Deploying Models to Azure Analysis Services

This guide walks you through the process of deploying your tabular or multidimensional models to an Azure Analysis Services (AAS) instance. Successful deployment is crucial for making your data models accessible to end-users and reporting tools.

Prerequisites

An active Azure subscription.
An Azure Analysis Services instance provisioned.
An Analysis Services project in Visual Studio (SQL Server Data Tools) or Azure Data Studio.
Permissions to deploy to your AAS instance.

Deployment Methods

There are several ways to deploy your models:

Visual Studio: The most common and integrated method during development.
SQL Server Management Studio (SSMS): For managing and deploying existing models.
Azure Data Studio: A modern, cross-platform database tool.
Programmatically: Using TOM (Tabular Object Model) or AMO (Analysis Management Objects) for automation.
Azure DevOps/CI/CD Pipelines: For automated deployments as part of your development workflow.

Deployment using Visual Studio

This is the primary method used when building models interactively.

Open Your Analysis Services Project

Launch Visual Studio and open your existing Azure Analysis Services project.

Set the Deployment Target

In the Solution Explorer, right-click on your Analysis Services project and select Properties.

Navigate to the Deployment tab.
In the Server name field, enter the fully qualified name of your Azure Analysis Services instance (e.g., yourservername.asazure.windows.net).
Optionally, set the Database name. If left blank, it will use the project name.

Deploy the Model

Right-click on the project in Solution Explorer again and select Deploy, or press F5.

Visual Studio will build the project and then deploy the model to the specified Azure Analysis Services server.

Monitor Deployment Progress

The Output window in Visual Studio will show the progress and any messages related to the deployment. Look for a "Deployment succeeded" message.

Ensure that the account you are using to deploy has the necessary permissions (e.g., 'Administrator' role) on the Azure Analysis Services instance.

Deployment using SQL Server Management Studio (SSMS)

SSMS is useful for deploying existing model files (BACPAC) or managing your AAS instance.

Connect to Your AAS Instance

Open SSMS and connect to your Azure Analysis Services server using its server name.

Deploy as a New Database

Right-click on the Databases folder in Object Explorer and select Restore.

Choose From backup file and provide the path to your model's BACPAC file.
Specify the target database name.
Configure other restore options as needed.

Deploy as an Existing Database Update

If you are updating an existing database:

Right-click on the AAS server instance.
Select New Query.
Execute an ALTER script to update the model or deploy a new version of the BACPAC.

Deployment using Azure Data Studio

Azure Data Studio provides a modern interface for managing and deploying to AAS.

Install the Azure Analysis Services Extension

If you haven't already, install the AAS extension from the Extensions Marketplace in Azure Data Studio.

Connect to Your AAS Instance

Use the AAS extension to establish a connection to your Azure Analysis Services server.

Deploy Model

Within the AAS extension interface, you'll find options to deploy your model. This typically involves selecting your project files or a BACPAC.

Azure Analysis Services Connection Strings

When connecting your applications or BI tools to your deployed model, you'll use a connection string. A typical format is:

"Provider=MSOLAP;Data Source=yourservername.asazure.windows.net;Initial Catalog=YourDatabaseName;"

For Azure AD authentication, you might use:

"Provider=MSOLAP;Data Source=yourservername.asazure.windows.net;Initial Catalog=YourDatabaseName;User ID=;Password=;AADTenant=;"

Refer to Azure documentation for the most current and secure authentication methods.

Consider using Azure Active Directory (Azure AD) authentication for secure access to your AAS instance.

Troubleshooting Common Deployment Issues

Permissions: Verify the deploying user/service principal has the correct role on the AAS server.
Server Name: Double-check the server name is accurate and accessible.
Model Compatibility: Ensure your model is compatible with the AAS service version.
Resource Limits: Large models might hit memory or processing limits during deployment.
Firewall Rules: Ensure any network security rules allow connections to the AAS instance.

For advanced scenarios like automated deployments, explore Azure DevOps pipelines with tasks specifically designed for Azure Analysis Services deployment.

Back to Azure Analysis Services Overview