>> [MUSIC].
>> Hi. I'm Vicki Harp with the SQL Server product team.
I'm here today to show you Notebooks and Azure Data Studio.
So Notebooks are a concept in
Data science which has been
used to do a lot of data visualization,
data exploration and data work,
primarily in the Python language.
When people are talking about Notebooks,
a lot of times we're talking about Jupiter Notebooks.
So the implementation of Notebooks
that we have in Azure Data Studio is
a Jupiter Notebook with a custom front-end that's
better tailored to fit within the Azure Data Studio experience.
So first, I'm going to show you a simple Python Notebook.
This is a Notebook that has
Python language and you can see we've got
a section up here which is
a human-readable text and a section down here that has code.
So if I hit run on this,
this particular code calls out to
the Internet and pulls down a random photograph of a dog.
To show you around the Notebook experience,
we have a picker here,
we can choose the language.
So in this case, I'm running Python 3,
and I'm running it on my local machine.
I could instead move over to the language SQL and then
pick which of MySQL server machines that I want to attach to.
I've got MySQL servers listed here.
So to take it a little bit further,
I'll show you an example of how you might use this.
So here I've got a SQL Server 2019 Big Data Cluster which
is SQL Server running in Kubernetes with both SQL and Spark.
So here in my HDFS section,
I've gotten this directory full of CSV files.
If I take a look at that, preview it,
you can see that it's a lot of
information about dog license information.
So this is actually the Data Dump of
the Allegheny County in Pennsylvania
dog licenses for the last several years.
So using the Spark language and connecting
to Python using Spark on the SQL Server Big Data Cluster.
I'm able to run,
code to read those files directly out of CSV.
Then I can do some analysis.
So in this case, I'm going to get the count of the rows.
I'm going to list the distinct list of just the names,
so we have 25,000 names.
Then I'm going to pull a random name out of that hat.
So we have a name here.
Now, if I was using a Spark,
if I was very used to using Python, that would be great.
But if I'm more of a SQL person,
then maybe I want to connect to that same data-set,
I will run cluster using the SQL language.
Here, I've created external table over that file format.
This is something that you can do using a wizard.
I'm going to go here,
create external table from CSV files.
Then, that would create this external table that I'm using.
But in this case, I've done it using code.
If I hit run cells,
you can see that I'm also using SQL
to access the same data that I just accessed using Spark.
Now, these files are saved as IPYNB files,
which I can then share with my colleagues and I can run again.
So in the case that I wanted to run this today,
send it over to my colleague,
they could open it on their instant and
attach it to their own cluster or
to my same cluster and run it again.
We're also using Notebooks in a few other ways.
Here, we have something called Jupiter books.
This is a list of basically Notebooks
that had been compiled together into a chapter page format.
So here we have a SQL Server ML services course,
which is shown through Notebooks.
So we've got all of the documentation present.
We've got the code samples here and then you can,
at the end of each chapter,
either hit the next button to go to the next
one or use this chapter browser to pick it.
We've also got the supportability book
for SQL Server 2019 Big Data Clusters.
If I brought up the command pilot and pick
this Jupiter books 2019 guide,
that's what I use to open this up.
In here, we have all of the information that you might
need in order to support your new Big Data Cluster,
a lot of the Kubernetes Commands, the cluster information.
This is something that you can run,
you can send to someone else,
and you can save and do what you want with.
So we are very excited about Notebooks in Azure Data Studio.
We really think that this is something that will be
very useful to our community.
We're really interested in finding out how
you're using it and what you want from us.
So if you are interested in learning more, go to GitHub,
look for Azure Data Studio and you
can open up feature request inter bugs.
That's also where you can download both the
stable and the insiders addition. Thank you so much for your time.
>> [MUSIC]