STEPHANIE WONG: All right, hi everyone.
So we have a lot coming out of Google Cloud
Next across data analytics, data management,
artificial intelligence, and business intelligence.
So today I have on the show Bruno Aziza,
the Head of Data Analytics and Business Intelligence at Google
to take us through the announcements
and how they can help you.
So Bruno, welcome to the show.
BRUNO AZIZA: Well, thanks for having me, Stephanie.
STEPHANIE WONG: So Bruno, we have done this together
several times now, but for those who don't know you by now,
you come to us from companies like Microsoft, Oracle,
BusinessObjects, and many other data analytics startups
that you helped launch.
Your team spends a lot of time with customers, so tell us,
what should customers be excited about this year?
And how are data, AI, and analytics launches
aligned with what you see in the marketplace?
BRUNO AZIZA: Yeah, so if your audience, at this point if they
don't know, my passion is around customers,
so I'm going to direct you to go and watch
the customer sessions.
There are many of them.
We have Walmart, PayPal, Credit Karma, Buck's, L'Oreal, MLB.
And so those are great sessions that
give you a sense of what's going on in companies of all sizes
innovating with data.
I particularly like Walmart.
Of course, you heard them last time at Next.
I think they were talking about their BigQuery
treatment for data.
This time they're going to talk about Lakehouse,
and Lakehouse concept I think is an important one for customers
to really get connected, particularly people
that have succeeded with it.
Myself, I had the opportunity to do a great panel with leaders
from Snap, Deutsche Bank, and Wizard, so there's a lot there.
I really want to encourage people
to go and engage, not just watch the session
but reach out to the customers.
There's also a great session on our Data Journey channel
with Sanjay from Exabeam where he
talks about how to build data products,
which I think is another trend that customers really
are looking for best practices there.
So that's where I would go first,
is find the customer sessions, listen to them,
connect with the customers themselves,
and then of course lots of great innovation
that I know we're going to break down here, lots and lots
of great new products and new capabilities
that we're deploying in the marketplace at Next this year.
STEPHANIE WONG: Super exciting.
I also noticed that we had a great amount
of partner announcements, too, which is always a big positive.
So how do you see all these announcements connect with what
you see in the marketplace?
BRUNO AZIZA: Yeah, so I mean, we couldn't do what we're doing,
the speed at which we're innovating with customers
and the acceleration of their digital transformation,
if we didn't really partner well with the entire ecosystem.
So of course, I'm excited about the partnerships that we're
announcing with MongoDB and Calibra
and Elastic and Databricks and extended work
that we are doing with them, also enabling customers
in the multicloud world.
So we're going to show a BigQuery Omni trial, which
allows you to now kind of get a taste of what it's like to work
in a multicloud environment.
And so I really want to encourage
people to take advantage of that,
because our commitment is we're building for your environment.
We want to meet you where you are,
and so that's where you see the spirit of these partnership
announcements.
In general there are three big trends
that we see in the marketplace.
The first one is what I might call convergence, a convergence
of data and workloads.
The second one is this idea of governance.
I call this Governance with a big G,
because it's beyond just security and access.
It's also around data quality and lineage.
And then the third one is acceleration of the activation.
Now we have this data.
You get access to more data.
You can govern.
You can have high-quality data.
How do you activate that data is what
we'll be focusing on around these launches at Next
this time.
STEPHANIE WONG: All right.
Yeah, so I know that obviously we
can't go through all of the launches,
but let's try to highlight some of the big ones,
particularly as they connect to these trends.
BRUNO AZIZA: Yeah.
Yeah, so I'll start with the first one I'm particularly
excited about, is the support from BigQuery
on unstructured data.
So if you think about the history
of Big Query as this amazing analytics environment,
you know, we think about it as a cloud data warehouse,
but it's so much more, right?
It's this limitless environment for analytics workload.
And now with support of unstructured data,
it's opening all these opportunities of analyzing
more types of data.
So any data, structured, semi-structured,
unstructured, now you can run SQL on.
And while this is really exciting for business analysts
to be able to run these queries, what they also get is,
because it's inside BigQuery now,
you get all the added services that you would get in BigQuery.
So for instance, now you want to do
machine learning on top of unstructured data,
it's just available.
It's right there, because it is a core capability of BigQuery.
So a great scenario is imagine that you're a real estate agent
analyst and you want to be able to predict
the propensity of a house to sell based on its attributes.
So while you can use structured attributes,
now you can run BigQuery machine learning
on unstructured attributes like photos, for instance.
