1. Powering Up with Power BI
Brian Larson & Lily Vergara
Superior Consulting Services, LLC
Powering Up with Power BI

2. SQLSaturday #682 – After Party
4th Floor of Mall of America at 6:30 PM
Sponsored By:

3. Thank you Sponsors!
Platinum Sponsor:
Gold Sponsors:

4. PASSMN – News/Info Sponsors: Board Member Elections:
Thanks to all our sponsors of 2017!
We need Sponsors for 2018!
Special thanks to our annual sponsor:
Board Member Elections:
3 spots available for term.
Your chance to help out the MN SQL community!

5. Brian Larson
15 years experience with Microsoft Data Visualization Tools
Contributed to the initial codebase of SQL Server Reporting Service
Regional and National Speaker
Author on Business Intelligence and Data Visualization topics for McGraw-Hill Professional

6. Lily Vergara
Certified Microsoft Data Scientist and Business Intelligence Consultant
Experience working in manufacturing, retail, restaurant, agriculture, health insurance, and higher education
I enjoy long walks with datamarts on the beach, DAX by candlelit dinner, and curling up with a duvet coding some R

7. Course Outline Power BI Architecture Power BI Desktop
Direct Connect with Power BI Desktop
Basic Visualizations
Data Import and Transformations
Tabular Data Model Building
Measures and DAX Expressions
Advanced Visualizations
Power BI Report Server
Power BI Desktop Report Viewer
Power BI Service
Navigation
Features
Workspaces
Content Packs and Apps
Subscriptions
Refreshing Data with Gateway
Administration
Power BI Mobile
App

8. Power BI Architecture
Brian

9. Power BI Architecture Power BI is…
a set of tools from Microsoft designed to provide business users with self-service business intelligence capabilities.
Brian

10. Power BI Architecture Power BI Desktop (pbix file) Tabular Data
Storage
Visualizations
Model Structure
Source Data
Query Editor
Brian

11. Power BI Architecture Power BI Desktop (pbix file) Tabular Data
Storage
Model Structure
Visualizations
Query Editor
Source Data
Brian

12. Power BI Architecture Power BI Service Power BI Report Server Deploy
Brian
Power BI Service
Power BI Report Server

13. Power BI Connection Types
Components Stored in…
Works with…
Data Source
Power BI
Visualizations
Model Structure
Data
SQL Server
Analysis Services
DAX/MDX Queries
Live Connection
Live Connection
Visualizations
Data
Model Structure
SQL Server Relational DB, Azure SQL Database,
Azure SQL Data Warehouse, SAP HANA
T-SQL Queries
Direct Query
Direct Query
Data
Visualizations
Model Structure
All supported Power BI data sources including those listed above.
Import
Import

14. Power BI Desktop
Brian

15. Direct Connect with Power BI Desktop
Brian
Demonstration

16. Basic Visualizations
Brian
Demonstration

17. Data Import and Transformations
Brian
Demonstration

18. Tabular Data Model Building
User Friendly Models – star schema
Relationships –establish and maintain
Hierarchies – how to build, start with highest level
Demonstration

19. Measures and DAX Expressions
Brian

20. Default Context in a Measure
Default Context is the table
Brian

21. Measures Respond to Filters and Slicers
Only Andy
Only 2015
Only Guiding Lights
Brian
Measures respond to the context created by filters and slicers

22. Measures Respond to Filters and Slicers
Sales Tax:=SUM('Orders'[StoreSales]) *
2015 Guiding Lights Orders
for Andy
2015 Guiding Lights Orders
By Product Type
By Sales Person
Sales Tax
$50,652.48
Only 2015
. . .
. . .
. . .
All
Orders
2015
Orders
2015 Woodland Creatures Orders
for Zeb
Brian
2015 Woodland Creatures Orders
Filter
Sales Tax
$490,457.43
Slice
Slice
Calculate Measure

