1. Power BI Desktop

2. Power BI – Brief Information
Power BI is a business analytics solution that lets you visualize your data and share insights across your organization, or embed them in your app or website. Connect to hundreds of data sources and bring your data to life with live dashboards and reports. interface is simple enough for end users to create their own reports and dashboards.
Power BI provides cloud-based BI services, known as "Power BI Services", along with a desktop based interface, called "Power BI Desktop". It offers data warehouse capabilities including data preparation, data discovery and interactive dashboards

3. Power BI – Key Component
Power BI Desktop :- The Windows-desktop-based application for PCs and desktops, primarily for designing and publishing reports to the Service.
Power BI Service : online service (now referred to as PowerBI.com or simply Power BI).
Power BI Mobile Apps : The Power BI Mobile apps for Android and iOS devices, as well as for Windows phones and tablets.
Power BI Gateway :Gateways used to sync external data in and out of Power BI. In Enterprise mode, can also be used by Flows and PowerApps in Office 365.
Power BI Embedded : Power BI REST API can be used to build dashboards and reports into the custom applications that serves Power BI users, as well as non-Power BI users.
Power BI Report Server : An On-Premises Power BI Reporting solution for companies that won't or can't store data in the cloud-based Power BI Service.
Power BI Visuals Marketplace :A marketplace of custom visuals and R-powered visuals.

4. Workflow of Power BI Desktop
Query Editor
Data View
Report View
Data preparation
Data modelling
Data visualization
Relationship View

5. The Query Editor
How we import and prepare our data

6. Power BI Desktop – Query Editor
Data View
Report View
Relationship View
Data preparation
Data modelling
Data visualization

7. The Star Schema
FACT TABLE
DIM TABLE VS

8. The Star Schema Products IdentifierProd ProductType PricePerUnit
CostperUnit
DIM TABLE
Customers
IdentifierCust
FirstName
SecondName
Age
Gender
DIM TABLE
Sales
IdentifierProd
IdentifierDate
IdentifierCust
IdentifierGeo
UnitsSold
TotalSales
TotalCost
FACT TABLE
Time
IdentifierDate
Year
Quarter
Month
Week
Day
SalesPoint
IdentifierGeo
Continent
Country
City

9. Our Project – Current structure
Population-Combined
Country-ID
Country
Year
AgeGroup
Gender
Population

10. Out Project turned into a Star Schema
Population
Country-ID
AgeGroup-ID
Year
Gender
FACT TABLE
Region
Country-ID
Country
DIM TABLE
Age
AgeGroup-ID
AgeGroup
Category
DIM TABLE

11. Query: Duplicate vs. Reference
Source file
Query Editor C
Query 2 (Reference to Query 1) A B
Query 1 (Created in Query Editor)
Query 2 (Duplicate of Query 1)

12. Merge Queries - Join Kind
Merged Queries
LEFT ID
Sales
Region A 10
USA B 50
n/a C 20
Asia
RIGHT ID
Region
Sales A
USA 10 BB
Europe
n/a C
Asia 20
FULL ID
Sales
Region A 10
USA B 50
n/a C 20
Asia BB
Europe
Separate Queries
Outer
Query 1 LEFT
Query 2 RIGHT ID
Sales A 10 B 50 C 20 ID
Region A
USA BB
Europe C
Asia
Anti ID
Sales
Region B 50
n/a ID
Region
Sales BB
Europe
n/a
Inner ID
Sales
Region A 10
USA C 20
Asia

13. Import data into the data model
Query 1
Query 2
Default =
Enable load is set for all
queries
Data preparation
Query 1 &
Query 2 are loaded into the data model
Data model
Query Editor
Data View/Report View
Source files
Data preparation
Enable load is only selected for Query 1
Query 1 is loaded into the data model
Data model
Import data Query 1
Query 2
Query Editor
Data View/Report View

14. Data View & Relationships
How we model our data

15. Power BI Desktop – Data Modelling
Query Editor
Data View
Report View
Relationship View
Data preparation
Data modelling
Data visualization

16. Query Editor vs. Data Model
Connect to source files
Clean data
Add relationships
Add calculated columns
Shape data
Add measures
Structure + prepare data
Analyse data

17. Query Editor vs. Data Model
Power BI Desktop
Data preparation
Data modelling
Data visualization
Relationship View
Query Editor
Data View
Report View

18. Let‘s bring our Data Model to live
Cardinality
= „Type of relationship“

19. One to many (1:*) & Many to one (*:1)
Customers
Orders
ID-Customer
FirstName
SecondName 1
Maximilian
Schwarzmueller 2
John
Meyer 3
Linda
Belle 4
Manuel
Lorenz
ID-Order
OrderDate
ID-Customer A
01 Jan 2017 1 B
08 Jan 2017 2 C
15 Jan 2017 D
25 Jan 2017 E
05 Feb 2017 3 F
15 Feb 2017 4
Each customer is unique
Each customer can have multiple orders

20. One to one (1:1) Passport Person ID-Passport Valid Issued FirstName
SecondName
Country 1
2025
2005
Maximilian
Schwarzmueller
Germany 2
2019
1999
John
Meyer
USA 3
2017
1997
Linda
Belle
Japan
Passport
Person
ID-Passport
Valid
Issued 1
2025
2005 2
2019
1999 3
2017
1997
ID-Passport
FirstName
Second Name
Country 1
Maximilian
Schwarzmueller
Germany 2
John
Meyer
USA 3
Linda
Belle
Japan

21. Power BI Desktop – Data Model
Data preparation
Data modelling
Data visualization
Query Editor
Data View
Relationship View
Report View

22. Course interim conclusion
This course M
DAX OR

23. Calculated Columns vs. Measures
Perform an operation that generates results for each row of your table
Calculated Column
Return a single result of a calculation or an aggregated value (e.g. Averages)
Measure

24. Report View
Let‘s create beautiful charts and tables

25. Power BI Desktop – Report View
Data preparation
Data modelling
Data visualization
Query Editor
Data View
Relationship View
Report View