[MUSIC PLAYING]
RON PANTOFARO: Welcome back.
I'm Ron from the Google Databases product team.
Today I'm very excited to host Kristian Cibulskis,
Director of Technology from the Broad
Institute of MIT and Harvard.
He has some interesting Cloud SQL stories
that he's going to tell Andy Goodmans, our VP and General
Manager for Databases.
So Kris, can you tell us a little bit about the Broad?
KRISTIAN CIBULSKIS: Yeah, hi, Ron.
So the Broad Institute of MIT and Harvard
is a non-profit biomedical research organization
that was founded in 2003.
And our mission is to fulfill the promise of genomics
through the creation of tools, making them broadly available,
applying them to the understanding of biology
to cure human disease.
And since our leading role in the sequencing
of the first human genome, the creation and analysis
of human genetic data has been one of our key growth areas.
And within the Broad Institute is the data sciences platform.
And our mission is to create and operate
cloud-based tools for our researchers
to help them in their goals.
As for me, I've been at the Broad for 15 years.
And in that time, I've spent time as an IC engineer
in our cancer research group doing genetic analysis,
as well as leading our engineering teams as part
of the data sciences platform.
But now I'm part of our special projects team.
And our mission is to look at emerging technologies
to see how they could create differentiating products
for our users.
ANDY GOODMANS: Kristian, thanks so much for joining us today.
Can you tell us about some of your industry challenges
and why you ended up choosing Google?
KRISTIAN CIBULSKIS: So the life sciences
are being increasingly driven by the generation analysis
of these massive data sets.
And being able to organize and analyze that data is critical.
We make use of products, like BigQuery and Dataproc
in the course of that analysis.
But at the same time, the industry
is also changing extremely rapidly.
And from a software development perspective,
being able to keep pace with those changes
and act nimbly to deliver software to our users
is critical.
The more we can speed the pace of development,
the better we can serve those researchers.
And as for why GCP?
The Broad and Google have had a long history together.
And so as we started to look to where to start,
GCP was a natural starting point.
Of course, we're interested in extending this
to all the clouds.
Because that's what biomedical data ultimately resides.
ANDY GOODMANS: And which of our managed databases do you use?
And what are your key use cases?
KRISTIAN CIBULSKIS: One of the main products of the data
sciences platform is terra.bio, which
is a secure, scalable, open source platform
for biomedical research.
And it was co-developed by the Broad, Microsoft, and Verily.
It's used by researchers both at the Broad
and around the world to help them access public data
sets and methods, manage their own data,
organize their research, and collaborate with others.
And in the control plane for Terra,
we use a little bit of everything.
We use Datastore and Firestore.
And we use quite a lot of Cloud SQL, both Postgres and MySQL.
When we started out, we knew we wanted
to manage database service.
And MySQL was the initial offering.
And so we started building our services using that.
As Postgres became available, our newer services
were built using Postgres.
And we're slowly migrating our older services over time.
The kinds of things we track in this control plane
are the different data assets and methods, research results,
as well as access control, so things that maybe are not
totally unique to the life sciences industry,
but extremely critical for our users.
Another interesting use case we have
is when the global pandemic hit, the genomics platform
at the Broad who's one of the largest genomic sequencing
providers in the world quickly pivoted to become
a COVID testing provider.
There were times during last year
where we were executing 10% to 15%
of all the COVID tests in the United States.
And like a lot of people at the Broad,
the data sciences platform jumped in
to help with that effort.
And we helped build this solution
rather quickly using a lot of serverless
and a Postgres Cloud SQL database at the core.
ANDY GOODMANS: That's awesome that you
were able to react so fast to the pandemic.
Where do you see the value in using managed databases,
like Cloud SQL?
KRISTIAN CIBULSKIS: I think for us
it largely comes back to that speed of delivering solutions
for our users.
And it comes in two different flavors.
One is a lower barrier to entry.
So our developers can get using these managed database services
rather quickly without needing to go
through some centralized resource that
might become a bottleneck.
And the second is to lower operational burden.
