Analysis Services Archives - AzureOps

Azure Analysis Services Model Missing

Kunal Rathi — Thu, 01 May 2025 14:19:00 +0000

Last week, our Azure Analysis Services Model suddenly disappeared and all the PowerBI reports using that model stopped working or connection to AAS model from SSMS not working. If the account that was the owner of the Azure Analysis Services (AAS) model, and it got deleted, the model might have become inaccessible because there are no active administrators left. In this article, we will see how to fix Azure Analysis Services model missing issue.

Assign a New Admin via Azure Portal

Go to the Azure Portal.
Navigate to Azure Analysis Services.
Find the AAS server where your model was deployed.
Under Settings, click Administrators.
Add a new administrator (Global Admins or Subscription Owners can do this).
Save the changes.
Delete the inactive admin account if any from the admin list.

With the addition of a new admin account, the Azure Analysis Services model missing issue is resolved.

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Connect to Azure Analysis Services from Power BI

Kunal Rathi — Thu, 14 Sep 2023 20:24:16 +0000

Azure Analysis Services is an analytical data engine (VertiPaq) used in decision support and business analytics. It provides enterprise-grade semantic data models for business reports and client applications such as Power BI, Excel, Reporting Services reports, and other data visualization tools. There are various ways to connect to Azure analysis services for browsing and data analysis. This article describes how to connect to Azure analysis services from Power BI.

Prerequisites:
1. Download and install the latest Power BI desktop.
2. Azure active directory user has rights to connect to Azure analysis services.

Steps to connect to Azure analysis services from Power BI desktop.

1. Open Power BI Desktop.

2. Click on Get Data in the Home ribbon.

3. Click on Azure on the left part and select Azure Analysis Services database, as shown in the image below.

4. Enter the server name “asazure://westeurope.asazure.windows.net/youraasname” and Select the ‘Connect Live’ radio button. Click OK. It may ask you to log in. Select the ‘Microsoft account’ option and authenticate using your Azure active directory credentials.

5. Select the database NameOfYourASDB and click OK.

6. Once you click OK, you should see the model as shown below in the picture.

Pro tips:
1. Microsoft provides two types of analysis services models: multidimensional and tabular. Read this article to know how they differ from each other.
2. Refer to this post to connect to Azure analysis services from SSMS.

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Connect to Azure Analysis Services from SSMS

Kunal Rathi — Sun, 23 Apr 2023 10:59:42 +0000

Prerequisites:
1. Download and install the latest (>18.x) SQL Server Management Studio.
2. Azure active directory user has rights to connect to Azure analysis services.

Steps to connect to Azure analysis services using SQL Server Management Studio.

Open SSMS
Click on Connect in Object Explorer.
Click on Analysis Services.
In the Server name field, put Azure analysis server name (e.g., asazure://westeurope.asazure.windows.net/yourservername).
From the Authentication drop-down, select “Azure Active Directory – Universal with MFA”.
In the User name, put your Domain email address and click Connect.

It may ask you to log in using the email address to MS Azure, and it should take you to databases.

Right-click on the database and click on browse, as shown below

You will see the model as shown in the image below. Drag and Drop the objects from the Model pane and perform the required analysis. Click the auto-execute button in the SSMS toolbar to enable the automatic execution of the queries when you change objects in the execution pane.

Pro tips:
1. Microsoft provides two types of analysis services models; multidimensional and tabular. Read this article to know how they differ from each other.
2. Refer to this post to connect to Azure analysis services from Power BI desktop.

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The post Connect to Azure Analysis Services from SSMS appeared first on AzureOps.

SSAS Tabular vs Multidimensional model

Kunal Rathi — Tue, 25 Oct 2022 14:00:55 +0000

Analysis Services is an analytical data engine (VertiPaq) used in decision support and business analytics. It provides enterprise-grade semantic data models for business reports and client applications such as Power BI, Excel, Reporting Services reports, and other data visualization tools. Two types of Analysis Services models are available; the Multidimensional model and the Tabular model. While both these models have significance, here is a quick comparison between SSAS Tabular vs. Multidimensional models.

SSAS Tabular vs Multidimensional

#	Tabular Model	Multidimensional
Aggregations	No	Yes
Data Compression	Up to 1/10th of the original data	Up to 1/3th of the original data
Data Access Modes	Cached mode: All the data is loaded in memory, and all queries are answered from there. Direct Query (Pass-through): Bypass the in-memory model, allowing client applications to query data directly on the database	MOLAP: The source data is pulled from the relational store, the required aggregation is performed within Analysis Services, and finally, the data is stored in the Analysis Services server in a compressed and optimized multidimensional format. ROLAP: This does not pull data from the underlying relational database source to the OLAP server. Rather, the cube detail data and the aggregated data stay in the relational database source. To store the calculated aggregation, the database server creates additional database objects (indexed views).
Resources	More memory (RAM) & CPU will be required if the cached model is used.	Limited memory and CPU can suffice the requirement. (Will depend on the workload and kind of computations)
Data Language Layer	Uses DAX (Data Analysis Expression). The syntax of DAX is very similar to one of Excel formulas, and DAX concepts are similar to relational database concepts and, therefore, easier to grasp for a developer without any experience with Multidimensional concepts. Even though using DAX is rather simple for basic computations (margin, ratio, etc.), it becomes more complex for advanced computations.	Uses MDX (Multidimensional Expression) as its data language. MDX is a very powerful language but requires a thorough understanding of multidimensional concepts and, therefore, is often perceived as rather complex and difficult to learn.
Custom Assemblies	No	Yes
composite key relationships	Not supported directly, we need to define calculated column with composite columns data concatenated	Supported
Reasons to go for	• If you want to use DAX (based on Excel formulas, but is often seen as having a steep learning curve) • If you want to use Power View, PowerPivot, and SharePoint Excel Services • If you need to use multiple data sources • If you need extreme speed in your data queries • If your development timeline is compressed	• If your dataset is extremely large (in terabytes) • If you need to write back to support • If you need complex calculations, scoping, and named sets • If your solution requires complex modeling
Pros	• More performant than OLAP in the majority of the case. • Easier & faster to develop than a Multidimensional model. • Technology for the future. • Integration with Power Pivot.	• Matured Technology. • Scalable Technology; able to handle very large volumes of data. • Able to cope with advanced modeling/ computations requirements.
Cons	• Limited to RAM available (= midsized project). • Missing some advanced computations available with MDX.	• Cannot be used with Power View. • No major innovations to expect in this product in the future. • Higher complexity than Tabular

Which SSAS model is suitable for your reporting use case?

Based on the above comparison, below are some reporting scenarios that state which model will outperform another.

Scenario	Multidimensional MOLAP	Tabular In-Memory	More Performant
Report on Low granularity data	Read atomic data from disk.	Read columnar data from RAM.	Tabular In–Memory.
Report on aggregated data with no predefined aggregation	Read atomic data from disk. Aggregate data in Memory	Read columnar data from RAM. Aggregate data in Memory	Tabular In–Memory.
Report on aggregated data with predefined aggregations on Cold Cache	Read aggregated data from disk.	Read columnar data from RAM. Aggregate data in Memory	Comparable
Report on aggregated data with predefined aggregations on Warm Cache	Read aggregated data from RAM.	Read columnar data from RAM. Aggregate data in Memory	Multidimensional

We have seen a quick comparison between SSAS Tabular vs Multidimensional models.

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