(upbeat music)
Hello and welcome.
Please enjoy this short readiness video
on Azure Database for PostgreSQL.
With us now we have Dan Alpert and Chinmay Kanchi.
Welcome to the studio, Dan and Chinmay.
Thank. Thanks for having us.
How are you doing?
Really good, how are you?
Great, so can you please tell us more
about your role at Microsoft then?
Sure.
So, I came by way of the Citus acquisition about a year ago.
I'm a product specialist in the Open Source Division,
in product and engineering and I really specialize
around Postgres and Hyperscale Citus.
Fantastic, and I know, I'm really excited
to have Chinmay with us, one of our customers.
Yeah. So, can you please tell us
where do you work and what do you do?
Yeah, I'm Principal Engineer at Enlyft.
We're a technology start up and I work primarily
on building large distribution systems to process the data
that we get and also perform machine learning on them.
Fantastic!
We always love having customers in the studio.
So, thank you for being here with us.
All right then, I would like to start with you
and can you please just briefly tell us
what are you gonna talk about?
Yeah, so today we're gonna walk through a little bit
around Postgres and, of course, Hyperscale Citus.
Of course we're gonna talk about who Enlyft is
and what their workload is and then why they chose
to work with us here at Microsoft
and Hyperscale Citus. Yes.
All right, let's start with Hyperscale then.
Sure.
So, it's kinda hard to talk about Hyperscale Citus
without giving a little bit of a nod
to the Postgres community.
So, Postgres itself, second most popular database
in the world and the fastest growing database in the world.
Postgres has been around 22 years, 22 plus years.
It's a huge community that continues to do a lot
of investment and contributions to the platform every year
with major annual releases.
Nice thing about the platform is not only is it
the most popular open source database in the world
and the second most popular database in the world,
but it's great at multiple types of workloads.
It's great at multiple types of data.
So, not just structure data but you can actually use
semi-structured and unstructured data.
Which also leads towards,
Chinmay will mention a little bit later,
around the popularity at their organization with Postgres.
Now, it's hard, the one thing about Postgres though,
is it's single node.
So, it can be compute, MOM
or even storage eventually can create a bottleneck
in that single node setup.
So, that's where really Hyperscale Citus comes into play.
You add us onto a vanilla Postgres node,
as you would any other extension.
And Postgres has a ton of extensions available to it.
But, now you can take that single node up to hundreds
of nodes, thousands of cores and petabytes of data
in a single Postgres cluster.
So, really you can scale it to whatever your needs are,
depending on the workload and the use case.
Not only this, but it enables multiple types of workloads.
So, OLAP analytics, transactional and many types
of workloads but at scale and with speed.
And the way that we do it with speed is,
if your familiar with Hadoop and MapReduce,
we're that but for the SQL world.
So, from a parallelism standpoint,
we're able to efficiently,
the Citus logic efficiently uses the resources
within all the nodes to be able to run parallelism
on the queries so that it returns faster results.
Okay, awesome.
So, now Chinmay, could you please tell us
about your organization and the platform.
So, as I said, Enlyft is a tech startup.
We're based right down the road in Bellevue.
And what we do is we help B2B companies
with their customer acquisition needs.
So, if you think about the way sales and marketing works,
the top, the very top of the funnel,
prospecting and trying to find your early, sort of,
target audience.
That's historically been really inefficient
and very data deficient.
What we aim to do is change that.
We profile tens of millions of organizations.
So, using billions of publicly available documents
and we find out what technologies those organizations use.
Whether they're really primarily a fast shop
or are they moving to the cloud?
Or do they still have a large on Primer ID infrastructure?
So, stuff like that.
And we then combine that with our customers' own sales
and marketing data and build really sophisticated
machine learning modules on top of that.
Microsoft actually is a customer as well.
And we've seen conversion rates of up to 2x or even 4x
using our platform versus just going blind.
So, yeah, that's what we do.
Fantastic.
So, Dan, I know you rolled with that.