23. Aggregate in a Calculate Column
Does not react to
User Filters
Brian
Calculated Column becomes an Attribute

24. Filter Condition in a Measure
Large Order Sales:=CALCULATE([Sales in Dollars], Orders[StoreSales]>150)
2015 Guiding Lights Orders
for Andy
2015 Guiding Lights Orders
> $150
Large Order Sales {None}
By Product Type
By Sales Person
Only 2015
. . .
. . .
. . .
. . .
All
Orders
2015
Orders
2015 Woodland Creatures Orders
for Zeb
> $150
Brian
2015 Woodland Creatures Orders
Large Order Sales
$4,766,105.40
User Filter
Measure Filter
User Slice
User Slice
Calculate Measure

25. Same Attribute in Slicer & Measure Filter
Guiding Lights Sales:= CALCULATE([Sales in Dollars], 'Products'[Product Type]="Guiding Lights")
2015 Guiding Lights Orders
for Andy
2015 Guiding Lights Orders
Guiding Lights
2015 Guiding Lights Sales for Andy $651,478.80 X
By Product Type
By Sales Person
Only 2015
. . .
. . .
. . .
. . .
All
Orders
2015
Orders
2015 Guiding Lights Sales for Zeb $2,120,164.20
Guiding Lights
2015 Woodland Creatures Orders
for Zeb
Brian
2015 Woodland Creatures Orders X
User Filter
Measure Filter
User Slice
User Slice
Calculate Measure

26. Multiple Attributes Filtered in Measure
Guiding Lights 2014 Sales:= CALCULATE([Sales in Dollars], 'Products'[Product Type]="Guiding Lights", 'Time'[Year]=2014) X
2015 Guiding Lights Orders
for Andy
2014 Guiding Lights Sales for Andy $703,915.80
Guiding Lights
2015 Guiding Lights Orders X
By Product Type
By Sales Person
Only 2015
. . .
. . .
. . .
. . .
All
Orders
2015
Orders X
2014 Guiding Lights Sales for Zeb $2,115,498.00
Guiding Lights
2015 Woodland Creatures Orders
for Zeb
Brian
2015 Woodland Creatures Orders X
User Filter
Measure Filter
User Slice
User Slice
Calculate Measure

27. Overriding a User Filter/Slicer with ALL()
Guiding Lights All Years Sales:= CALCULATE([Sales in Dollars], 'Products'[Product Type]="Guiding Lights", ALL('Time'[Year])) X
Guiding Lights Sales for Andy $2,069,970.00
2015 Guiding Lights Orders
for Andy
Guiding Lights
2015 Guiding Lights Orders X
By Product Type
By Sales Person
Only 2015
. . .
. . .
. . .
. . .
All
Orders
2015
Orders X
Guiding Lights Sales for Zeb $6,323,343.60
Guiding Lights
2015 Woodland Creatures Orders
for Zeb
Brian
2015 Woodland Creatures Orders X
User Filter
Measure Filter
User Slice
User Slice
Calculate Measure

28. Overriding Filter/Slicer with ALLEXCEPT()
All Sales Selected Year:= CALCULATE([Sales in Dollars], ALLEXCEPT('Orders', 'Time'[Year]))
2015 Guiding Lights Orders
for Andy
Guiding Lights
2015 Sales $161,815,808.92
2015 Guiding Lights Orders X
By Product Type
By Sales Person
Only 2015 X
. . .
. . .
. . .
. . .
All
Orders
2015
Orders
Guiding Lights
2015 Sales $161,815,808.92
2015 Woodland Creatures Orders
for Zeb
Brian
2015 Woodland Creatures Orders X
User Filter X
Measure Filter
User Slice
User Slice
Calculate Measure

29. RELATED() Function Orders Product =RELATED(Products[Retail Price]) 2
Unit Price 2
$22.05 5
$15.75 6
$14.70 4
$25.20
Product Code
Product
Retail Price 2
Boston, MS Harbor Lighthouse
$22.05 3
Cape Hatteras, NC Lighthouse
$18.90 4
Portland, ME Lighthouse
$25.20 5
Split Rock, MN Lighthouse
$15.75 6
Manitowoc, WI Lighthouse
$14.70
Brian

