Now let's talk about Amazon QuickSight.
So Amazon QuickSight is a serverless machine powered
business intelligence service,
and so when you hear business intelligence,
that means that you're going to create
interactive dashboards.
So this is what QuickSight looks like,
you can create dashboards
and these dashboards are connected
to data sources that you own,
and therefore you can create these nice visuals.
So it's fast, it's automatically scalable,
you can embed it in website,
and you get a per-session pricing.
The use cases for QuickSight are going to be
around business analytics, building visualization,
performing ad-hoc analysis in the visual fashion,
to get business insights using data.
And so you can connect to many data sources
as we'll see in the next slide,
such as RDS, Aurora, Athena, Redshift, S3, and so on.
Now there is something called the SPICE engine,
you need to remember it,
it's an in-memory computation engine
and it only works if you actually import data
directly into Amazon QuickSight.
It doesn't work if Amazon QuickSight
is connected to another database.
And finally,
QuickSight has some very nice user level features,
and so it's possible
in the Enterprise edition of Amazon QuickSight
to set up column-level security, CLS,
to prevent some columns from being displayed for some users
if they don't have enough access rights.
So what does QuickSight integrate with?
Well, you can integrate it with many data sources from AWS,
for example, your RDS database, or Aurora,
or Redshift, which is a great data warehousing service,
or Athena to do ad-hoc queries on Amazon S3,
or Amazon S3 to actually import data, or OpenSearch,
or Timestream to visualize your time stream data
in an optimized fashion.
You can also integrate with third party data sources
that are software as a service and supported by QuickSight,
and the full list is on the QuickSight website,
but some of them include Salesforce, and also Jira.
You can also integrate with third party databases
such as Teradata, or for example,
an on-premises database that is using
the JDBC protocol internally.
And as I said, you can import data sources
directly into QuickSight,
such as your Excel files, your CSV file,
JSON documents, TSV, or the EFS CLF format for log formats.
And if these data sources are imported
into Amazon QuickSight,
then you can leverage the SPICE engine
to perform in memory computation in a very, very fast way.
So that's for the integrations,
and you will very commonly see at the exam
using QuickSight with Athena
or QuickSight with Redshift,
but any of these is possible to be seen.
Now you have two things in QuickSight,
you have dashboards and analysis.
So in QuickSight, you actually have users,
which is available in the Standard version,
and groups of users,
only available in the Enterprise version,
and these users and groups,
they only exist within the QuickSight service,
so they're not equivalent to IAM users.
IAM users are only used for administration.
And so you define these users and these groups
from within QuickSight,
and then you create a dashboard.
And a dashboard is a read-only snapshot
of an analysis so they can share,
and it will preserve the configuration of the analysis,
so whatever filters, parameters,
controls, and sorting options you set on your analysis,
is going to be preserved and is going to be a dashboard.
And so an analysis is more complete,
but you can share either the analysis or the dashboard
with specific users or groups,
and to do so, you must first publish,
for example, your dashboards.
And then if users have access to the dashboards,
they can also see the underlying data.
So in QuickSight,
you would create you analysis or dashboards,
and then you would share it with specified users and groups.
And that's it, you know everything there is to know
about QuickSight for the exam.
I hope you liked it,
and I will see you in the next lecture.