1. Let’s Build a Tabular Model in Azure
Analysis Services is in Azure? Seriously!? Let's build a model!
Phillip Labry

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3. Demo

4. Phillip Labry Sr. BI Consultant at Pragmatic Works
IT development for over 30 years
Developer, DBA, Business Intelligence
Experience with Manufacturing, Telecom, Banking, Retail, Government, Insurance, Healthcare, Consulting, Energy, Finance,Logistics
Blog:

5. Today’s scenario

6. Basic Terms
Measure
Numeric value that can be aggregated (Sales Amount)
Fact
Collection of fields mainly consisting of Measures
Dimension
Table of values that describes a fact (people, places, things)
Star Schema
Dimension tables radiating out from a related fact table
Snowflake Schema
Dimensions related to other dimensions
Aggregate
A mathematical summarization of measures
Attribute
Another name for Column(used in Dimensions)

7. What is Analysis Services?
Analytical database designed to be business facing
Optimized for aggregating huge data sets
Two engines: OLAP and Tabular

8. Benefits of Azure Analysis Services
Scale up, scale down and pause
No hardware required
Inherent redundancy
Explore data from anywhere
Use the tools you love and know
SSMS, SSDT, Power BI, Excel

9. Why Azure Analysis Services?
Upsize data from Power BI
Faster data refreshes
Adjust for peak workloads
New features quicker
Save on hardware costs, IT infrastructure

10. Model Types
In Memory
Direct Query

11. Supported data sources
On Premises
SQL Server
PDW / APS
Oracle
Teradata
Cloud
Azure SQL Database
Azure SQL Warehouse

12. BI Semantic Model: Vision (2012)
Data model
Business logic
and queries
Data access
ROLAP
MOLAP
xVelocity
Direct
Query
MDX
DAX
Multi-
dimensional
Tabular
Third-party
applications
Reporting
Services
Excel
PowerPivot
Databases
LOB Applications
Files
OData Feeds
Cloud Services
SharePoint
Insights
Power View

13. BI Semantic Model (Azure Analysis Services)

14. For Development DO NOT CHOOSE PRODUCTION SERVER FOR WORKSPACE
Use Developer Tier pricing
Remember to pause your machines

15. Dimensions Wide and shallow Describe facts Can contain hierarchies
Can contain calculated columns

16. Hierarchies Predefine common hierarchies for the users
Hierarchies are defined from largest group to smallest
Year
Quarter
Month
Hide columns used for hierarchies where appropriate

17. Fact Tables Deep and narrow Mostly measures(Numbers)
Keys to dimensions(Ints)
Natural repository for calculated measures

18. Calculated columns and Measures
Created in the model only
Calculated measures execute when called based on filter context
Calculated columns are created on data load and persist in memory

19. Requirements Azure account
Azure Active Directory Tenant(MS live accounts are not supported)
Directory integration between AAD and on premises AD is recommended but not required
Resource Group
Create a server

20. Demo

21. Current Pricing QPUS MEMORY (GB) Hourly Price Monthly Price S0 40 10
$0.605/hr
~$450.12 S1
100 25
$1.015/hr
~$755.16 S2
200 50
$2.03/hr
~$1,510.32 S4
400
$4.055/hr
~3,016.92
Developer 20 3
$0.066/hr
~$49.11

22. Tips for development Clean table names on first import
Settle on column names before creating any calculated columns or measures
Flatten out snowflakes where possible
Avoid creating calculated columns for intermediate measures
Use views for source data
Use meaningful and verbose names
Use attribute properties and formatting

23. Current Azure Analysis Services challenges
Preview only
Backups
Limited source data
No Power BI connectivity

24. Additional resources

25. Thank you
@PhillipLabry