30. LOOKUPVALUE() Function
=LOOKUPVALUE(Products[Retail Price], [Product Code], [Product])
Orders
Product X
Product
Unit Price 2
$22.05 5
$15.75 6
$14.70 4
$25.20
Product Code
Product
Retail Price 2
Boston, MS Harbor Lighthouse
$22.05 3
Cape Hatteras, NC Lighthouse
$18.90 4
Portland, ME Lighthouse
$25.20 5
Split Rock, MN Lighthouse
$15.75 6
Manitowoc, WI Lighthouse
$14.70
Brian
Relationship created on the fly

31. SUMX() Iterative Function
Calcd Total Sales:=SUMX('Orders', 'Orders'[Unit Price] * 'Orders'[UnitSales])
30.80 * 4 =
Brian
123.20

32. SUMX() Iterative Function
Calcd Total Sales:=SUMX('Orders', 'Orders'[Unit Price] * 'Orders'[UnitSales])
30.80 * 4 =
Brian
246.40

33. SUMX() Iterative Function
Calcd Total Sales:=SUMX('Orders', 'Orders'[Unit Price] * 'Orders'[UnitSales])
30.80 * 4 =
Brian
369.60

34. SUMX() Iterative Function
Calcd Total Sales:=SUMX('Orders', 'Orders'[Unit Price] * 'Orders'[UnitSales])
29.40 * 4 =
Brian
487.20

35. Using the FILTER() Function
Total Sales for Medium Priced Items:=SUMX(FILTER('Orders',Orders[Unit Price] >= && Orders[Unit Price] <= 30.00), 'Orders'[Unit Price] * 'Orders'[UnitSales])
2015 Guiding Lights Orders
for Andy
Medium Priced Items
Total Sales for Medium Priced Items $236,048.40
2015 Guiding Lights Orders
By Product Type
By Sales Person
Only 2015
. . .
. . .
. . .
. . .
All
Orders
2015
Orders
Medium Priced Items
Total Sales for Medium Priced Items $657,643.20
2015 Woodland Creatures Orders
for Zeb
Brian
2015 Woodland Creatures Orders
User Filter
User Slice
User Slice
Measure Filter
Calculate Measure

36. Using the FILTER() Function
Total Sales for Medium Priced Items:=SUMX(FILTER('Orders',Orders[Unit Price] >= && Orders[Unit Price] <= 30.00), 'Orders'[Unit Price] * 'Orders'[UnitSales])
2015 Guiding Lights Medium Priced Item Orders
for Andy
2015 Guiding Lights Orders
for Andy
SUMX(…)
Iterates through the virtual table calculating the sum of the [Unit Price] * [UnitSales]
FILTER(…)
Filtered Rows
Virtual Table
New Virtual Table
Brian

37. Using the DATESYTD() Function
YTD Sales:=CALCULATE([Sales in Dollars], DATESYTD('Time'[Date]))
2015 Guiding Lights Orders
for January
Set of Dates YTD
Guiding Lights January YTD Sales $1,212,750.00
2015 Guiding Lights Orders
By Product Type
By Month of Year
Only 2015
. . .
. . .
. . .
. . .
All
Orders
2015
Orders
Set of Dates YTD
Woodland Creatures December 2015 YTD Sales
$41,577,790.20
2015 Woodland Creatures Orders
for December
Brian
2015 Woodland Creatures Orders
User Filter
User Slice
User Slice
Measure Filter
Calculate Measure

38. Using the DATESYTD() Function
YTD Sales:=CALCULATE([Sales in Dollars], DATESYTD('Time'[Date]))
2015 Guiding Lights Orders
for January
2015 Guiding Lights Orders
for January
SUM(…)
Sum all Sales in the Virtual Table
DATESYTD(…)
Filtered Rows
Virtual Table
New Virtual Table
Brian

39. Using the DATESYTD() Function
YTD Sales:=CALCULATE([Sales in Dollars], DATESYTD('Time'[Date]))
2015 Guiding Lights Orders
for January - February
2015 Guiding Lights Orders
for February
SUM(…)
Sum all Sales in the Virtual Table
DATESYTD(…)
Filtered Rows
Virtual Table
New Virtual Table
Brian

