[MUSIC PLAYING]
AJA HAMMERLY: Hi, everyone.
Thanks for joining us today for the developer
live Q&A. I'm Aja.
And a reminder that this Q&A is live.
If you want to join the conversation,
reach out on Twitter with the hashtag googlecloudnext.
We've reached out asking for your questions on social media,
and y'all have been responding.
We'll be answering your questions during the next 20
minutes.
And first of all, let's welcome Priyanka,
who's here to answer your questions with me today.
Hi, Priyanka.
PRIYANKA VERGADIA: Thanks, Aja.
That keynote was absolutely amazing.
Yeah, so let's get into the goodness
that Urs and Aparna shared.
A highlight for me was really that upcoming documentary
on Kubernetes.
So cool.
I also enjoyed the Google Cloud innovator community
announcement, securing software supply chain,
and building sustainably.
What caught your eye?
AJA HAMMERLY: Oh, a lot of the stuff you just mentioned.
Definitely our innovations and sustainability.
The ability to see your carbon footprint of your cloud
workloads is really, really cool.
When I heard about it in yesterday's keynote,
I immediately went and looked at all my personal projects
to see the carbon cost of what I'd been doing.
And I love having that kind of data
to use in decision making like--
help me make the right decisions.
But I'm mostly really excited about the Cloud innovators
program.
I've really missed interacting with our Google Cloud dev
community.
I want to hear and see all the amazing things
the community has been up to.
And the innovators program should let us do that.
And we're working on some super special innovators-only events
in 2022 that I can't wait to tell the community about.
PRIYANKA VERGADIA: Wow.
Yeah, that all sounds really amazing.
I'm also excited about that secure supply chain
announcement--
SLSA-- and how the combination of Cloud Build and Binary
Authorization actually helps kickstart your journey
to secure your software artifacts by fully
automating that build process.
AJA HAMMERLY: Yeah, that's just so interesting.
That's so cool.
So let's get to our first question.
We've got questions coming in just for the folks at home.
The backstage crew is looking for your tweets as well,
so keep them coming in during the thing.
We'll be bringing them in live.
So our first question is from Asher,
and it is who would be good candidates
for the participation in the private preview of Earth
Engine?
PRIYANKA VERGADIA: Yeah, so anybody
who is like doing work on sustainable things.
So with the sustainability lens--
if you're thinking about financial services,
the customer packaged goods, and their impact
on the environment, and how all of that
can be combined together.
So yeah, anything that you're doing with the sustainability
lens.
Obviously, it's in private preview, so you have to qualify
and stuff.
But if you have that angle of sustainability,
you're probably a right fit for it.
AJA HAMMERLY: Awesome.
Thank you for that.
Ah, second question came in from Priyanka's Twitter.
I saw this one this morning as I was coming in to do this Q&A,
and this one is any examples-- do
we have any examples of end-to-end Spark-based
pipelines for ML?
They'd love to see them.
Specifically, they're interested in learning
how serverless Spark can be leveraged in big data and ML
ops framework on Google Cloud.
PRIYANKA VERGADIA: Yeah, so thank you for this question.
I think it came from [INAUDIBLE]..
I saw it on my Twitter this morning.
So really what it's all about is the serverless
Spark ML framework is about not having
to create your backend infrastructure to run Spark.
So that's all taken care of for you.
So you can just run your workloads,
which is, in this case, like you're
trying to run a machine learning Spark job--
you can just get started by not worrying
about the infrastructure.
So that's one part.
The other piece of it is the Vertex Workbench,
which is the Jupyter notebook, but hosted.
But it also gives you the opportunity
to connect with Dataproc, or the Spark ML
jobs that you might have built, and run all of that.
So as a data scientist, I can just use my workbench,
and that becomes my home to kind of get the data,
massage it, connect with Dataproc, Spark ML jobs.
And then get all those predictions
right in that one spot.
AJA HAMMERLY: Awesome.
Thanks, Priyanka.
That was a really comprehensive answer.
Ah, so now we have a question from Christopher,
and it's what are the benefits of the innovators program?
You mind if I take this one?
PRIYANKA VERGADIA: Yeah, please.
AJA HAMMERLY: So the big benefit of the innovators program
is going to be access to innovators only events.
