>> You've now learned what Azure SQL Edge is
and some of the use cases and the reference architectures.
In this video, you'll dive deeper into
actually how it works and take a look at a demo.
This week on Data Exposed.
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>> Hi, I'm Anna Hoffman and
welcome to this episode of Data Exposed.
I'm joined once again by the Vasiya,
a Program Manager on the Azure SQL Server team.
Thanks for being with us again today.
>> It's a pleasure to be here, Anna.
>> Awesome. In our previous videos,
we've talked about what is Azure SQL Edge
and we talked about some of the use cases and some of
the architectures and now I'm really excited because you're
going to show us what it
actually looks like and some of the things we can do with it.
Can you tell us like, what are you going to show us?
>> I'm actually going to show how
to use Azure SQL Edge in a wind farm.
It's essentially helping wind turbine operators
to understand if something is going wrong
and if electricity is not being generated properly.
What do we have for the purpose of this demo?
Let us assume that we are the operators of
the Contoso wind farm and what we
have in front of us is a dashboard,
which represents the turbines on this wind farm.
The central unit of each of these turbine
has an Azure SQL Edge Instance running on them.
The Edge Instance is actually collecting data from
multiple sensors located in
the different components or parts of the wind turbine.
These sensors measure various aspects
that could affect the wind turbine,
such as wind speed,
wind direction, oil temperature, etc.
Additionally, data from each of these sensors is
streamed to the Azure SQL Edge for processing and ML inferencing.
Data from each of these instances is further
aggregated and uploaded to a Cloud target.
In the case of this video for this demo,
the Cloud target is actually an Azure Blob Storage.
This is essential for long-term retention in ML model training.
As you can see over here,
an issue has been identified with unit
34 and we need to figure out what's actually happening over there.
We are running a query to understand what's going on.
At an immediate look over here,
we find that there is like
a surge in the power that's being
generated by this particular wind turbine.
But when we look at the vibration,
the oil temperature or if any part has failed of the fire,
none of that is actually happening,
it's just power generation.
You see this pane over here for security alert,
but it shows that none of the events are visible to us,
that's because we are an operator and not a security engineer.
What's in play over here is the security feature of SQL Engineer,
which is Role-Based Access Control,
so only security engineers can have a look
at what's happening in terms of security.
Let's look at environmental factors.
On our left-side, we have
the wind speed and wind direction of the farm,
and both seem to be pretty stable for the wind farm as a whole.
But then when you look at the right side,
which is for this particular turbine,
we see a lot of
turbulence in the wind speed and the wind direction.
This creates a lot of deviation and that's why
the power generation is not happening properly as expected.
What we're going to do now is
actually first see this data that we already have.
There is some issue happening over here.
We need to take that data from Blob Storage,
run an ML model,
train it, and deploy it again onto
our turbine to correct the issue.
Let's look at our ML Studio.
What we have over here is a Jupyter notebook
that we are going to use for training the ML model.
First and foremost, we'll be setting up
our Azure ML workspace and next is where we will fitting our data.
Data here is actually fetched
from our Azure Blob Storage container
that I initially mentioned where we're storing
all of our data from the wind turbines.
Once we fetch our data,
we just collected a subset to see if
we're getting all of the details that we need.
Looking at it, we are getting the details,
and the next thing that we're going to do is actually check if
we have enough amount of data to train an ML model.
When you look at the count over here,
it says approximately 8,000 records.
This is good enough for training a model.
Next, let's just have a look at how
the wind speed and wind direction is
going based on the data that is there.
When you look over here, there is an anomaly for R43,
which is there is a wind over the wind direction angle is at 50,
that's when there is a spike in the turbine when distribution.
If we keep going down,
we have something called as AlterBlades.
When we say AlterBlades,
it means essentially restarting the wind turbine,
or that is essentially altering the blades.
Once the AlterBlades are true,
that's when the wind direction angle is at 50.
From the data what we can see over here is yes,
the wind direction angle is at 50 and there is some issues with
the speed and therefore it needs to be changed.
There are a few more graphs over here that we are currently using.
What we see over here is whenever
the wind direction angle is at 50 degrees,
that's when the turbine wind direction gets turbulent.
There is a huge spike over here and that's exactly
the place where we also see that
the AlterBlades need to become true.
Let me say AlterBlades,
that means that the direction in which the turbine is moving,
that needs to be altered.
We also do carry out
further analysis in terms
of what is the average wind distribution,
what is the wind speed.
What we can see over here is
another measurement of standard deviation which again, spikes.
Every time there's a spike is when power generation drops.
What you're going to do based off of looking
at these graphs is essentially create an experiment.
We will be creating and running an ML experiment.
The process of creating and running
an ML experiment takes about 5-10 minutes.
But in the interest of time over here,
I'm going to use an already run ML experiment and
take the SAS URL from
this experiment and put it in our sensor Module Twin.
Let's go ahead and do that.
We will be generating a SAS URL.
Let's copy this.
When it comes to deploying
Azure SQL Edge through IoT Edge on IoT Hub,
we use something called as Module Twin.
When we say Module Identity Twin,
what happens here is this is a replica
of the sensor that is already been on your wind turbine,
that's created over here.
When we paste this SAS URL in this particular place,
what happen is the trained ML model that was being stored
in our Blob Storage is now being saved into this Module Twin,
and as you can see that it's been a success.
When we go back over here and hit "Refresh",
you will see that the wind speed of
the turbine and the wind direction has been rectified,
and therefore the power is now being generated as required.
With that, we are at the end of the demo,
which talks about how to leverage Azure SQL Edge in a wind farm.
As we have been discussing in the past couple of videos,
what I can say is Azure SQL Edge simplifies
Edge architecture and IoT application development
for customers by supplying
a small footprint data engine that
runs on a choice of platforms in Dell or
AMD64 architecture or ARM64 architecture
and delivers consistency in data,
tooling, and security from Edge to the Cloud.
It's powerful enough to run offline,
but with the capability to connect
seamlessly to a broader network of services.
Azure SQL Edge also includes built-in ML capabilities,
support for time series data,
and native data synchronization with SQL servers,
Azure services such as SQL database, etc.
Finally, with Azure SQL Edge,
customers have a small but powerful data engine
that meets the challenges of IoT
which empowers businesses to use
and analyze data collected at the network Edge.
You save costs, drive innovation,
improve safety, and gain competitive advantage.
What you see here are a list of
additional resources to learn more about Azure SQL Edge.
>> Thanks Vasiya. That was a really powerful demo.
I really like the way that you took us from end-to-end.
Where we're analyzing and monitoring
our wind farm and we see there's an issue,
we are able to look at the data
almost on the fly of what's happening down at the Edge.
Then we actually went and you retrained the model
and that was in Azure and then you deployed it back to the Edge.
It's a really powerful end-to-end story
of what's possible with Azure SQL Edge.
I really thank you for coming out on the show.
>> Thank you so much, Anna.
To our co-viewers over here,
Azure SQL Edge is currently in public preview,
so please feel free to go ahead
download the bits and test it out for your various scenarios.
You can definitely reach out to us through one of the links
provided and which I believe will be
provided in this video in the description links.
Thank you so much for having me here Anna.
>> Awesome. Yeah. We hope you
check it out and if you like this video,
please like our videos,
subscribe to our channel and let us
know what you're doing with Azure SQL Edge.
Thanks for joining us on Data Exposed.
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