>> For the purposes of this demo,
please imagine that I'm
an operations manager in a European auto company.
We're going to launch a new line of autonomous vehicles shortly,
and our team of solution architects has been working on
an application to monitor these connected cars.
As you see on the screen,
we have a few hundred connected cars
being monitored already in Europe.
Let's deep dive into one of the cars to see
what type and volume of data we're collecting.
As you can see on the screen,
we're collecting hundreds of gigabytes of sensor data every day.
Now, to support a scenario like autonomous driving,
the data processing engine inside
the car needs to support two things.
First, it needs to be Edge-Optimized to run
really well on the intelligent control unit of the car.
Second, it needs to be powerful enough
to support real-time decision making right inside the car.
Azure SQL Database Edge is the perfect fit for this scenario.
It's still supports native scoring right inside
the relational database to enable real-time decisions.
It can handle tens of thousands of events per second.
It shares the same relational engine
as the SQL Server that you know and love,
enabling a level of consistency for the developers across
the intelligent Cloud and
the intelligent Edge that is unparalleled in the industry.
Best of all, Azure SQL Database Edge,
a fully functional performant relational database now
can run really well on
ARM devices like the one I'm holding in my hand.
We're also streaming a small subset of
the process data in every car to a relational database
in the Cloud to generate operational insights
on every car and across the entire fleet.
Let's take a look at a dashboard that captures
the peak key performance KPIs
of this relational database in the Cloud.
As you can tell, we are currently running
this application against AWS RDS for SQL Server.
More and more cars are coming online in our simulation
and the volume of data being ingested is growing rapidly.
As the database grows,
it's become very clear that
we've reached the limits with the current design,
which cannot handle data volumes greater than 16 terabytes.
As we've reached these limits,
all the right transactions or the updates to the database fail,
which essentially means we cannot add
anymore cars to the fleet being monitored.
This is a very common challenge with all relational databases.
Managing very large relational databases is a complex challenge,
and more often than not,
developers are left with no choice,
but to completely rewrite and rearchitect their application,
with concepts like application level database sharding
which are very complex to get right.
The reality is there is a very clear need for
a relational database that can break through all these limits.
Do you guys think we have such a database in Azure?
Yes, we absolutely do with Azure SQL Database Hyperscale.
Now, let's take a look. I also have
the same application running against Hyperscale.
Let's see how that performs.
As you can tell, you're very easily
able to go past the 16 terabyte limit,
both the read and write performance of
the application workloads are doing
great and the database is healthy.
Well, we're currently monitoring just a 1,000 cars.
What if we wanted to monitor a million,
can Azure SQL Database Hyperscale handle that? Let's take a look.
This is the part of the demo,
I start feeling a little nervous by the way.
We're going from 1,000 cars to a million. Let's see if that works.
Well, a million cars is not
a problem for Azure SQL Database Hyperscale.
As you can tell, the number of cars are
rapidly getting added to the fleet being monitored,
the read and write performance is looking healthy.
Now, of course, it's going to take a while before
all the million cars get to a steady state of data ingestion.
Now, in the interest of time,
we actually run this demo earlier against
the different Hyperscale relational database
and got it to a steady state.
Any guesses on how large
a relational database backed by Hyperscale in Azure
is when it gets data being
ingested from a million cars? Let's take a look.
It's a little over 200 terabytes.
That's 200 terabytes in
a single relational database backed by a Hyperscale in Azure.
Now, you may be wondering what's the magic behind Hyperscale?
There are three key pieces of innovation that
we worked on for the last several years.
The first one, we've completely rearchitected
the core database engine to
separate out the query processing layer,
compute layer, from the storage management layer to be totally
Cloud-native so that each layer can be scaled out independently.
Now, this is an extremely hard challenge to get right for
relational databases that support
high performant operational workloads.
Second, we built out an
intelligent caching hierarchy to
leverage the memory hierarchy and SSDs in a very efficient way,
to get you the best price performance
for workloads of any size and scale.
Finally, we have technology now
that guarantees that databases of any size,
scale, and state can recover in constant time.
We made all these groundbreaking changes
while maintaining complete compatibility with
the SQL Server ecosystem that has served
our customers well for the last 25 plus years.
Now, some of you may be wondering,
what about core database operations like backup
and restore on these very large databases?
Well, backup is a managed service.
We utilized snapshots, it's instantaneous,
and almost has zero impact in the higher bandwidth.
But what about restores?
How long does it take to restore
such a large database backed by Hyperscale?
Well, my friends, it doesn't take weeks,
it doesn't take days,
it doesn't even take hours as you may be used
to as managing these large databases On-prem.
With the Hyperscale architecture,
it takes less than 10 minutes.
Now, finally, we'd like to provide personalized insights to
all our millions of users of these autonomous vehicles.
Now, you can imagine
the amount of query processing power you would
need to actually get that to scale on a relational database.
Let's see if our Azure SQL Database Hyperscale can handle that.
Now, what you're seeing on the screen is
the Azure portal experience which allows me to
create 30 replicas on
a single relational database backed by Hyperscale.
To put that in perspective,
that's 2,400 cores of
processing power on
a single relational database backed by Hyperscale.
With that amount of computing power in
a single relational database, you can handle,
you can easily satisfy the scenario to give out
personalized recommendations to all the millions of users.
Azure is the only public Cloud that supports
scaling SQL relational workloads at this scale and magnitude.
Our customers can build their solutions on Azure with
confidence that we'll be able to
meet their needs today and in the future.
We can't wait to see what kind of
innovation you build on the Azure data platform.