40. Using the DATESYTD() Function
YTD Sales:=CALCULATE([Sales in Dollars], DATESYTD('Time'[Date]))
2015 Guiding Lights Orders
for January - March
2015 Guiding Lights Orders
for March
SUM(…)
Sum all Sales in the Virtual Table
DATESYTD(…)
Filtered Rows
Virtual Table
New Virtual Table
Brian

41. Using the PARALLELPERIOD() Function
Prev Qtr Sales:=CALCULATE([Sales in Dollars], PARALLELPERIOD('Time'[Date],-1, QUARTER))
2015 Guiding Lights Orders
for Quarter 1
Set of Dates Prev Quarter
Guiding Lights Quarter Prev Qtr Sales $3,534,379.80
2015 Guiding Lights Orders
By Product Type
By Month of Year
Only 2015
. . .
. . .
. . .
. . .
All
Orders
2015
Orders
Dates Prev Quarter
Set of
Woodland Creatures Quarter Prev Qtr Sales
$10,572,865.72
2015 Woodland Creatures Orders
for Quarter 4
Brian
2015 Woodland Creatures Orders
User Filter
User Slice
User Slice
Measure Filter
Calculate Measure

42. Using the PARALLELPERIOD() Function
Prev Qtr Sales:=CALCULATE([Sales in Dollars], PARALLELPERIOD('Time'[Date],-1, QUARTER))
2014 Guiding Lights Orders
for Quarter 4
2015 Guiding Lights Orders
for Quarter 1
SUM(…)
Sum all Sales in the Virtual Table
PARALLELPERIOD(…)
Filtered Rows
Virtual Table
New Virtual Table
Brian

43. Using the PARALLELPERIOD() Function
Prev Qtr Sales:=CALCULATE([Sales in Dollars], PARALLELPERIOD('Time'[Date],-1, QUARTER))
2015 Guiding Lights Orders
for Quarter 1
2015 Guiding Lights Orders
for Quarter 2
SUM(…)
Sum all Sales in the Virtual Table
PARALLELPERIOD(…)
Filtered Rows
Virtual Table
New Virtual Table
Brian

44. Using the DATESBETWEEN() Function
Curr Qtr Sales:=CALCULATE([Sales in Dollars], DATESBETWEEN('Time'[Date], STARTOFQUARTER('Time'[Date]), ENDOFQUARTER('Time'[Date])))
2015 Guiding Lights Orders
for Quarter 1
2015 Guiding Lights Orders
for January
SUM(…)
Sum all Sales in the Virtual Table
DATESBETWEEN(…)
Filtered Rows
Virtual Table
New Virtual Table
Brian

45. Using Variables in a Measure
Qtr Over Qtr Sales Growth:= VAR QtrOverQtrDiff = [Curr Qtr Sales] - [Prev Qtr Sales]
return IF([Prev Qtr Sales] <> 0, DIVIDE(QtrOverQtrDiff, [Prev Qtr Sales]))
Brian

46. Advanced Visualizations Out of The Box Features, Custom Visuals, and R Scripting
Lily
•Power BI service supports viewing and interacting with visuals created with R scripts. Visuals created with R scripts, commonly called R visuals, can present advanced data shaping and analytics such as forecasting, using the rich analytics and visualization power of R.
•Demonstrate this feature by having R installed on the presentation laptop or server where data is sourced, install common package ggplot, create 2 visualization showing capability
Demonstration

47. Custom Visualizations
Office Store
Community-sourced
R Powered Custom Visuals
R Script Showcase
R scripted visualizations
Data visualizations allow you to interact with your data to find business insights. Power BI lets you choose from a list of available visualizations, add a custom visualization that you create yourself, or select from our expanding list of available visualizations in the community gallery.
Custom Visualizations: The Office store contains custom visualizations created by members of the community and by Microsoft. These visualizations can be downloaded and added to Power BI Service reports and Power BI Desktop reports. All of these custom visuals have been approved by Microsoft and they behave just similar to the native visualizations included with Power BI; they can be filtered, highlighted, edited, shared, etc.
You can use R-powered custom visuals without any knowledge of R, and without any R scripting. This enables you to harness the analytic and visual power of R visuals, and R scripts, without learning R or doing any programming yourself.