And so it opens all these amazing scenarios
that previously alternatives would
require different environments to run those.
And so we're bringing all that data in to one place, so now
people, business analysts using SQL can do more.
STEPHANIE WONG: OK, so unstructured data,
so that's obviously a huge point, but what else is there?
BRUNO AZIZA: So now that we've got people that have access
to more data, we also want to bring additional workloads.
And so a few months ago, we announced BigLake as a preview,
and we got a lot of great reaction
from the marketplace on that.
And because, of course, it's based on BigQuery
as a platform, it allows us to get that feedback
and innovate a lot faster.
And so now what we announced at Next
is this idea of supporting more formats.
And so we're going to start with Iceberg,
and then we also announced this idea
of supporting additional formats that our customers are
interested in, like Delta Lake or Hudi.
And so that's the idea of now you've got more data,
you've got more workloads, and you can enable more people
to do more with that.
The other announcement that's really interesting around this
is the idea of Spark and BigQuery.
So we've talked a lot about giving people
this first experience of Spark in Google Cloud in general,
and now we've announced this BigQuery and Spark coming
together as a kind of a native workload.
And so what's exciting about this
I think is, one, of course, you can find it all in BigQuery,
so it allows people to collaborate around data,
collaborate around workloads.
But also it provides administrators to know that you
have a predictable environment both from a billing
standpoint-- because now when you're running those workloads
inside BigQuery, well, the billing is something that
people are familiar with, BigQuery slots--
but also an integrated security and all the other
attributes around making sure that people
are working with the right data and so forth.
And so these three, right, unstructured, semi-structured,
and structured data in the same place-- really exciting.
Spark is really exciting for folks.
And then of course, this idea of extending BigLake to more data
formats, it's creating this environment where now you
can work with any data in any workload across a platform.
STEPHANIE WONG: Yeah, exactly, unstructured data,
Iceberg support, Spark in BigQuery,
you are really making the case here for all data,
all workloads there.
BRUNO AZIZA: That's the idea.
The idea is, you know, we think about it
as, to enable their organization to accelerate innovation,
they've got to have limitless data, limitless workload.
And now the next step is that, OK,
now I get access to this gigantic ecosystem--
and also, by the way, Analytics Hub is going GA as well.
So that's another big piece of news
because now you can create exchanges with external data
and bring that into your environment, also exchanges
inside your organization, and securely
create those exchanges people have access to.
So really creating an ecosystem inside your organization,
outside your organization.
But what we want to add to that is trust of this data.
That's what I was talking about earlier,
this idea of Governance with a big G. Dataplex
has been a very successful product, service for us,
and we released this service in GA
at the beginning of this year, got a lot of feedback
and a lot of great reaction from our customers.
The next level beyond that is introducing
automatic data quality and lineage,
and all types of services that's going
to land you in an environment where,
when you're building your data mesh
and you bring BigQuery in combination with Dataplex,
you now have the ability to secure,
provide better quality around freshness and completeness
of the data automatically from within the platform.
And so look back at the sessions with Dataplex
and the innovation that we are really
launching inside this product.
We expect there's going to be a ton of reaction
there because it's additional functionality,
but also additional functionality on more data.
Originally we started just in a very narrow set,
and now we're expanding that to metadata discovery in Spanner,
Bigtable, Cloud SQL, and Looker as well.
And so we really kind of expanded, if you will,
the surface area of where you have data today
that you want to make sure your people can trust.
STEPHANIE WONG: OK, so more data, better data, so
that's fantastic, but what else can we
highlight here from Next?
BRUNO AZIZA: So you know that if it was me
we would cover all the launches, right?
So I can't really do that here, but I
want to connect these themes.
So you've got this idea of, you know, you build your data ocean
and it's as wide as possible.
I use the term "ocean"--
we've talked about this in the past--
because customers are telling us,
my environment is not a lake.
The lake is nicely surrounded by earth, and so it's landlocked.
But my ocean is, I never see the end of the data.
So we want to put capabilities that
enable people to build their ocean in a secured matter,
scalable matter, and so forth, so that's step one.
The second bit here is trusting the data,
so that's why Dataplex, data quality, lineage,
all these capabilities are super important.
Now once you have that, you can then activate your data
and put it in front of people so they can now
really lower the cost of innovation for themselves.
And so the first bit here is to think about,
what is the number one application or the number one
interface or metaphor that people use?
And I'm not talking about data engineers or data analysts.
