Introduction to Azure Analysis Services

Last updated: October 26, 2023

Azure Analysis Services is a fully managed Platform as a Service (PaaS) that provides enterprise-grade data modeling capabilities. It helps you create and manage semantic models in the cloud, enabling business intelligence (BI) applications and data visualizations. Analysis Services acts as a central source of business logic and data, making it easier for users to explore and analyze data with tools like Power BI, Excel, and Tableau.

What is Azure Analysis Services?

Azure Analysis Services is built on the same engine as SQL Server Analysis Services, providing a robust and scalable platform for data analytics. It allows you to combine data from various sources, define business logic, and serve it to end-users through BI tools. This approach centralizes business rules, ensures data consistency, and improves query performance.

Key Features:

• Cloud-based PaaS: Eliminates the need to manage infrastructure, allowing you to focus on data modeling.
• Enterprise-Grade Performance: Leverages in-memory columnar storage and query optimization for fast query responses.
• Scalability: Easily scale your service up or down based on demand.
• Integration: Seamless integration with Azure data sources, Power BI, and other BI tools.
• Security: Robust security features including Azure Active Directory integration and row-level security.
• Hybrid Support: Connect to on-premises data sources through an On-premises data gateway.

Core Concepts

Tabular Models

Azure Analysis Services primarily uses the Tabular modeling experience. Tabular models store data in a relational in-memory database. They are designed for ease of use and rapid development, especially for BI professionals familiar with relational concepts.

• Tables: Represent data entities, similar to tables in a relational database.
• Relationships: Define how tables are connected, enabling the exploration of data across different entities.
• Measures: Calculations defined using DAX (Data Analysis Expressions) to aggregate data and provide business insights (e.g., Total Sales, Average Profit).
• Calculated Columns: Columns that derive values based on expressions applied to other columns within the same table.
• Hierarchies: Organize data in a drill-down structure (e.g., Date hierarchy: Year -> Quarter -> Month).

DAX (Data Analysis Expressions)

DAX is a formula language used extensively in Analysis Services and Power BI for creating custom calculations and measures. It's a powerful language for data analysis and manipulation.

Data Sources

Analysis Services can connect to a wide range of data sources, including:

• Azure SQL Database
• Azure Synapse Analytics
• Azure Data Lake Storage
• SQL Server
• Oracle
• And many more...

For on-premises data sources, you'll need to configure the On-premises data gateway.

Use Cases

Azure Analysis Services is ideal for scenarios such as:

• Providing a consistent, single source of truth for business metrics.
• Enabling self-service BI for business users.
• Improving performance for complex analytical queries.
• Creating data models for Power BI paginated reports and interactive dashboards.
• Building custom BI solutions that require advanced data modeling capabilities.

Getting Started

To start using Azure Analysis Services:

1. Create an Azure Analysis Services resource in the Azure portal.
2. Configure your server, including choosing a pricing tier.
3. Connect to your data sources and build your tabular model using tools like SQL Server Data Tools (SSDT) or Visual Studio with Analysis Services projects.
4. Deploy your model to the Azure Analysis Services server.
5. Connect your BI tools to the deployed model.

For more detailed information, explore the following resources:

• Azure Analysis Services documentation overview
• Tabular modeling tutorial
• DAX syntax reference