48. Open Source Software Environment
Introduction to R
Open Source Software Environment
Extracting Data
Transforming Data
Loading Data
Analyzing
Organizing
Summarizing
Programming Language
Statistics
Graphing
Cross Platform
Linux
Windows
MacOS
FreeBSD
Used By
Statisticians
Data Scientists
Developers
Programmers
IT Professionals
Analysts
R is a free, open source software environment and programming language for statistical computing and graphics.
R provides a wide variety of statistical techniques, including linear and non-linear modeling, classical statistical tests, time-series analysis, classification, clustering, machine learning, among many more. Also used for ETL. R is for analyzing, organizing, summarizing data
Cross platform: It compiles and runs on a wide variety of UNIX platforms, FreeBSD, Linux, Windows, and MacOS.
R is used by statisticians, data scientists, developers, programmers, IT professionals, analyst, etc.
R operates on built-in functions and self developed packages that target different technique and graphical aspects of data analysis. Packages as a group of functions, datasets, compiled codes, or documentation. Contents of package are downloaded from Comprehensive R Archive Network (CRAN) and stored on local machine.
The current R is the result of a collaborative effort with contributions from all over the world. R was initially written by Robert Gentleman and Ross Ihaka—also known as “R & R” of the Statistics Department of the University of Auckland.

49. Microsoft Power BI Analytics Features With A Brief History of R Integration
1970’s-1980’s
John Chambers, Bell Labs
S Language Publicly Made Available
1993
R Founded
Ross Ihaka and Robert Gentleman
R Released under GNU Public License (GPL)
R Mailing Lists
Comprehensive R Archive Network Established
2000
R Released
Stable for Production Use
2007
Revolution Analytics Founded
Purpose of R Development, Support, Services, and Consulting
2015
Microsoft Acquires Revolution Analytics
R Consortium Organized (Microsoft, Rstudio, Alteryx, Google, Hewlett Packard, Ketchum Trading, Mango Solutions, Oracle)
Custom Visuals
R Integration (R Scripted Visuals)
2016
Grouping and Binning
Include/Exclude Data Points
Forecasting
TopN Filter
R Powered Custom Visual
Use your own R IDE
Analytics Pane
Dynamic Reference Lines
2017
Explain the Increase/Decrease Insights
What-If Parameters
Symmetry Shading and Ratio Lines in Scatterplot
Quick Measures
Show Values as
Clustering
1970s-1980s: Before there was R, there was S.
1993: The current R is the result of a collaborative effort with contributions from all over the world. R was initially written by Robert Gentleman and Ross Ihaka—also known as “R & R” of the Statistics Department of the University of Auckland.
2015: Revolution Analytics Purchased by Microsoft: Revolution Analytics focuses on big data, large scale multiprocessor (or "high performance") computing, and multi-core functionality.
2015: Enhanced distribution of R: MRAN-Time stamped Version of CRAN, Multithreaded Performance-Math Kernel libraries, and Reproducible R Toolkit

50. R is among the most popular and widely used analytics tools
Used across a variety of industries: automotive, social media, retail, technology, government, travel, real estate, etc.
In RedMonk’s 2017 Q1 study on programming languages rank from GitHub projects and stack overflow tags, R was ranked among the top 15 in programming languages
Ranked number 1 analytics tool used by analytics professionals according to Rexer Analytics 2015 Data Science Survey
Ranked number 2 in self-service BI, next to Excel
Show the audience the website to download R-from MRAN:

51. Power BI Report Server

52. Power BI Report Server A Suite of Products
Paginated reports
Mobile reports
Power BI Report Server is a solution that customers deploy on their own premises for creating, publishing, and managing reports, then delivering them to the right users in different ways, whether that’s viewing them in web browser, on their mobile device, or as an in their in-box.
A modern web portal you can view in any modern browser. In the web portal, you can organize and display reports and KPIs. You can also store Excel workbooks on the portal.
View PBI Desktop reports, paginated reports, and mobile reports within the web portal in your own environment.

