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

2. Creating Enterprise Grade BI Models with Azure Analysis Services
Christian Wade
Senior Program Manager
@_christianWade

3. Azure Analysis Services
Enterprise grade analytics engine as a service
Build rich semantic models
Transform complex data into business user friendly semantic models
Gain insights at the speed of thought
Gain instant insights with in-memory cache using your preferred visualization tools
Proven technology
Based on powerful, proven SQL Server Analysis Services
Provision and scale with ease
Easy to deploy, scale, and manage as a platform-as-a-service solution
Key points: Summarize key benefits for Azure Analysis Services
Azure Analysis Services helps you transform complex data from different data sources into a BI semantic model, so users in your organization can easily gain insights by connecting to the data models using tools like Excel, Power BI, and others to create reports and perform ad hoc data analysis
Talk Track
Transform Complex Data into rich BI semantic models: Azure Analysis Services Analysis Services helps you transform complex data into a single business user friendly data model making it easy for business users to understand and analyze data across different data sources.
Gain instant insights with in-memory cache using your preferred visualization tools : Not only can business users get insights from data easily using their preferred data visualization tool, whether it is Power BI, Excel or other major data visualization tools, but with the in-memory cache capabilities of Azure Analysis Services, users can gain insights over billions of rows of data at the speed of thought
Proven Technology: Azure Analysis Services is based on the proven analytics engine in SQL Server 2016 Analysis Services, that has helped organizations turn complex data into a trusted, single source of truth for years. This means that BI professionals who are familiar with SQL Server Analysis Services, tabular models can get started quickly and do not need to learn new tools or skills.
Analytics engine as-a-service (provision fast, scale faster): The same proven enterprise grade BI platform is now available as a fully managed service in Azure. With the power of the trusted Microsoft Cloud, you do not need to manage infrastructure on-premises and can benefit from the scalability of the cloud. Additionally you can use Azure Resource Manager to create and deploy an Azure Analysis Services instance within seconds, and use backup restore to quickly move your existing models to Azure Analysis Services and take advantage of the scale, flexibility and management benefits of the cloud. Scale up, scale down, or pause the service and pay only for what you use.
Azure Analysis Services is built for Hybrid BI - Organizations store data in the cloud and on-premises. Azure Analysis Services is built for hybrid data. Data can be access in the cloud, on-premises or a combination of both, enabling a hybrid solution. So - you do not have to move on-premises data to the cloud.
To summarize, Azure Analysis Services is simple to use – it is easy to get started, you can use your existing skills to create BI semantic models, and your favorite data visualizations tools to analyze your data.

4. Self-Service & Corporate BI …
11/7/ :43 PM
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”.
… a centralized team in overall charge that finds and then promotes interesting analysis across the entire organization.
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.

5. Microsoft BI Platform DATA Model Analyze & author Deliver Visualize
Power BI
Azure Analysis Services
Power BI
Web
Embedded in your apps
Mobile
Cloud
On –premises data gateway
On-premises
Key points:
Azure Analysis Services, is based on the proven analytical engine in SQL Server 2016 Analysis Services. Customers can access data sources across on-premises and the cloud, model that data, and provide business users with a simplified view of their data to enable interactive self-service BI and data discovery using their preferred data visualization tool.
Easy to deploy, scale, and manage as a platform-as-a-service solution
Create a provision an Azure Analysis Service server in seconds.
Elastic scale to move up and down with your business needs.
Reduce the burden of managing infrastructure with a fully managed Analysis Services in the cloud.
Integrate data from anywhere. Build your semantic model from modern data sources like Azure SQL Database and Azure SQL DW as well as on-prem data like SQL Server 2016
Connect to your semantic model with your favorite BI visualization tool and interact with data at scale and the speed.
SQL Server
Analysis Services
SQL Server Reporting Services
Excel
Power BI Desktop

6. Azure Analysis Services
11/7/ :43 PM
Demo Architecture
Cloud data sources
Azure Analysis Services
Visualization & Insights
SQL Data
Warehouse
Power BI
Blob Storage
Configuration & logging
Cloud processing
SQL Database
Azure Functions
Authoring & Development
Self-Service Authoring
Power BI Desktop
Power BI Desktop
Visual Studio
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

7. Demo

8. Corporate BI Features in Demo
11/7/ :43 PM
Corporate BI Features in Demo
Fine-grain partition management through API
BI schema-compare for application lifecycle management (ALM)
Deployment methodologies
Other typical corporate BI features …
Monitoring
Advanced calculations
Gartner IT Glossary:
Kurt Schlegel general discussion (not quote):
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

9. 11/7/ :43 PM
Further Info
Power BI Governance & Deployment Approaches:
Gartner Research Paper by Kurt Schlegel et al. (2016): Create a Centralized and Decentralized Organizational Model for Business Intelligence
Analysis Services Team Blog:
Analysis Services Git Repo:
/AsPartitionProcessing/Automated Partition Management for Analysis Services Tabular Models.pdf
/BismNormalizer/Model Comparison and Merging for Analysis Services.pdf
Gartner IT Glossary:
Kurt Schlegel general discussion (not quote):
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

10. Thank you for Attending!
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