>> You've learned what Azure SQL Edge is.
In this video, you will dive deeper into
the various architectures and use cases that
customers are using Azure SQL Edge for,
in this video of Data Exposed.
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>> Hi, I'm Anna Hoffman
and welcome to this episode of Data Exposed.
I'm joined again by Vasiya,
a Program Manager on the Azure SQL, SQL Server Team.
Thanks for coming back on the show with us.
>> Thank you for having me here, Anna.
>> On the previous video,
we talked about what is Azure SQL Edge.
If you haven't seen that video already,
we'll put a link to it in our description,
you should definitely go check it out.
Now, in this video,
we're going to dive a little bit deeper into some of
the IoT reference architectures
related specifically to Azure SQL Edge
and even a customer use case.
Vasiya, why don't you tell us a little bit about some of
the architecture possibilities with Azure SQL Edge.
>> Sure. As you all know that there are millions of
messages that are being generated by the IoT devices,
especially from the sensors installed on these devices.
By devices, it could be anything.
It could be retail stores,
smart factories, automobiles, etc.
If you look at the architecture here,
you will realize that the goal of Azure SQL Edge on a whole
is to collect all this data that's coming from these sensors
and to analyze this data.
As you already know, Azure SQL Edge
is a containerized application which
runs on the same SQL engine that powers the SQL Server
and various other Azure SQL offerings.
The streaming engine that you see here
is the Azure Stream Analytics,
which helps us with many tasks such as windowing,
transformation, anomaly detection, aggregation.
All of these play a key role in
Azure SQL Edge's time series capabilities.
The trained ML models that are
deployed to the Azure SQL Edge instance
will monitor functioning of components in
either a connected or a disconnected scenario.
For the purpose of this example,
we are looking at a connected scenario.
Finally, data after processing,
analyzing, can actually be moved to the Cloud.
In the case of a connected scenario,
this happens through the IoT Hub.
The reason why we move data to the Cloud is for
long-term storage and for further processing.
From storage services, data can also be sent
to Azure Machine Learning for retraining of the models.
These updated models can then be sent back to
Azure SQL Edge to improve ML performance for the IoT applications.
The other thing that you can see is,
from these storage devices,
business analysts can use Power BI to analyze the warehouse data.
Also, finally, web, mobile, and other applications
can be built on the serving layer as well.
For this, you can expose APIs
based on the storage data for third-party users.
>> Awesome. This is really cool.
What I think is most powerful here is that we've taken
a lot of the technologies that you saw on the left side
and moved them from being
Cloud-only to being able to be actually used at the Edge.
Things like Cognitive Services and Azure ML were not
things previously that you could really run at the Edge.
So that's really cool.
What about customers?
Do we have some customers that are
already implementing with Azure SQL Edge?
>> Yes, we do have a couple of customers that are already
using Azure SQL Edge to solve a lot of their business challenges.
I would actually want to talk about one such customer, Fugro.
Fugro is a geo-intelligence provider.
They help clients in the energy sector
gain vital insights especially for meeting projects.
To support its clients in early project phases,
Fugro sends a team of oceanographers,
meteorologists, etc., to collect
environmental data such as water depth,
wind currents, waves, temperature.
This data which is collected on ships through sensors
is then stored on the ships, and until recently,
the marine data that Fugro collected
offshore was made available via a satellite link
to the company offices that are on dry land.
So this data acquired over the satellite was then
imported into Microsoft SQL Server on-premises,
and then the Fugro team would run analytics on
this collected data to derive insights for the customers.
So one would say, end-to-end,
this entire process took on an average two weeks
from collecting data to finally giving insights to customers,
and this was not ideal for Fugro.
So there are multiple challenges that this company faced.
One, they were not able to run real-time insights.
Most of the analytics happened
only after the ship had reached the coast.
During this entire store exploration process,
the ship had to store hundreds of GBs of data,
which is not a cheap affair.
It's a very expensive to store that much data.
So the company actually set about to fix these problems,
and this is what you see here in terms of the architecture.
As we discussed initially,
they were all on SQL Server premises,
and what they did was they actually moved
the data into Azure SQL Database.
That was the first step towards moving into the Cloud.
Then they also wanted to use the network edge.
For that, they deployed Azure SQL Database Edge on
various ships and buoys that they
use on their unmanned vessels and things for collecting the data.
Then through this process,
the team actually devised a new process,
in which Azure IoT Edge and Azure SQL Edge
were deployed locally at the offshore project site.
At this time, they actually used unmanned vessels.
They didn't want to send their oceanographers over there
because they believe that there are
actually trained models on Azure SQL Edge,
which will collect the right data from them.
Owing to this Azure SQL Edge,
the team was able to ingest queries stored
and also process time series data locally,
thereby saving the company significant time.
From what the company told us,
they now get data within eight minutes.
That's actually from two weeks to eight minutes.
There were a lot of cost benefits
associated with latency and bandwidth usage.
So this is one of our customers
who's currently using Azure SQL Edge.
>> Wow, that's a lot.
I mean, just that two weeks to eight minutes,
if you think about how much faster you can innovate and make
decisions when you're getting reports in much faster fashion.
Thanks for sharing that with us.
We talked about this very specific use case
and we talked about the general architecture,
what about other scenarios?
What other scenarios is
IoT and things like Azure SQL Edge good for?
>> So when you look at sample use cases for Azure SQL Edge,
it's not restricted to a particular industry or
a particular segment of manufacturing or anything.
It actually is pervasive.
It's everywhere.
That's why when I initially
said that IoT devices are all around us,
which means data is constantly being generated,
some examples, but not limiting to this,
are manufacturing smart factories.
When you look at manufacturing,
the fact that factories are located in places where you
might not always have
Internet connection to send and process data,
so you need local processing as in when needed.
Defense, another key place.
Oil and gas, automotive.
Automotive, one need not talk more about automotive,
given the fact that we have autonomous vehicles,
driverless vehicles, the number of data points one has to collect,
the sensors that are put on these vehicles.
Healthcare.
The current situation with COVID and all those things,
having Edge do most of the work
for healthcare purposes, that's super important.
Factories which are providing for these healthcare officials,
retail, finance, aerospace, smart buildings and homes.
These are just a few of the cases that we thought about,
but it doesn't stop over here.
Azure SQL Edge is there wherever IoT is.
In short, I would like to say
Azure SQL Edge is not just
limited to the use cases that are shown here.
Azure SQL Edge is there wherever IoT use cases exist.
>> Awesome. Thanks, Vasiya.
That's really powerful, and I really like what you said there.
I like how you showed us these different use cases and industries.
But I think you make a really valid point,
that we're not really tied to a specific industry.
As time goes on and we become more of a digital type of world,
which I think that's the direction we're actually already in,
this is going to be really important.
So thanks so much for sharing that with us today.
This is part of a series,
so we do have one more video where Vasiya is going to
go into an actual demo and show us how this thing works.
So be sure to tune in for our next episode.
But in the meantime,
please like our video.
If you liked the video,
subscribe to our channel,
leave us a comment, and we'll see you next time in Data Exposed.
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