[Serious Music]
- Welcome to Azure Essentials.
In the next few minutes we'll explore the services
Azure offers to help you model and visualize your data
for interactive reporting and business intelligence.
Wherever your data is stored,
Azure offers Azure comprehensive lineup of services
to help you ingest, transform and store the aggregate data
so that it can be modeled and explored
with commonly used visualization tools,
like Microsoft Power BI, Excel and more.
Azure's an open platform. We want it to give you
all the tools that you need to make sense of
the broadest range of data sources,
whether you're working with well structured
operational databases hosted on-premises or in the Cloud;
online data sources, for example data from your Cloud apps
and other reporting services;
stores of unstructured data, such as event logs,
emails, documents, media files and more;
or even realtime data streams,
including telemetry from sensors and IoT devices.
Let's start with data ingestion.
If you're working across disparate data sources,
you'll want to first extract the data you want
from each source, transforming it so that it can be unified
and aggregated with other data,
and stored in a central data repository.
Able to scale to hold all of the data
that you might want to report on.
If you've got structured data in
operational databases on-premises,
you can leave your data where it is,
or you can move it to one of Azure's many database options.
We've got a gallery of data connectors available
to connect to Azure, or you can develop your own.
If you're working with large amounts of unstructured data
that you'd like to report on,
Azure has the scalar compute power and analytics engines
to extract information and transform it into a state
suitable for storing and modeling.
Now to ensure that your reporting stays up to date,
it makes sense to automate the ingestion
and transformation process on a schedule
that suits your needs.
Azure offers a range of ways to do this,
including scripting your own batch processes in Azure CLI,
or using Azure Data Factory;
a full hybrid data integration service.
Azure Data Factory allows you to create,
schedule and orchestrate your ETL,
or Extract Transform Load workflows at scale,
wherever your data lives; in the Cloud,
or self hosted on-premises.
Now collecting massive volumes of data
from multiple structured and unstructured sources
and ingesting data from all of them in parallel
requires a powerful storage service to give you
the foundation for reporting and business insights.
In Azure, Azure SQL Data Warehouse has the performance,
elasticity and compute power to scale up or down as needed,
allowing you to host a centrally curated repository
virtually unlimited in size.
But a full data warehouse solution's not always required.
If your total storage requirements aren't gonna exceed
four terra bites, Azure SQL server
is likely to be sufficient for your needs.
A Data Model is the heart of any analytic system
and establishes the structure of your data
and the relationships between your data.
Whatever the data source, models provide data views
that can be explored for business intelligence;
therefore needs to be capable of scaling out
to user demand and fast.
In Azure we focus on enabling you
to combine all relevant data into
a rich semantic business oriented data model.
Able to cache potentially many billions of rows of data
in memory for lightning fast queries.
With Azure you can create this semantic data model directly
in Power BI for an all in one self service solution,
or you can use Azure Analysis Services
to build out an IT managed model,
accessible by Power BI and other data visualization tools.
Modeling in Power BI or Azure Analysis Services,
is very similar.
In each case you create a semantic model,
that presents the data as logically linked tables,
mashed up from different sources,
presenting a unified, consistent view of the underlying data
using friendly names, understandable to the business users
who'll be querying this model.
Using data analysis expressions or DAX,
you can also derive new data for the model.
You can use this to filter the data
or calculate and create new measures and KPIs.
One thing to note, there are times where
rather than ingesting the data into a data warehouse,
you may ingest data directly into the model
for ad-hoc data modeling.
This might be the case when you're working with
unstructured datasets, where you're still exploring data
for patterns and structures not yet supported by
the warehouse schema, or where your data
needs to stay on-premises.
Now if you're interest in a hybrid model,
or ultimately moving your data to Azure
and you've worked with Microsoft SQL Analysis Services,
or SSAS on-premises, you'll find working
with these data models very familiar.
Moving from an existing on-premises SSAS implementation
to Azure's straightforward.
You can move your on-premises model to Azure
and continue to link to your existing on-premises databases
via the Azure Gateway; or you can migrate
your on-premises databases too.
All you need to do is give your service a name,
specify what capacity you need and the geographic location
where you want it hosted; and Azure handles the rest.
Unlike on-premises implementations,
it takes just a few minutes to spin up a new instance
of Azure Analysis Services and provision more
deep provision capacity, to meet change in business needs.
Now it's in the Cloud, you no longer need
to do the capacity planning in advance.
You can scale up and out to meet demand.
Additionally, with Web designer,
you can now connect to the data warehouse
and other sources to develop your data models.
With the data model in place, you can now visualize
your data using your visualization tools
of like Power BI.
Power BI's a versatile and easy to use way
for everyone in your organization to access rich,
interactive reports, and to gain insights
from the data from many angles.
Using Power BI dashboards you can create single panes
showing complete 360 degree views
of your business, project, or department.
And you can also provide realtime streaming of data
into these dashboards from social media sources,
service uses metrics or sensors and IoT devices
via Azure IoT services.
These dashboards help everyone in the organization
make data driven decisions and share their other insights
with other people and teams.
Each user can then build on top of this as they wish.
They don't even have to be familiar
with how to use Power BI.
Power Bis support of natural query language,
which means users can interrogate the model
with a Q and A capability, the data view they need,
without having to create a report first.
Users don't even need to type, as Azure Cognitive Service
Integration into Power BI includes speech recognition too.
So that was a quick overview of modeling
and visualizing your data for instructive reporting
and business intelligence in action. Keep checking back
on Azure Essentials for more topics,
and check out our hands on learning series
at the link show. Thanks for watching.