Azure Analysis Services Development

Getting Started with Azure Analysis Services Development

This section guides you through the process of developing data models for Azure Analysis Services. We'll cover setting up your development environment, creating projects, connecting to data sources, and implementing effective data modeling techniques.

Setting Up Your Development Environment

A robust development environment is crucial for efficient Azure Analysis Services development. Here are the key tools you'll need:

Visual Studio

Visual Studio is the primary Integrated Development Environment (IDE) for developing Azure Analysis Services models. Ensure you have a recent version installed.

SQL Server Data Tools (SSDT)

SSDT is a set of add-ons for Visual Studio that enables you to develop SQL Server Analysis Services, Reporting Services, and Integration Services projects. Install the Analysis Services projects extension for Visual Studio.

Tip

You can download the latest version of SSDT from the Microsoft Download Center or install it directly from the Visual Studio Marketplace.

Azure PowerShell

Azure PowerShell provides cmdlets for managing Azure resources, including Azure Analysis Services. It's useful for scripting and automating deployments.

Azure CLI

The Azure Command-Line Interface (CLI) offers another way to interact with Azure resources. It's a powerful tool for scripting and automation.

Creating Analysis Services Projects

In Visual Studio, you can create a new Azure Analysis Services tabular model project. This project will contain your data model definitions.

  1. Open Visual Studio.
  2. Go to File > New > Project...
  3. Search for "Analysis Services" and select Microsoft Azure Analysis Services Tabular Project.
  4. Provide a project name and location, then click Create.

Connecting to Data Sources

Your Analysis Services model needs to connect to one or more data sources to retrieve data. Azure Analysis Services supports a wide range of sources.

Relational Databases

Connect to SQL Server, Azure SQL Database, and other relational databases using appropriate connection strings and credentials.

Cloud Sources

Integration with Azure data services like Azure Data Lake Storage, Azure Blob Storage, and Azure Cosmos DB is seamless.

Data Gateways

For on-premises data sources, you'll need to configure an On-premises data gateway to enable secure connectivity from Azure Analysis Services.

Data Modeling Techniques

Effective data modeling is key to building performant and user-friendly Analysis Services solutions.

Tables and Relationships

Import data into tables and define relationships between them to create a coherent data model. Star and snowflake schemas are common patterns.

Measures and Calculations

Use DAX (Data Analysis Expressions) to create measures that perform calculations and derive insights from your data. Measures are dynamic and respond to user selections.

-- Example DAX measure
Total Sales = SUM(Sales[Amount])

Hierarchies

Create hierarchies (e.g., Year > Quarter > Month) to enable drill-down analysis for users.

Perspectives

Perspectives allow you to define subsets of your model, showing users only the tables, columns, and measures relevant to their specific business needs.

Translations

Support multiple languages by adding translations for object names and descriptions within your model.

Developing with T-SQL

While DAX is the primary language for calculations and queries in tabular models, T-SQL can be used within the data source views to shape and transform data before it's loaded into the model.

Developing with MDX and DAX

DAX is used for creating measures and calculations within the tabular model itself. MDX (Multidimensional Expressions) can also be used for querying, especially if you're familiar with older Analysis Services versions or migrating from multidimensional models.

DAX Basics

DAX is a powerful formula expression language used for creating calculated columns, measures, and row-level security in Analysis Services, Power BI, and Power Pivot.

MDX Basics

MDX is a query language for OLAP (Online Analytical Processing) data. It's particularly effective for complex slicing and dicing of multidimensional data.

Optimizing Queries

Writing efficient DAX and MDX queries is crucial for a responsive user experience. Understand query plans, filter context, and aggregation performance.

Note

For optimal performance, focus on creating efficient DAX measures and structuring your model appropriately. Avoid overly complex calculations that can slow down query execution.

Deployment and Management

Once your model is developed, you'll need to deploy it to an Azure Analysis Services instance and manage its lifecycle.

Deploying Projects

Deploy your tabular model directly from Visual Studio to your Azure Analysis Services instance. You can also use deployment scripts with Azure PowerShell or Azure CLI.

Managing Models

Monitor performance, manage data refreshes, and configure security settings for your deployed models through the Azure portal or management tools.

Security Considerations

Implement security at different levels: Azure resource-level security, Azure Analysis Services database roles, and row-level security within the model itself.

Best Practices

Follow these best practices for developing robust and scalable Azure Analysis Services solutions: