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>> Hi, I'm Rony Chatterjee,
I'm a Senior Product Manager in the Azure Data team.
I work on the SQL server product and
today I'm excited to show you what we have
built as a data visualization experience
in Azure Data Studio. Let's get started.
In Azure Data Studio,
you have a product which gives you
a data visualization as well as an
experienced to work over data anywhere,
whether it's data on-premises as well
as data inside Big Data Clusters.
So in this addition of Azure Data Studio, as you can see,
I have a SQL Database Edge and
a SQL Server Big Data Cluster I'm connected into.
Now, in Azure Data Studio is built on top of extensions.
So one of the things with Azure Data Studio provides is
an ability to install
any extension which you might like for your data operation.
So here, one of the extensions which we are going to
install today is called SandDance.
SandDance is a data visualization experience.
It provides unit visualization for the data
you would like to explore and visualize in SandDance.
So here I'm installing the SandDance extension,
and as you can see,
the SandDance extension was just installed.
So let's go back to where the data is.
So I have data inside the SQL Server Big Data Cluster.
The SQL Server Big Data Cluster comes with
HDFS with the Big Data Cluster
where we can store high volume of data.
One such data which I have stored in HDFS for instance
of SQL Server Big Data Cluster is a demovote.tsv.
So now let's take a look at how this data looks like.
So if you do a quick preview of the data,
the datasets opens from HDFS,
and as you can see,
this is nothing but voting data.
It has the voting data during the Obama,
Romney time frame of
elections which happened across the United States.
Now, if I have to process this particular data set and give it to
my data scientists to actually make sense from the data,
he would have to actually build some reports,
and whether he uses
data visualization library structural to view it,
or he uses some code
to actually visualize what the data looks like.
But in Azure Data Studio,
we have our right-click option of view in SandDance.
Let's take a look at how this data looks like.
Now, you can see that SandDance is loading up,
and SandDance actually auto figured out that
this data has latitude,
longitude, and scatter plot would be
the best chart to choose for plotting this particular data.
So we have a recommender chart type built inside SandDance.
So now this data looks good.
I can see that the map of the United States,
as expected because the voting data,
and have the x-axis and the y-axis as well.
Now, still I don't know if there are
more information in the data and I could
obviously filter in terms of the x-axis and the y-axis,
but wouldn't it be great if I could actually plot this in
a three-dimensional space to actually
see which one might be the best classifier.
So let's make this graph a three-dimensional one.
So if I click at "SandDance" and if I
click the "Three-dimensional one," now I have the z-axis.
Now in the z-axis I can choose income and see
across the United States what
the income demographics of the people are.
You can see that Washington is doing good,
Delaware is doing a little better,
and then in New York and Miami is great as well.
But let's take a look at,
in terms of the median home value,
how are the prices looking?
You can see that the graph automatically adjusted itself.
You could also have a various color schemas you can use.
So I could actually see, income,
and you can see that I can choose
different color schemas to actually color the graph.
I could also easily change the bincount size.
So you can see that as I change the bincount,
the graph dynamically just changes completely.
Now this is good.
I can also visualize the data,
zoom in, zoom out, but now,
I would like to see if the data set is giving me some way of
where I could predict
some anomalies if that exists inside the data.
So now, let's start by doing some search on top of the data.
So I'm going to search where income is less than,
say $ 40,000, and also add another expression,
and say, median home value is greater than $ 800,000.
That seems to be a good check.
Let's see if we can find some data set which
actually satisfies this needs.
Now, if I actually do a search and select,
you can see that SandDance has couple of
data points which it actually picked it up.
Those are shown in the graph.
I could easily isolate these data points,
and now I just have
a subset of these four data points to consider.
You can see that there's a person in
San Miguel County who has bought a house of
$ 812,500 with an income of $ 39,070.
So SandDance actually give you unit visualization of
the data you are trying to explore and make sense from the data.
You could also do other things
like where you could take a snapshot of the picture,
you want to create a snapshot view,
you could actually create a snapshot,
and it creates a snapshot view.
Now you can embed this snapshot view in a document
which you want to probably send it over to
someone else to take a look at as well.
So this is the way we are providing
data visualization in the context of
the data you are operating in.
Now, also one of the things we have added
inside SandDance was the ability to visualize the query results.
So here I have a database I have created,
and in this particular database I have a sensor data table.
So now if I do select a 1,000
from that particular table and I could
also click on this chart
here and load this data in the SandDance as visualizer,
though this is just the sensor data and it does not have
as interesting as the voting data is but still,
this gives you a quick view of what
the data visualization in the context of the query could be like.
Now in this particular video,
you have seen how we are using SandDance to actually
quickly visualize the data and make
meaningful trends and understand
what the trends are happening from
the data so that it can help you in
building advanced machine learning algorithms.
Thank you so much for listening in today.
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