Azure Analysis Services Data Sources

Data Sources in Azure Analysis Services

Azure Analysis Services enables you to connect to a variety of data sources to build your tabular or multidimensional models. Understanding how to connect to and manage these sources is crucial for effective data modeling.

Supported Data Source Types

Azure Analysis Services supports connections to a wide range of data sources, including:

Relational Databases: SQL Server, Azure SQL Database, Azure Synapse Analytics, Oracle, MySQL, PostgreSQL, etc.
Data Warehouses: Azure Synapse Analytics, SQL Server Parallel Data Warehouse.
Cloud Data Services: Azure Blob Storage, Azure Data Lake Storage, Azure Cosmos DB.
Flat Files: CSV, Text files.
Online Services: Salesforce, Dynamics 365.
Other: OData feeds, SharePoint lists.

Connecting to Data Sources

You can connect to data sources using tools like SQL Server Data Tools (SSDT) for Visual Studio or by using tabular model script commands (TMSL).

Using SQL Server Data Tools (SSDT)

Open your Azure Analysis Services project in SSDT.
In the Solution Explorer, right-click on the Data Sources folder and select Add Data Source.
The Table Import Wizard or Data Source Wizard will appear.
Choose your data source type from the list.
Provide the necessary connection details, such as server name, database name, authentication method, and credentials.
Click Test Connection to ensure the details are correct.
Click OK or Next to complete the connection.

Note: For cloud-based data sources like Azure SQL Database or Azure Synapse Analytics, ensure that your Azure Analysis Services firewall rules are configured to allow access, or use VNet service endpoints for enhanced security.

Connection Strings

Connection strings contain all the information needed to establish a connection to a data source. Here's an example of a SQL Server connection string:

Server=myserver.database.windows.net;Database=mydatabase;UID=myuser@myserver;PWD=mypassword;Encrypt=True;TrustServerCertificate=False;Connection Timeout=30;

Data Source Management

Once connected, you can manage your data sources within the Azure portal or through scripting:

Azure Portal: Navigate to your Azure Analysis Services resource. Under Data model, you can view and manage existing data sources.
Tabular Model Scripting Language (TMSL): Use TMSL commands to create, update, or delete data source objects programmatically.

Best Practices

Use Secure Authentication: Prefer Azure Active Directory authentication over SQL authentication whenever possible for enhanced security.
Minimize Data Transfer: Filter and shape your data as early as possible in the data source connection to reduce the amount of data transferred to Analysis Services.
Monitor Performance: Regularly monitor data source connection performance and optimize queries for efficiency.
Understand Data Source Limitations: Be aware of any specific limitations or requirements for the data sources you are connecting to.

For detailed information on specific data source types and advanced configuration options, please refer to the official Microsoft documentation.