>> Hello and welcome to the Power BI, July 2021 update.
In this video, we'll explore what's new
this month and see some demos of the highlights.
We have some exciting new features
for you in the Reporting section.
First, we're excited to announce that
small multiples is now generally available.
Thank you for all of
your feedback throughout the preview period and
please continue to send us
feedback about how this feature can be improved.
We've included two improvements
to the feature this month.
First, we've improved
keyboard navigation and screen reader support
for small multiples.
Moving focus around the small multiples grid
should now be more consistent and intuitive
and screen-reader readouts should be descriptive.
Second, we've introduced sorting functionality
for your small multiples,
allowing you to sort the order in which they
appear by the measures in your field wells.
This is useful for cases like
seeing the highest cumulative value,
small multiples first, and
will help you make more useful comparisons.
We've also added conditional formatting
to various properties across our visuals.
Now, in addition to all of
the properties which already
supported conditional formatting,
you'll find entry points for color and
text formatting in new areas as well.
Check out this month's blog post to learn more.
Next, Power BI's built-in visuals
now include the Power Automate visual.
The Power Automate visual allows users to go from
insights to action without
leaving the context of the report.
Back in April, we released
a preview of this visual to AppSource.
This month, you'll now find the
visual in the visualizations pane by default.
Next, sensitivity labels in
Power BI Desktop are now generally available.
With Microsoft Information Protection sensitivity labels,
creators can easily label and protect
sensitive content all within the desktop app.
With this release, creators will be able to republish
PBIX files without overriding
sensitivity labels on destination datasets or reports.
This is useful for cases where
the sensitivity label applied on
the PBIX file doesn't match
the sensitivity of the data of the target in the service.
Whether it's because the data sources are
different or because the report in the service
is applied with different sensitivity labels
than the one in the dataset.
Starting this month, Power BI
reports will also now inherit
sensitivity labels set to
Excel files when getting data from them.
Excel files are some of
the most popular data sources for Power BI reports.
When you get data from protected Excel files,
the downstream Power BI dataset and
report will inherit those labels as well.
In the Analytics section,
we're happy to announce that we will soon support
sharing synonyms for everyone in your organization.
Field synonyms are an important part of Q&A,
because users often have a variety of
terms they use to refer to the same field.
For example, total sales,
net sales, total net sales.
Now you can leverage your coworkers' synonyms and
minimize duplicate efforts when
adding synonyms to your models.
We have some exciting new updates
for you in the Modeling section.
First, the new model view UI is now generally available.
Thank you to everyone who provided
feedback and your continued support.
To see all of the new features that were added,
you can visit one of our previous blog posts.
Please keep in mind that if you are using
a report that you haven't
already upgraded to the new model view,
it will automatically be upgraded for you now.
Next, we're continuing our work on
DirectQuery for Azure Analysis Services
and Power BI datasets by improving
support for the flow of properties to your local model.
If the remote model you've connected to has
date tables or auto-generated date tables,
those will now show up in your local model.
Further, if you're remote model has lineage tags defined,
any renames on the remote model will show up in
your local model without
the renamed objects being deleted or recreated.
This also means you won't lose
any local customizations on
objects that have been renamed.
In the Data Connectivity section,
we're excited to announce the release
of the Amazon Athena connector.
Athena is an interactive query service that
makes it easy to analyze data stored in Amazon S3,
and other relational, non-relational,
and custom data sources through federated queries.
Using the Amazon Athena connector for Power BI,
you can now query, analyze,
visualize, and share insights from
your data lake with teammates across your organization.
We've also updated the Databricks,
Dremio, and MariaDB connectors.
We have a variety of
updates for you in the Service section.
First, streaming dataflows is now in preview.
Streaming dataflows allows every business analyst
to work with streaming
data with beautiful drag-and-drop, no code experiences.
Users can now connect to,
prepare, and visualize real-time data to create
end-to-end streaming analytics solutions
directly in Power BI.
Miguel will show us a demo of this exciting new feature.
>> Let's take a look at streaming dataflows.