So, okay, please give us an overview of the use case.
Yeah, so the use case for Enlyft and Chinmay's team
really falls into three workloads.
OLAP from an analytical standpoint,
they need to be able to serve back realtime information
to their clients and, of course, transactional, so OLTP,
be able to ingest from the thousands
of different data points that they're bringing
into their system.
And they'd like to do this in a near realtime fashion.
And then, of course, their SaaS application.
So, as a future kinda leaning on the front
of your skis kinda thing.
Having the ability to have to see their
in a multi-tenant set-up or as tenant isolation
for a lot of clients, or noisy neighbors.
Hyperscale Citus is able to do all three of these
in a single database.
Enlyft needed a fully read/write database at scale.
They also needed this in low terabytes
from a data size standpoint.
But also needed this to return results realtime.
Previously, Enlyft had used another database solution
to provide the read part of this and they were getting
the performance speeds that they wanted
from a read standpoint but the writes weren't scaling
with this other database.
The overwrites, those are the difficulties.
That's right, that's right. (laughing)
And it also created a little bit of manual effort
on their part, pulling it out of one database
to putting it into another database from the read,
you can feel to provide that OLAP, that analytics
and the speed that they needed to for their clients
and Chinmay will go through that, here in a little bit.
But what Hyperscale does, is it really delivers on both
of these, fast reads, fast writes, in a concurrent way.
So, and of course, keeping in mind multi-tenancy
as a continued role.
I see, I see.
So, Chinmay, what brought you to have interest
in Hyperscale Citus and Postgres?
Yeah, so, we've, as an organization and as a start-up,
we've always really strongly believed that regardless
of the pain that we might face,
we have to be on the right technology all the time.
So, we've switched databases
as our needs changed, pretty aggressively.
But, somehow, once we started using Postgres, about,
I don't know, four, four and a half years ago,
it just stuck in our organization.
And we really love Postgres,
A, because you get all the SQL stuff;
you also get really really robust support
for unstructured data with JSON-B.
And, also it's open source which we absolutely love.
As an organization, we're really into opensource.
So, that was primarily why we ended up using Postgres.
But, for certain use cases, Postgres wouldn't scale
and there we would use other databases, you know,
something NoSQL that kind of stuff.
And what ends up happening was,
you have this really painful migration that was run,
I don't know, weekly or whatever from Postgres
to your NoSQL database.
With the Citus, we actually started looking
into Citus even before the Microsoft acquisition
was announced.
But, we were, for a smaller organization,
we were very wary of the amount of devops
and management overhead that you could have.
And so when I heard Craig speak at Belle
about when you guys launched a public preview,
we just jumped on it straight away.
I see.
Well, there's a live question from the audience.
What unstructural data did you have?
What type of unstructures were you looking at?
See, it's mostly JSON data.
We have some scheme around it internally
but we like to keep that really flexible.
The others were kind of unstructured data
that we have as we actually synthesize client information
with our own data.
And that can be completely Schemels.
Yes. So, having a database
that can actually do those things
and do drawings against our data,
which loads of other databases can't,
that was a big win for us.
That's awesome.
All right, so, I would also like you to speak
to the results that you saw about by up Hyperscale.
Yeah, so, as Dan was mentioning,
we retained comparable performance to our previous solution,
which was what we really wanted.
But, we were able to scale our writes pretty significantly.
So, I think our bigger job for our data use used to take,
I don't know, 36 hours and now it takes three.
So, we got really significant wins from that.
Yes.
And on top of that, I think, just the fact that,
as I was saying, you have client data
and because you can sort of use SQL semantics on top
of that, and join it to data and sort of display it
in innovative ways, it really has made our app
far more powerful and enabled use cases
that weren't enabled before.
I see.
Well, that was great.
And Dan, any closing thoughts?
No, well we appreciate the opportunity Chinmay
and thanks for having us.
Of course. Yes, thanks.
Thank you so much Chinmay and thank you Dan.
Thanks for watching this short Azure Readiness video.