Like we're going to be doing some AMAs we're
planning right now, roadmap meetings potentially.
And there's also an innovators virtual background
that you can download and use in your Google Meet meetings.
Ooh, another question came in from Twitter.
Awesome.
So let me read this one.
My POV, massive focus on the security-- massive focus
on security focus in the Developer Cloud.
Isn't this a topic that matters more
for the managers of developers than for developers?
And I'll take this one too, if that's cool with you.
PRIYANKA VERGADIA: Yeah.
AJA HAMMERLY: So I'm a manager, and yeah,
that's a really good question.
Security is vital to all aspects of software development,
and security needs to be everyone's job.
Yeah, managers need to care a lot about it,
but we need to make the tools so that developers
can do the right thing automatically,
and that everyone is participating
in making our cloud more secure.
And we talked about some of those things.
We talked about our tooling that can help you make sure
that you put your secrets in secure locations
as opposed putting them in code.
We talked about the SLSA.
We talked about lots of other parts.
And all of this is part of a secure software supply chain,
but security does need to be everyone's job.
Managers can help by teaching their developers
and enforcing it, but everyone needs
to take the steps to make things more secure.
So yes, good point, but everyone's involved.
This is a great one from Priyanka's LinkedIn.
Is it possible to connect to the public IP of a Cloud SQL
instance from a private Cloud Run service?
Ooh, I've been asked this one before when visiting customers.
Also is this cross-project set up?
Cloud Run service and Cloud SQL instance
are on separate GCP projects, so can you
do a cross-project setup with your database
and you're running a server somewhere else?
Priyanka?
PRIYANKA VERGADIA: You actually can.
So you have-- so in this scenario,
you have a Cloud SQL instance in one project,
and you have your compute or whatever
is calling that Cloud SQL instance in another project.
And you could totally make them work
by using what is called as private service access.
What it does is it connects the two together even
with a private IP.
So you don't even have to expose a public IP for your Cloud SQL
instance, which again comes back to the security point
you were making earlier--
everybody has to think about security.
So you're not exposing with public IP.
You're just using the private IP of your Cloud SQL instance,
and connecting it to the virtual machine
or wherever you're running your compute
to call your servers from.
So it's possible.
Private service access is the service
you're looking for to kind of connect the two together.
It's a VPC offering.
AJA HAMMERLY: Awesome.
Thank you so much, Priyanka.
Cool.
A question from Andy.
This is one of my favorites.
So there's a lot of ways to run an application with Google
Cloud.
It's a huge platform.
How do I know what I should choose?
Where do I run my stuff?
Should I run it on GKE, Compute Engine, Cloud Run?
So many good choices.
PRIYANKA VERGADIA: Yeah, and this
is a question we get a lot.
And it really just depends on your situation.
And the situations can be a lot-- the type of team,
the size of team, and the number of developers you have,
and the languages that you might be using.
So there are lots of different scenarios
in which you can decide.
So I'll give an example of a few.
So for example, Compute Engine.
If you're migrating and you just want to get from on-premise
infrastructure into cloud, and you just want the speed--
you just want to get there--
I would choose Compute Engine to just migrate as-is, and then
modernize later if needed.
Sometimes, you don't need to modernize
if you have licensing requirements
and stuff like that.
So that's Compute Engine.
If you want to work with containers-- need a little bit
more abstraction-- less abstraction,
you can work with containers with GKE.
And that gives you a lot more control
over the number of nodes you have,
and the processing that you're using.
But if you might be just wanting to run containers,
but don't want to manage the underlying infrastructure,
like nodes and stuff like that, and the regions,
just use Cloud Run, because it's serverless,
but it allows you to use your container images
and just deploy them.
Cloud Functions is kind of like everywhere.
So you're trying to do-- handle one function, or a piece
or a feature of code that you just
deploy in that function as a Function
as a Code sort of service.
But it kind of applies everywhere.
So I wouldn't say--
so Cloud Function is not like an OR--
it's more of an AND--
like it works with any of those.
It's just more of an extension and enhancement
of your services with serverless.
So I hope that helped clarify a little bit of that,
but there's a lot that goes in that decision.
