1. Welcome! Power BI User Group (PUG)
Sydney

2. Creating Enterprise Grade BI Models with Azure Analysis Services
Darren Gosbell
Data Platform MVP
As you have seen in the previous two sessions, Analysis Services enables consistent data across reports and users of Power BI. This session will focus on optimizing configurations, management automation, governance, sizing considerations and best practices. Various modeling enhancements will be included such as Detail Rows allowing users to easily see transactional records, deployment and application-lifecycle management (ALM) features to bridge the gap between self-service and corporate BI. Security drill down such as logging where and how your data is stored, and look at how to leverage additional Azure services to make your information even more secure.
@darrengosbell

3. Meet Your Presenter DARREN GOSBELL
Leader – SQL Server User Group – Melbourne
Data Platform MVP since 2006
SSAS Maestro
Contributor to open source BI projects
DAX Studio
BIDS Helper
AS Stored Procedures

4. 8/1/ :03 AM
1200 Compatibility Level Azure Analysis Services / SQL Server Analysis Services 2016
Performance and scalability
Parallel partition processing
NUMA awareness and memory allocator (SP1)
Super DAX
Developer Tools
Tabular Model Explorer
Integrated workspace server
DAX formula editing
Modeling and analytics
Bi-directional cross filters
Calculated tables
Display folders
Translations
Over 50 new DAX functions
DirectQuery enhancements
Manageability
Tabular Object Model (TOM)
Tabular Model Scripting Language (TMSL)
Modeling and analytics
Bi-directional cross filters
Many-to-many dimension scenarios without need to write complex DAX formulas
For whole table, not just a measure
Various use cases such as account balance, currency conversion, distinct count of attributes
Calculated tables
Dynamically generate a table based on a DAX formula.
Various use cases including role-playing dimensions
Some performance improvements available
Display folders
Present model elements and measures by business function in Pivot Tables and in Power BI
Translations
DirectQuery enhancements
Data is up to date with no management of loading the in-memory cache
Big data sets that don’t fit into memory
New data sources: APS / SQL DW, Oracle, Teradata
Support for MDX queries (Pivot Tables)
Improved query generation resulting in faster performance
Row-level security defined with DAX filters
Over 50 new DAX functions
DATEDIFF, SELECTCOLUMNS, SUMMARIZECOLUMNS, CROSSFILTER
Manageability
Tabular Object Model (TOM)
Tables, columns, relationships – not cubes, measure groups, dimensions
Tabular Model Scripting Language (TMSL)
JSON representation of TOM – with commands for manageability such as create/alter database, backup, restore, attach, detach
Performance and scalability
NUMA awareness and memory allocator
The in-memory engine in SP1 maintains a separate job queue on each NUMA node
The Intel TBB-based scalable allocator that provides separate memory pools for every core. As the number of cores increases, the system can scale almost linearly
Parallel partition processing
Super DAX
Strict evaluation of IF/SWITCH, Variables
Some cases just restoring a 2014 model to 2016 provides significant DAX performance benefits
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

5. 8/1/ :03 AM
1400 Compatibility Level Azure Analysis Services / SQL Server Analysis Services 2017
Data connectivity
Rich set of data sources
Data transformations and mashups with Power Query Formula Language
Modeling and analytics
Detail Rows
Object level security
Enhanced support for ragged hierarchies
Developer tools
SSDT for VS 2017
DAX Editor for SSDT and SSMS
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

