Microsoft Azure Analysis Services

- Using a standard data model that is easy to maintain
- Creating a layer on the data from which end users can get their data
- Only pay for what you use, scaling up and down when you want

What is Azure Analysis Services?  

Azure Analysis Services is a fully managed platform that provides enterprise-level data models in the cloud. By using modeling functions, data is combined from different sources. In addition, AAS offers you the possibility to secure all your data in a tabular form in one trusted semantic data model. This data model ensures that you can quickly perform ad-hoc data analysis with tools such as Power BI and Excel.  

What does it do?  

With Azure Analysis Services, you create a semantic model on your data. A semantic layer is located between the original database and a reporting tool. A semantic layer translates organizational data into business data that is accessible to the end-user. This includes the conditions that the organization wants to apply to the data.      

A semantic layer is a solution for end-users who do not want to do their own data preparation and modeling activities. The semantic layer makes it possible to retrieve the data in Power BI through DirectQuery, making it possible to retrieve real-time data and report on it.  

At customer A, the data model is developed in Azure Analysis Services by the IT Department of this organization. This data model is the one source of truth for Customer A. Calculations are also included in this data model. This prevents employees within the organization from using the same data but then applying different calculations to it. The end-user can then create the report themselves in a BI solution.  

Benefits of Azure Analysis Services  

  1. No hardware and/or upgrades required  
  2. 400GB of data model memory, compared to PowerBI Pro’s 1GB  
  3. No limits on the number of data refreshes like with Power BI Pro & Premium 
  4. Connect to your favorite tools, such as Power BI, Power BI Report Builder, Excel, Tableau, etc.  

Advantages of a Semantic Layer:  

  1. Data is already integrated before the end-user starts using it.   
  2. Relationships and joins are already processed in the data model for the user  
  3. Columns have been renamed to user-friendly names 
  4. Business logic and calculations are centralized in the data model  
  5. Time-oriented calculations are included in the data model (e.g., turnover year to date)  
  6. Built-in security options allow you to secure data based on the logged-in user 

Rockfeather & Azure Analysis Services  

Not everyone has the technical knowledge to work with data. We, as Rockfeather, want to allow everyone to make their own reports and/or ad hoc analysis. In Azure Analysis Services, we create a Semantic layer on the data warehouse. This is a ready-made dataset that end users can use directly in Power BI or Excel. With AAS, we make it as accessible as possible for users to get started with the data and of course, we apply the right security levels. With AAS, you make one version of the truth, but you also define, for example, how the total turnover of an organization should be added up.   

Related events

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Want to know more?

We love to hear your questions or ideas. Please contact Paul by sending him an email.