53. Power BI Report Server
Demonstration

54. Power BI Service
Lily
From the open .pbix, publish to service, select workspace, publish from power bi desktop OR Navigate to PowerBI.com
Supported browsers:
Microsoft Edge
Internet Explorer 11
Chrome desktop latest version
Safari Mac latest version
Firefox desktop latest version

55. Power BI Service Features
Demonstration

56. Power BI and Excel Integration
Demonstration

57. Workspaces
Demonstration

58. Sharing and Receiving Reports and Dashboards
Share Dashboard, Create and View Content Packs and Apps
Subscriptions
Demonstration

59. Data Gateway

60. Gateway Personal Data Gateway On-premises Data Gateway
Personal, prototype use
Impersonates you
Available with Power BI Pro
Computer must be on
Computer must have internet connection
You have to be logged in or run services as an admin (run as an autonomous service running in the back ground)
On-premises Data Gateway
For production enterprise use
Installed on server, available 24/7
Always on and not asleep
Direct internet connection, not wireless
Runs as local services
The on-premises data gateway acts as a bridge, providing quick and secure data transfer between on-premises data (data that is not in the cloud) and the Power BI, Microsoft Flow, Logic Apps, and PowerApps services.

61. Data Gateway Power BI Model Gateway Queue Gateway Cloud Service
Azure Service Bus
PowerBI.com
On-premises
Service Bus: piece of middleware that allows applications to communicate indirectly
Key to enabling hybrid solutions
Everything done via outbound requests
Different ways to download gateway
Power BI.com
When logged in, download arrow on navigation bar
Dataset ellipses>Datasets>manage gateways>learn more
Can only have one gateway on server
Requirements
.Net 4.5
64 bit windows
Windows 7 or higher
Windows Server 2008 R2 or higher
Recommendations
8 core
8 GB RAM
Windows Server 2012 R2 or higher
Network Requirements
Internet connectivity
No inbound ports
Outbound ports
443 login/general
5671, 5672 messaging protocol
service bus listeners
Install and Configure
Identify the account to use with the gateway, service account (O365 login)
Gateway name
Recovery key: used for disaster or migration
Application Gateway
Data Source

62. Data Gateway Power BI Model Gateway Queue Gateway Cloud Service
Q/DS/C
A model refresh or Direct Query queues up a request with Query/Data Source/Credentials (encrypted)
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
PowerBI.com
On-premises
Application Gateway
Data Source

63. The request is analyzed by the Gateway Cloud Service
Data Gateway
Q/DS/C
Power BI Model
Gateway Queue
Gateway Cloud
Service
The request is analyzed by the Gateway Cloud Service
Azure Service Bus
PowerBI.com
On-premises
Application Gateway
Data Source

64. The Gateway Cloud Service sends the request to the Azure Service Bus
Data Gateway
Power BI Model
Gateway Queue
Gateway Cloud
Service
Q/DS/C
The Gateway Cloud Service sends the request to the Azure Service Bus
Azure Service Bus
PowerBI.com
On-premises
Application Gateway
Data Source

65. Data Gateway Power BI Model Gateway Queue Gateway Cloud Service
Azure Service Bus
Q/DS/C
PowerBI.com
On-premises
The Application Gateway makes a secure, outbound connection to the Azure Service Bus
Application Gateway
Data Source

66. The Application Gateway receives any pending requests
Data Gateway
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
Q/DS/C
PowerBI.com
On-premises
The Application Gateway receives any pending requests
Application Gateway
Data Source

67. The Application Gateway uses the Data Source to initiate a connection
Data Gateway
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
PowerBI.com
On-premises
The Application Gateway uses the Data Source to initiate a connection
Q/DS/C
Application Gateway
Data Source