6. Demo: Modelling & Analytics
Showing
Object Level Security
Ragged Hierarchies
DetailRow Expressions

7. Ragged Hierarchies
Default View
Desired View

8. Ragged Hierarchies pre-1400
Nodes[HierarchyPath] =PATH ( Nodes[NodeKey], Nodes[ParentKey] ) Nodes[Level1] = LOOKUPVALUE ( Nodes[Name], Nodes[NodeKey], PATHITEM ( Nodes[HierarchyPath], 1, INTEGER ) ) Nodes[Level2] = IF ( Nodes[HierarchyDepth] >= 2, LOOKUPVALUE ( Nodes[Name], Nodes[NodeKey], PATHITEM ( Nodes[HierarchyPath], 2, INTEGER ) ), Nodes[Level1] ) Nodes[Level3] = IF ( Nodes[HierarchyDepth] >= 3, LOOKUPVALUE ( Nodes[Name], Nodes[NodeKey], PATHITEM ( Nodes[HierarchyPath], 3, INTEGER ) ), Nodes[Level2] ) Nodes[IsLeaf]=CALCULATE ( COUNTROWS (Nodes), ALL (Nodes), Nodes[ParentKey] = EARLIER ( Nodes[NodeKey] ) ) = 0 Nodes[HierarchyDepth] =PATHLENGTH( Nodes[HierarchyPath] ) BrowseDepth:=ISFILTERED ( Nodes[Level1] ) + ISFILTERED ( Nodes[Level2] ) + ISFILTERED ( Nodes[Level3] ) MaxNodeDepth:=MAX ( Nodes[HierarchyDepth] )
Sales Amount:=IF ( [BrowseDepth] > [MaxNodeDepth] + 1, BLANK (), IF ( [BrowseDepth] = [MaxNodeDepth] + 1, AND ( VALUES ( Nodes[IsLeaf] ) = FALSE, SUM ( Transactions[Amount] ) <> 0 ), SUM ( Transactions[Amount] ), BLANK () SUM ( Transactions[Amount] ) )
Source:

9. Self-Service & Corporate BI …
8/1/ :03 AM
Self-Service & Corporate BI …
> “Bimodal” BI
Self-service BI is characterized by having a large number of small models.
Corporate BI is characterized by having a small number of large models.
Kurt Schlegel et al. (2016) observes the following. Create a Centralized and Decentralized Organizational Model for Business Intelligence, Page 6. Retrieved from Gartner database; “Gartner Foundational”.
Local teams are being empowered to create and innovate. The centralized team identifies the most successful work being done at a local level, and provides a platform to share and promote this work globally.
Self-service & corporate BI
IT-owned, enterprise BI has the following characteristics compared to self-service BI.
Enterprise BI is characterized by having a small number of large models. This promotes reusability, consistent decisions based on corporate metrics, and efficiencies around the management of data.
Self-service BI is characterized by having a large number of small models. This is a result of enabling agility and freedom for analysts to uncover new insights and data sets.
The reality is most organizations need to strike a balance between the two camps. The challenge lies in how the two camps can work together in harmony.
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

10. Azure Analysis Services
8/1/ :03 AM
Demo Architecture
Cloud data sources
Azure Analysis Services
Visualization & Insights
SQL Data
Warehouse
Power BI
Blob Storage
Cloud processing
Azure Automation
TOM/AMO Partition Creation
Authoring & Development
Self-Service Authoring
Power BI Desktop
Power BI Desktop
Visual Studio
Visual Studio
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

11. Demo: bridge the gap between self-service & corporate BI
Showing
Import from PBIX
Merging Self-Serve with Corporate
Incremental Deployment

12. Demo Summary Fine-grain partition management through API
BI schema-compare for application lifecycle management (ALM)
Power Query data integration
Deployment across environments

13. Corporate BI Features Automation
Partition management to refresh big datasets
Application lifecycle management (ALM)
Deployment across environments
Enhanced security
Enhanced modeling & analytics
Enhanced monitoring

14. Use Case: Azure Intelligence Team

15. Azure Intelligence Team
Mission Statement: Azure Intelligence Platform (AIP) builds a comprehensive data platform for C+E focused on managing metrics for demand (our customers), monetization (usage and billing), and market intelligence.
Main Customers: Sales, Marketing, Finance, Compensation
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

16. By the numbers 5 TB of data per day 1 PB of data 900 pipelines
70 nodes
1200 cores

17. 8/1/ :03 AM
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

18. 8/1/ :03 AM
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

19. 8/1/ :03 AM
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

20. 8/1/ :03 AM
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

21. More information AS processing whitepaper: http://aka.ms/asprocessing
Azure Analysis Services:
Azure BI Blog:

22. Thank you for Attending!
Don’t forget to join your local PUG to enjoy year- round networking and learning.