So by relying on these managed services,
we can spend less resource keeping them up and running
and performing at their best, and instead
use those resources to develop new features for our users.
So going back to the global pandemic example
with our COVID testing pipeline, we had about two or three weeks
to come up with a solution with a very small number of people
on the team.
I think it really was two weeks from the very first idea
that the Broad maybe should get into large scale COVID
testing till our first day of production operations.
And on that first day, we delivered 140 COVID tests.
And almost a year of the day later,
we are delivering 140,000 COVID tests a day.
Our Cloud SQL Postgres database at the core of that
has been performing really well.
With a few CLI commands, we were able to enable
high availability and read replicas.
Backups and maintenance upgrades where
transparently performed for us.
And just given the tight timelines we had
and the small team, I'm not sure we
could have confidently enabled all those features
successfully.
ANDY GOODMANS: Wow, that's an over 1,000 times increase
in COVID testing per day.
That's pretty amazing.
KRISTIAN CIBULSKIS: Yeah, it was a huge scale up,
both from a software perspective as well as, obviously,
the lab that's working to actually handle
the physical samples.
It's quite an amazing thing to watch.
ANDY GOODMANS: Can you give us an example
of how you're leveraging our analytics products together
with the databases?
KRISTIAN CIBULSKIS: So as I mentioned earlier,
we definitely use the analytic products,
like BigQuery and Dataproc in the analysis of genomic data.
But a lot of the source of that data comes from large files
and object storage.
I think a really interesting use case
where we see these two working well together is
analyzing cost data.
So in our Google projects, we enable billing exports
that put our billing data into BigQuery data sets.
And we make heavy use of labels to label those resources.
But those labels really aren't enough information
to help our scientists understand their Cloud spend.
So we joined that back off to our relational databases.
So we can then tell a researcher that this VM
they spent $0.17 on was actually used as part of this analysis,
as part of this research project on this particular sample.
And they can use that both to control their costs,
but also decide where to focus their optimizations.
ANDY GOODMANS: That's pretty great.
What is one thing you think other customers should know
about our managed databases?
KRISTIAN CIBULSKIS: Mostly it's that they just work.
You really do just click a button.
And you get a highly reliable relational database
that you can use in production.
We operate in a regulated environment.
And we've got clinical regulations, as well as
Fed ramp for federal data.
And being able to leverage GCP's compliance
story for those products has also been really helpful.
I think the other thing I would add, though,
is that just because you've got these really powerful tools
doesn't mean that no one in your organization
should understand how databases work.
You still have to think about things like data lifecycle,
data sunset policies, schema modeling, and so forth.
And we're learning how to strike the right balance
between enabling a lot of developers
yet still giving them the technical knowledge
they need to deliver high quality solutions.
ANDY GOODMANS: And if you could ask one thing
from us, the Google team, what would that be?
KRISTIAN CIBULSKIS: I think if you had asked me a year ago,
I would have said better insight to the performance
and reliability of the database.
Some of our services were degrading
in terms of performance.
And it was really tricky to get a handle
on what the source of those performance problems were.
With Cloud SQL Insights, I think that's a great new tool that
fills a gap.
And we're really excited to spend some time learning more
about that tool.
So I think if I could ask for something else then,
it would be looking at the maintenance and associated
uptime.
This has been a great story I think so far
with the addition of things like shorter downtimes,
alerts for maintenance windows, and the ability to reschedule
those maintenance windows.
But for things like the COVID testing platform where
we really are a 24/7 operation, those
sort of downtimes that all have a real impact for us.
So having something like zero time upgrades the way
that the serverless products have would be really powerful.
ANDY GOODMANS: That is great feedback.
And we are definitely working on it.
Kristian, I want to thank you again
for sharing with us how you're transforming medicine
by using GCP databases.
Thanks a lot.
KRISTIAN CIBULSKIS: Great, thanks.
And thanks for having me.
RON PANTOFARO: Thanks, Kristian.
Thank you for sharing your story.
And Andy, thank you, as usual, for hosting.
On behalf of everyone here in Google Cloud Databases,
thank you for watching and see you next time.