>> IoT is a new and hot topic and
Azure SQL Edge addresses a lot of
the challenges that IoT brings to the table.
Learn how in this episode of Data Exposed.
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>> Hi, I'm Anna Hoffman and welcome
to this episode of Data Exposed.
Today, I'm joined by Vasiya,
who is a Program Manager on the Azure SQL, SQL Server team.
Welcome and thanks for joining us today.
>> Thank you so much for having me here, Anna.
>> Now, Vasiya, you've told us a lot about how there's
a lot of data in IoT and
there's a lot of things happening at the Edge.
I'm sure there are some challenges that come with
this volume of data and also doing it in a disconnected state.
Can you tell us a little bit more about those challenges?
>> Of course. When we look at,
as I mentioned in my previous slides,
there were about 180 zettabytes of data
that we are foreseeing we'll be collecting by the year 2025.
That definitely changes this IoT-driven data landscape.
The challenges are two-fold over here,
one being network and
infrastructure and the second being data that is collected.
Let us look at network.
IoT devices are heavily dependent on
network but network availability varies across the globe.
We have low bandwidth, highly latency,
low network availability, and
high cost which all play a crucial role here.
Well, [inaudible] is aimed at solving some of these problems,
it will not be addressed globally.
Just take the case of
a ship which is in the middle of the Pacific,
there are huge stretches over
there without Internet and the only Internet that would
be available is the expensive satellite Internet
and that's not reliable at all.
When it comes to data,
there is this need to process it at the origin
of where it's being created for quick decision-making.
With the increasing volume,
transmitting data constantly is also expensive.
Added to that, there is a layer of complexity
when it comes to storage and the security of data.
This is where Azure SQL Edge comes in.
Azure SQL Edge is one step
closer towards solving these challenges.
It is a database that is optimized for IoT deployments.
It's built using the same SQL engine that is
present on SQL Server and various other Azure SQL offerings,
which means that it provides
the same industrially leading security and performance.
It also provides the same Developer and
tuning experience all of us know and trust.
Azure SQL Edge can run on devices
that have at least a minimum of one core.
The range of devices is from IoT devices to Edge service.
Further, Azure SQL Edge supports a connected,
disconnected, and a hybrid scenarios.
Let's dive a little deep into the number
of new features that are being introduced with Azure SQL Edge.
First and foremost, I would like to say that Azure SQL Edge is
a fully containerized solution that is
compatible with most Docker Container Engines.
It runs not only on 64 architectures of Intel or AMD devices,
but also, on ARM 64 devices.
When you look over here,
you find that SQL Edge includes native support for data streaming.
That is, it helps to capture data to
and from multiple input and output targets.
This is the same engine that covers Azure Stream Analytics.
Second, time series processing.
This is another important feature of Azure SQL Edge.
When we say time series processing,
it comprises of already existing capabilities
in Azure Stream Analytics such as anomaly detection.
We also have designed
T-SQL functions and syntax to query time series data.
Next, the most spoken about
ML inferencing capabilities in Azure SQL Edge.
What we have now on Azure SQL Edge is
ML models can be trained in the Cloud and on
any other On-Premise devices and deployed to
the Edge where inferencing can be
done on data that is collected over there.
Finally, there is also native data movement.
By native data movement what happens
is we ensure seamless integration to
Azure and make use of
the rich ecosystem of
data products that are made available to the customer.
What happens over here is data from the Edge
can be sent to targets such as the Azure Blob Storage,
Azure SQL Database, Azure Cosmos DB, etc.
This anxious consistent app development
and management from Cloud to the Edge.
We're just looking at developing once and deploying anywhere,
be it on Cloud or on Edge.
Finally, when it comes to security features,
we have data encryption as well as
classification and access controls.
>> Thanks for showing us this, Vasiya.
When I think about it, what you
did and I really like the way you showed it to us.
You showed us the challenges that IoT
faces and poses for all of data professionals.
Then you showed us how
Azure SQL Edge is really doing a lot to address these things,
whether it's streaming, whether it's machine-learning.
It seems simple but it's really a big thing,
it's just being able to use native T-SQL and the SQL engine
that our community knows and loves, really powerful stuff.
>> True. This is something that given the amount of
data that is being generated and what we see going
forward with IoT is the next big thing,
analyzing that data and giving timely insights is what
will add a lot of value for
customers and businesses going forward.
That is exactly what we are
trying to solve with Azure SQL Edge here.
>> Awesome. Well, thanks so much for coming on the show today.
We do have a series going.
In the next video of this series,
Vasiya is going to dive into some actual architectures and
reference architectures that you can use
and architectures that our customers have used.
Again, thanks for joining us today
and we hope that you'll like this video,
leave us a comment of what you're doing with Edge
and we'll see you next time on Data Exposed.
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