I'm thinking about people like you and me, so business users.
If you do a budget tomorrow, Stephanie,
what is the tool you're going to be using?
STEPHANIE WONG: A spreadsheet probably.
BRUNO AZIZA: That's right.
So spreadsheets are very popular.
In fact, I do most of my thinking in spreadsheets
today, because it's a format I understand, rows, columns.
I can make calculations happen.
I can share it.
I love Sheets functionalities around artificial intelligence,
autocompletion of my sums and so forth.
So that tooling is very useful to me.
And a few years ago, we introduced this concept
of Connected Sheets, which now is Connected Sheets that
can talk to BigQuery through a connection, right?
So now as a business analyst, you
can imagine you've got access to this limitless analytics
infrastructure that passes on your rights.
It's a collaborative environment,
and now you can query lots and lots of data
without really requiring SQL.
And that really has opened a lot of innovation
inside organizations.
Now, historically Sheets was in the enterprise version
of Workspace, and what we're announcing
is that we're making this now broadly accessible.
And so you want to go back to those sessions
and take a look at how you activate that.
The design here is democratization, more access
to more types of personas who can use data at scale
and need to do it in the modern manner.
So spreadsheets of the past, they were disconnected.
You had to download them and send them around--
not very secure, not very scalable,
not very well connected to modern data environments.
We are opening that up now, and we see a ton of innovation
around that.
So that's a key announce that I'm really excited about,
as a spreadsheet fan myself.
Now what this is also opening is the idea of,
what is our philosophy around business intelligence?
When we look at the scenarios and the use cases customers
are interested in, of course we talked about this information
worker idea of integration within an environment
or workflow, where you're in the spreadsheet
and then you want to use that as a business user.
We also know that there's people needing
self-service and governed business intelligence,
and so we're bringing under the Looker family brand
the idea of bringing Data Studio and Looker.
And so now Data Studio is becoming Looker Studio,
and it's tightly connected with the rest
of the roadmap of Looker.
And then finally, we also know that the market is evolving,
with BI that goes beyond dashboards,
and so what that means is people are building data products
and they need BI to be accessed through an API
as part of a consumer-grade application that
scales to millions of people.
And this concept of building data products
is really a disruptive one that we
see some of the best organizations drive through.
They can organize themselves as building data products,
so they have a PM for their product
and they have an engineering team around their product.
In fact, the session I was talking
about earlier with Exabeam is really
around how do we teach our organization
to become data product first, have the mindset around data
products.
So you'll see a lot of innovation
around that, how we enable organizations through Looker
to have one semantic layer that is powering your applications,
either custom, either through Productivity suite,
or through the self-service and Govern dashboards
that they need so much.
And so we do a lot of work with that.
And to close on what we started with,
partners also now get access to this universal semantic layer.
We announced the work that we're doing with Tableau
and all our BI partners as well, where we want to just open up
this idea of more data, more governed data,
better data will lead to more innovation,
because the cost of experimentation
is just going to go down because it's accessible to more people.
So you can expect more innovation from your people.
STEPHANIE WONG: Yeah, definitely.
I mean, wow, we covered a lot of launches,
between BigLake, BigQuery, Connected Sheets, and Looker.
Do you have any parting words for us here?
BRUNO AZIZA: Yes, and there is more,
so you have to keep me honest on just a few.
And so what I would say is that if you've not
been able to attend the Google Next sessions,
go ahead and register, because that gives you
access to the replays.
And then engage.
So if you watch something that you like from a customer,
don't wait.
Just reach out to that customer on LinkedIn.
Of course you can reach out to me as well.
You know, we are doing these events
and we are building these products
for you, so we want to get your reaction, see what we can do.
And then finally what I would say
is that every week we have a new interview
of a customer in this program called Data Journeys.
It's a YouTube playlist.
And so I want to encourage you to subscribe there and make
sure you follow our customers.
These are not marketing videos.
These are really focused videos on how the innovation is driven
through our customers, and we want to share that with you
as a platform so you can share with each other.
So that's what I would say.
Make sure to register.
Attend the recording.
Share that.
Reach out to us.
Reach out to the customers.
Reach out to me, tell us how we're doing.
And then finally, follow us on Data Journeys
so we can get more customer stories and best practices
out to you.
STEPHANIE WONG: All right, well, you
heard it from Bruno, a very busy person, as you can tell.
But thanks so much Bruno, and we'll see you again next time.
BRUNO AZIZA: Thanks for having me, Stephanie.