68. Data Gateway Power BI Model Gateway Queue Gateway Cloud Service
Azure Service Bus
PowerBI.com
On-premises
The Application Gateway decrypts the credentials and uses them to complete the connection
Q/DS/C
Application Gateway
Data Source

69. The query is sent to the data source and executed
Data Gateway
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
PowerBI.com
On-premises
The query is sent to the data source and executed
Q/DS/C
Application Gateway
Data Source

70. The result set is returned
Data Gateway
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
PowerBI.com
On-premises
The result set is returned
to the model
Results
Application Gateway
Data Source

71. Data Gateway with Live Connect
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
PowerBI.com
On-premises
Application Gateway
Data Source

72. Data Gateway with Live Connect
Q/DS/C/UPN
A Live Query queues up a request with Query/Data Source/Credentials (encrypted)/ User Principal Name
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
PowerBI.com
On-premises
Application Gateway
Data Source

73. Data Gateway with Live Connect
Q/DS/C/UPN
Power BI Model
Gateway Queue
Gateway Cloud
Service
The request is analyzed by the Gateway Cloud Service
Azure Service Bus
PowerBI.com
On-premises
Application Gateway
Data Source

74. Data Gateway with Live Connect
Power BI Model
Gateway Queue
Gateway Cloud
Service
Q/DS/C/UPN
The Gateway Cloud Service sends the request to the Azure Service Bus
Azure Service Bus
PowerBI.com
On-premises
Application Gateway
Data Source

75. Data Gateway with Live Connect
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
Q/DS/C/UPN
PowerBI.com
On-premises
The Application Gateway makes a secure, outbound connection to the Azure Service Bus
Application Gateway
Data Source

76. Data Gateway with Live Connect
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
Q/DS/C/UPN
PowerBI.com
On-premises
The Application Gateway receives any pending requests
Application Gateway
Data Source

77. Data Gateway with Live Connect
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
PowerBI.com
On-premises
The Application Gateway uses the Data Source to initiate a connection
Q/DS/C/UPN
Application Gateway
Data Source

78. Data Gateway with Live Connect
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
PowerBI.com
On-premises
The Application Gateway decrypts the credentials and uses them to complete the connection
Q/DS/C/UPN
Application Gateway
Data Source

79. Data Gateway with Live Connect
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
PowerBI.com
On-premises
The User Principal Name is sent as the EffectiveUserName in the connection string
Q/DS/C/UPN
Application Gateway
Data Source

80. Data Gateway with Live Connect
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
PowerBI.com
On-premises
The query is sent to the data source and executed in the security context of the user associated with the UPN
Q/DS/C/UPN
Application Gateway
Data Source

81. Data Gateway with Live Connect
Power BI Model
Gateway Queue
Gateway Cloud
Service
Azure Service Bus
PowerBI.com
On-premises
The result set is returned
to the model
Results
Application Gateway
Data Source

82. Data Refresh with On-Premises Data Gateway
Demonstration

83. Power BI Administration
Office 365 Admin Center
Manage users
Manage licenses
Power BI Admin Portal
Usage settings
User with most dashboards
Dashboard shared the most
Most consumed data sources
Delivery mode most used
Tenant settings
Disable publish to web, export data, publish content packs to entire organization, shared outside organization
Usage metrics for dashboards and reports
Admins can enable/disable for organization; specific groups
Admin
Power BI Admin role in O365
Audit logs globally available
Granular Admin Controls
Share content to external users
Export reports as PowerPoint presentations
Export Data
Print dashboards and reports
Publish content packs to the entire organization
Create template organizational content packs
Use Analyze in Excel with on-premises datasets

84. Power BI Mobile

85. Supported Devices and Power BI App
iOS (iPad, iPhone, iPod Touch, or Apple Watch)
Android phone or tablet
Windows 10 device
Supported Devices
Download the Power BI App
Google Play Store
Apple Store
Windows Store
Sign into your Power BI account
Power BI App
Note: Power BI App supports Power BI and SQL Server Reporting Services (2016)

86. Power BI Mobile
Demonstration

87. Questions

88. Thank you Brian Larson Lily Vergara