AJA HAMMERLY: Yeah, I know.
And I really liked how you called out
the Cloud Functions is not an OR, it's an AND.
It's-- Cloud Functions is just fantastic at tying pieces
together.
It's one of the things I love about it.
And I'm just going to point out for folks
that we have sessions on all of these in the breakouts.
So if you want to go learn more about these,
go look in our breakouts, and you
can find out more about the different offerings
that Google Cloud has.
So now we have a question from Esther.
What languages do Cloud Run and Cloud Functions support?
Oh, let's see if I can do this from memory.
I've been practicing.
So GCF-- these are the standard languages
you see on Google Cloud.
We've got Node.js, Python, Go, Java, .NET, Ruby, and PHP.
I got them all.
Awesome.
Cloud Run supports any language, or any library,
or any binaries that you can put in a container.
But if you want to use the source code deploys feature
that Abby showed earlier, that is supported on Node, Python,
Go, Java, and .NET.
And specific versions of those languages are supported,
so please do go to the website, and make sure the version
that you need is the one that we support.
PRIYANKA VERGADIA: Great memory, by the way.
AJA HAMMERLY: Oh, yeah, I've been
getting really good at naming all those languages.
So from Selena-- ooh, yes, great question.
When should I use GKE Autopilot versus Cloud Run?
PRIYANKA VERGADIA: I'll let you take that one.
AJA HAMMERLY: You're going to let me take this one?
OK.
So this pretty much boils down to,
do you want Kubernetes or not?
If you want Kubernetes, if you want the enhanced flexibility
that Kubernetes has, or you want to have all those knobs
and dials that you can turn to really fine tune everything
for your networking needs or your particular load profile,
use GKE Autopilot.
If you want to-- if you have a container,
and you want to run it on GCP, and that's your goal,
Cloud Run is great.
Cloud Run is fantastic at that.
And as we pointed out, you don't even
need a container if you use Cloud Run source
deploys for those languages I just mentioned.
More questions.
Ooh, this one's for you, Priyanka.
This is from Caleb.
What file formats are supported with the Doc AI stuff
that Anya talked about?
PRIYANKA VERGADIA: Oh, yeah.
So you can do images and PDFs.
So it's really about the unstructured image
data, so PDFs and images.
AJA HAMMERLY: Awesome.
Let's see what else we got.
Ooh, this one's from Mark.
Another for you, Priyanka.
Ooh, it's another security question.
So can you tell us more about Binary Authorization?
We covered it very briefly in the keynote,
but it's something I've been hearing a lot about,
and I'd love to know a little more about it.
PRIYANKA VERGADIA: Yeah.
So again, it kind of boils down to the whole security narrative
that you mentioned-- that everybody
is kind of responsible for the security
of the entire platform.
So in this case, the binary auth is really deploy time security.
So you're deploying, and making sure that your images--
or container images if you're using GKE or Cloud Run--
works with binary auth.
So when you're at your deployment stage, you have--
you can provide signature authorizations on your images.
So if-- and the verification and the authorities for those.
So if they are authorized, only-- binary auth
will apply the authorizations.
And once the image is authorized,
only then you can deploy it.
AJA HAMMERLY: Awesome.
So I just got the signal that we're running out
of time, so this is going to, unfortunately,
be the last question.
And this question comes from Wesley.
Can I use the build integrity features
with my on-prem software?
PRIYANKA VERGADIA: Hmm.
Yeah, so you kind of can.
So with Cloud Build, it's really any container image which
is built on Cloud Build, you can use both on--
you can use it in both on-prem or on Google Cloud.
You just have to use the binary auth attestation Cloud Build.
And it's on the GitHub page, so you can check that out.
But if you're building it in Cloud Build,
you can deploy it on-prem or in Google Cloud.
AJA HAMMERLY: Thanks, Priyanka.
Well, that was a lot of fun.
It was great to hear all the questions from the audience.
Y'all had some great ones.
And I want to say just a huge thank you
to Priyanka for joining us, and answering so many
of those questions.
Be sure to join us back over on g.co/cloudnext,
as the open infrastructure spotlight will be kicking off
shortly, and they have some amazing things that
they're going to show off.
Thanks for joining us, everyone.