1. Delivering enterprise BI with Azure Analysis Services
5/28/2018 4:27 PM
BRK3098
Delivering enterprise BI with Azure Analysis Services
Josh
Sr. Program Manager
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2. What do all of these have in common?
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5/28/2018 4:27 PM
What do all of these have in common?
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3. They can all use Azure Analysis Services
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5/28/2018 4:27 PM
They can all use Azure Analysis Services
Azure Analysis Services
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4. Business logic & metrics
Azure Analysis Services Azure Analysis Services is based on SQL Server technology
Data sources
BI semantic model
Client tools
Cloud
Cloud
SQL Database
Security
SQL Data Warehouse
Data modeling
Power BI
Data Lake
In-memory cache
HDInsight/Spark
Other data sources
On-premises
Azure Analysis Services
On-premises
Power BI Desktop
SQL Server / Oracle / Teradata
Excel
Lifecycle management
Business logic & metrics
Other data sources
Third party BI tools

5. Rich semantic modelling
Source Data
Complex raw data optimized for processing
Semantic Model
Rich, business user friendly semantic model
Cloud Data
Azure Analysis Services
Product Id
Product Name
Product Description
Category
Category Id
Category Name
Category Description
Sub Category
Shelf qty
Return qty
Order qty
Sale qty
On-Premises Data
locatioidentifier
identifiertype
fieldidentifier
numberofunitssoldtodate
remaningnumber
Numberofuntissoldtoday
receivedback
productidentifier
descriptionline1
descriptionline2
qtyafterqtysales
familyidentifier

6. Connect and go Microsoft Build 2017 5/28/2018 4:27 PM
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7. Lightning fast queries
Source Data
Complex raw data optimized for processing
Semantic Model
Rich, business user friendly semantic model
Cloud Data
Azure Analysis Services
Cache
Semantic model
On-Premises Data
Queries
productidentifier
descriptionline1
descriptionline2
qtyafterqtysales
familyidentifier
numberofunitssoldtodate
remaningnumber
Numberofuntissoldtoday
receivedback
Insights at the speed of thought
locatioidentifier
identifiertype
fieldidentifier

8. Lightning fast queries
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5/28/2018 4:27 PM
Lightning fast queries 2 1 3 4
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9. Demo: Azure Analysis Services
Microsoft Build 2017
5/28/2018 4:27 PM
Demo: Azure Analysis Services
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10. Demo: Getting started Microsoft Build 2017 5/28/2018 4:27 PM
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11. 5/28/2018 4:27 PM
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12. Considerations for using Azure AS
Scenario: Large models with high concurrent usage
Query latency: Low. All data in Memory
Data freshness: On demand
Data partitioning
Model size: Up to 400 GB of data after compression.
Programmability: ARM, PowerShell, TOM/AMO, TMSL, MSOLAP and ADOMD.
Application life cycle: Visual studio and source control integration.

13. 5/28/2018 4:27 PM
Loading Data
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14. Tabular Object Model (TOM)
5/28/2018 4:27 PM
Model
Data Sources
Tabular Object Model (TOM)
Tables
Relationships
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15. Tables Name: Table 1 Columns Measures Hierarchies Partitions Query
5/28/2018 4:27 PM
Tables
Name: Table 1
Columns
Measures
Hierarchies
Partitions
Query
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16. Query: Select * from dbo.factTable
Partitions
Name: Partition 1
Query: Select * from dbo.factTable

17. Partitions
Name:
Query: Select * from dbo.factTable where date = “1/1/2017”
Name:
Query: Select * from dbo.factTable where date = “1/2/2017”
Name:
Query: Select * from dbo.factTable where date = “1/3/2017”

18. Demo: Creating partitions with TOM and AMO

19. Automated Partition Management whitepaper

20. Management REST API (coming soon)

21. Management REST API (coming soon)
Advantages:
Doesn’t require .NET
Asynchronous
Automatic retry

22. Demo: Refresh with REST

23. Scaling queries

24. Scaling queries - before

25. Scale out Azure AS Cluster Frontend node Backend node
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
Frontend node
Backend node

26. Scale out Azure AS Cluster Frontend node Backend node
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
Frontend node
Backend node

27. Scale out Azure AS Cluster Frontend node Backend node
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
Frontend node
Backend node

28. Scale out Number of query replicas: 0 Azure AS Cluster Frontend node
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
Frontend node
Backend node

29. Scale out Number of query replicas: 3 Azure AS Cluster Frontend node
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
Frontend node
Backend node

30. Scale out Number of query replicas: 0 Azure AS Cluster Frontend node
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
asazure://aspaaseastus2.asazure.windows.net/someserver
Frontend node
Backend node

31. Demo: Scale out

32. Server alias

33. Connecting to a server

34. Link://linkfiletest.azurewebsites.net/
Connecting to a server
Link://linkfiletest.azurewebsites.net/

35. Link endpoint example namespace linkfiletest { public class Startup
// This method gets called by the runtime. Use this method to add services to the container.
// For more information on how to configure your application, visit
public void ConfigureServices(IServiceCollection services) }
// This method gets called by the runtime. Use this method to configure the HTTP request pipeline.
public void Configure(IApplicationBuilder app, IHostingEnvironment env, ILoggerFactory loggerFactory)
loggerFactory.AddConsole();
app.Run(async (context) =>
//context.Response.ContentType = "text/xml";
await context.Response.WriteAsync("asazure://southcentralus.asazure.windows.net/testux");
});

36. Other important features
Azure Active directory and B2B
On premise data gateway
Firewall (now available)

37. Azure Analysis Services
5/28/2018 4:27 PM
Azure Analysis Services
A customer case study
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38. Azure Intelligence Team
5/28/2018 4:27 PM
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
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39. By the numbers 5 TB of data per day 1 PB of data 900 pipelines
70 nodes
1200 cores

40. 5/28/2018 4:27 PM
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41. 5/28/2018 4:27 PM
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

42. 5/28/2018 4:27 PM
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

43. 5/28/2018 4:27 PM
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44. More information AS processing whitepaper: http://aka.ms/asprocessing
Azure Analysis Services:
Azure BI Blog:

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46. 5/28/2018 4:27 PM
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.