As you can see here, I'm on
my premium workspace in Power BI.
If I click on ''New'', I'm going to have
a new option called Streaming Dataflow.
I'm going to click on it and we're going give it a name.
In this case, we're looking at toll data,
so we'll call it Toll.
Let me show you now the authoring errors table,
there's going to be three key parts here.
When you click on any of the warnings or the errors,
where you have all of your inputs,
transformations, output, and of course the save button.
Number two is the Data Preview tab where you can also
access authoring errors and runtime errors in a table.
Number 3 is a blank canvas where you
where you can select,
Let's start adding an input,
Event Hub, which is one of the ones that we support.
We'll paste the connection string and click ''Connect''.
You're going to have two options here.
You can add fields manually or
we can auto-detect, like I'm doing here.
You get the live preview right away.
You can actually see the data coming
in as you connect to that source.
The other thing that we can do here is we can remove,
rename or change the data types of the inputs.
In this particular case, we have the entry time,
so let's switch that datetime,
to make sure that we can use it after.
We also support nested objects as you can see here.
Let me show you now the authoring errors table,
where let me add another card so we can see more errors.
When you click on any of the warnings or the errors,
you will pre-select the card so you can fix it.
Now, let's add some transformations
so we can see what we can do.
Let's start with managed fields
which is the most straightforward one
where you can select,
for example, the objects in
that nested object field like the car model.
Or you can rename, basically, you can
do whatever you want with those fields.
Let's start adding the ones
that are under that current model.
Let's go with make. Let's go with model itself.
We also have vehicle type,
so we'll add that too.
That way, we are parsing that nested object so we have it
available in the table when we want to do some analysis.
Last but not least, vehicle weight.
Then I can rename, let's say, weight.
Now that I'm happy with this,
basically, we can proceed and add,
for example, an output right away,
which is a table that will go directly to Power BI.
Let's connect the nodes.
Let's make sure that we give a name,
AllData, because it's all the data that we have.
As you can see here,
each card will give you
a glimpse of the logic that you have,
depending of course, on where that transformation is.
I can also zoom in and zoom out.
Let's actually take a look at the preview that you
can get once you have some logic in here.
You can click on ''Get Started
Preview'' and streaming dataflows
will basically run a simulation
or a sampling of that data so you can see it.
Of course, you always have the light preview
where you can see the actual record,
or you can pause it
in case you want to see something specific.
Let's keep going now. Why don't we add
another transformation. Let's do a "Group by".
This is a very interesting one because this is where
streaming becomes unique with the time dimension.
We can select the aggregation,
we have a bunch available here.
Why don't we do something like "Count"
and let's do "License plate."
Something very straightforward, counting
the cars going through the toll.
Then we will have the time window
here where we have several options,
we'll do "Tumbling", and we'll count
every five seconds the cars that are coming in.
Same as before, we'll put
the output table here, we'll name it,
"Count 5 seconds", and then, as you can see,
we can add multiple outputs to the streaming data flow.
Let's do one last one, also very straightforward,
it's going to be a filter and maybe we'll do "Make"
and we'll filter all the cards that are "Make Ford".
Also, we can do any of those are
the options in a filter, very straightforward,
and we'll add the last output table that we have here,
we'll call it a "Ford cars".
Now, let's run preview
again just to make sure that everything is working.
When you run the preview once,
it runs the whole tree.
So you will get a
preview of everything that we have here.
In this case, we're doing
the "Group by" every five seconds.
Looks like we are happy with everything we have,
remember we can reshape it, we'll save,
and now is the moment to run that streaming data flow.
If you hover over the data flow,
you will see that "Play" icon,
that's how you start it,
it's continuous run, I have one
running here which is the one that we'll
use from desktop, same thing.
If you hover, you can actually stop it.
Let's start it, it will start,
and now let's go to Desktop.
Here is where things work exactly the same.
We'll go to "Get data," and the connector
that we're going to use is one that
has been released this month,
which is the Power Platform dataflows Beta connector.
