloud gives me this ability, this notion or mirage,
of infinite capacity, which, as we know, is not true.
BRUNO AZIZA: That was a lot, Sanjeev.
Thank you so much for taking the time
to take us through the innovations together today.
Where can people find out more about your work?
SANJEEV MOHAN: Bruno, I am on LinkedIn,
of course, like most people.
But I also have my podcast series on YouTube,
called "It Depends."
And I have a Medium site.
We can share the links with you.
BRUNO AZIZA: We will absolutely do that.
And of course, people can Google your name
and find all the great content you've been sharing.
Thank you so much for spending time with us today.
SANJEEV MOHAN: It was my pleasure.
Thank you so much for having me on "Data Journeys."
It's always a pleasure to talk to you.
Thank you.
[MUSIC PLAYING]