Deploying Azure Analysis Services

This document provides a comprehensive guide on deploying Azure Analysis Services (AAS) instances, covering various methods and best practices.

Note: Azure Analysis Services is a powerful Platform as a Service (PaaS) offering that simplifies the creation and management of enterprise-grade semantic data models.

Deployment Methods

You can deploy Azure Analysis Services using several approaches:

1. Azure Portal

The Azure Portal is the most user-friendly way to deploy an AAS instance for quick setup and management.

  1. Navigate to the Azure Portal.
  2. Click on Create a resource.
  3. Search for "Azure Analysis Services".
  4. Select the "Azure Analysis Services" service and click Create.
  5. Fill in the required details: Subscription, Resource Group, Server name, Region, and choose a Generation and Capacity tier.
  6. Review and create the instance.

2. Azure Resource Manager (ARM) Templates

ARM templates allow for declarative deployment and infrastructure as code, enabling repeatable and automated deployments.

Here's a simplified example of an ARM template for deploying an AAS instance:


{
  "$schema": "https://schema.management.azure.com/schemas/2015-01-01/deploymentTemplate.json#",
  "contentVersion": "1.0.0.0",
  "parameters": {
    "serverName": {
      "type": "string",
      "metadata": {
        "description": "Name of the Analysis Services server."
      }
    },
    "location": {
      "type": "string",
      "defaultValue": "[resourceGroup().location]",
      "metadata": {
        "description": "Location for the Analysis Services server."
      }
    },
    "skuName": {
      "type": "string",
      "defaultValue": "S1",
      "allowedValues": [
        "S0",
        "S1",
        "S2",
        "S3"
      ],
      "metadata": {
        "description": "The SKU name of the Analysis Services server."
      }
    },
    "capacity": {
      "type": "int",
      "defaultValue": 1,
      "metadata": {
        "description": "The capacity of the Analysis Services server."
      }
    }
  },
  "resources": [
    {
      "type": "Microsoft.AnalysisServices/servers",
      "apiVersion": "2017-08-01",
      "name": "[parameters('serverName')]",
      "location": "[parameters('location')]",
      "sku": {
        "name": "[parameters('skuName')]",
        "capacity": "[parameters('capacity')]"
      },
      "properties": {
        "asAdministrator": {
          "สมาชิก": [
            "mailto:youradmin@example.com"
          ]
        }
      }
    }
  ]
}

You can deploy ARM templates using Azure CLI, Azure PowerShell, or the Azure Portal.

3. Azure CLI

The Azure Command-Line Interface (CLI) provides a powerful way to script and automate deployments.


az ams create --resource-group myResourceGroup --name myAnalysisServicesServer --sku S1 --location westus2

4. Azure PowerShell

Similar to Azure CLI, Azure PowerShell offers cmdlets for managing Azure resources.


New-AzAnalysisServicesServer -ResourceGroupName "myResourceGroup" -Name "myAnalysisServicesServer" -SkuName "S1" -Location "West US 2"

Deployment Considerations

SKU and Capacity

Choose the appropriate SKU (e.g., Developer, Basic, Standard) and capacity that matches your performance and scalability needs. The Standard tier offers multiple capacity levels (S0-S3) with varying performance and features. The Developer tier is suitable for development and testing environments.

Region Selection

Deploy your AAS instance in the same Azure region as your data sources and consumers to minimize latency and improve performance.

Administrator Configuration

Specify at least one administrator for your AAS instance. This user or group will have full control over the server.

Tip: For production environments, consider using a dedicated service principal or Azure AD group for administration rather than individual user accounts.

Network Security

Configure network security by setting up firewalls and virtual network service endpoints to control access to your AAS instance.

Post-Deployment Steps

After deploying your AAS instance, you will typically need to:

  • Connect to the server using tools like SQL Server Management Studio (SSMS) or Visual Studio.
  • Create or deploy your semantic model (e.g., Tabular or Multidimensional).
  • Configure data sources and refresh schedules.
  • Grant access to end-users and applications.
Important: Always refer to the latest Azure documentation for the most up-to-date information on pricing, features, and best practices.

Last updated: October 26, 2023