The Power BI dataflows would also work,
but it will have some caveats that
Let me show you now the authoring errors table,
I'm going to search for the streaming
dataflows workspace, we're going to expand it,
you can see both dataflows there,
and here's the interesting part.
You will see two sets of tables.
Let's start with managed fields
that it's for the real-time visualization,
the other one is Archived data or Cold.
Imagine this has been running for a month,
let's say if you want historical data,
you go to the latter,
if you want real-time like we're going to do right now,
you connect to the Streaming.
It's important to make sure that you select "DirectQuery"
once you are choosing the storage mode,
just because if you
want visuals that will update in real-time,
it needs to be "DirectQuery".
Now let's expand the AllData table that we have,
and let's actually take advantage of
the relative time filter here in the filter pane.
We'll do the last five minutes just to make sure that we
actually see the data moving once we create the visuals.
Let's do a line chart first,
something very straightforward, of course, over time,
and maybe we'll do revenue,
which is the money that we're
getting for each car passing the toll.
Maybe let's do something else,
let's count the cars
and we'll do that the same way we did
before with the "License plate,"
and we'll switch to "Count."
Now, this is the time where
we will make the visuals move,
we'll go to the "Format pane" for the page,
and we can see here that we have automatic page refresh.
We'll expand it, turn it on,
and select up to one-second refreshes.
You will see the data starts moving right away,
thanks to this particular feature.
The data is coming through the Streaming data flow,
landing in Power BI,
and then using Automatic page refresh,
we can see it in real-time will in here.
We see 2-3 minutes,
so we can see it a little bit better. Thank you so much.
>> Many organizations require
users to classify their sensitive information
to meet regulatory compliance
or internal audit requirements.
Now, with the mandatory label policy,
you can require creators to
set a sensitivity label when they
create new content or edit existing content in Power BI.
Creators will be required to apply
the sensitivity label when they
upload a PBIX file to Power BI,
or when they create new content or
edit content without a label in Power BI.
You can also set a sensitivity label on
paginated reports in the Power BI service,
the same way you can set labels on Power BI reports.
The label and protection will be set to files
created when exporting data
from paginated reports to Word,
Excel, PowerPoint, and PDF files.
Additionally, to help your organization's Power BI users
understand what your sensitivity labels
mean or how they should be used,
you can provide a "Learn More" link,
pointing to your organization's
custom webpage that users will
see when they're applying or being
prompted to apply sensitivity labels.
Next, we've made a number of
improvements to the Datasets hub,
including new sharing capabilities,
Chat in teams, Dataset share,
the other one is Archived data or Cold.
Dataset descriptions, and Goals.
You can find these new features
in the Dataset Hub landing page,
and the Dataset page.
Last December, we announced new Scanner APIs for
extracting tenant-level metadata using Admin list APIs.
We've already seen many customers
using the new APIs to query
Power BI to build
their own reporting and homegrown catalogs.
Our latest improvements include
items you have been asking for the most.
We've now enhanced the APIs to include Dataset labels,
Columns, Measures, DAX expressions, and Mash-up queries.
In the Visualization section,
we have several exciting,
new, and updated visuals available for you on AppSource.
New this month are
the multi-info cards by Portfolio Consultoria Empresarial,
and the PureViz infographic by PureViz.
Visuals that have been updated this month are,
the Multiple Sparklines visual by excelandaccess.com,
the Acterys Reporting visual by Acterys,
the Dynamic radio bar chart by JTA,
the Drill Down Waterfall PRO visual by Zoomcharts,
the Charticulator visual, and the Control
Chart XmR by Nova Silva BV.
This month's Editor's picks are
the Go-to Stratum visual by Sylvon Software Inc,
the Packed Bubble chart by Xviz,
the Multiple Sparklines visual by excelandaccess.com,
the Bowtie chart by Maq Software,
and the Ultimate Variants chart by
dataviz.boutique GmbH
You can find these editors' picks and all
of the other mentioned visuals on AppSource.
That's all for this month.
As always, we hope you enjoy the updates,
and please, keep sending us feedback on
what you'd like to see in upcoming releases.
Thanks for watching, and we'll see you again next month.