Azure Analysis Services Data Integration

Azure Data Factory (ADF)

Azure Data Factory is a cloud-based ETL and data integration service that allows you to orchestrate and automate data movement and transformation. It's a primary tool for building complex data pipelines to load data into AAS.

Key Features:

• Visual Data Pipeline Creation: Drag-and-drop interface for building pipelines.
• Extensive Connectors: Connects to hundreds of data sources and sinks.
• Data Transformation: Integrates with Azure Databricks, Azure HDInsight, and SQL Server Integration Services (SSIS).
• Scheduling and Monitoring: Robust scheduling and monitoring capabilities for pipeline execution.
• Incremental Loading: Supports efficient incremental loading strategies.

Typical Workflow with ADF:

1. Extract data from source systems.
2. Transform and cleanse data using mapping data flows or external compute.
3. Load transformed data into a staging area (e.g., Azure SQL Database, Azure Data Lake Storage).
4. Use an ADF activity (e.g., a custom activity or a script) to process the data from the staging area into AAS using TOM (Tabular Object Model) or other methods.

For loading directly into AAS, ADF can execute scripts that interact with AAS APIs, often leveraging the Tabular Object Model (TOM) or creating partitions and processing them.

Azure Synapse Analytics

Azure Synapse Analytics is an integrated analytics service that brings together data warehousing and Big Data analytics. It provides a unified experience for ingesting, preparing, managing, and serving data for immediate BI and machine learning needs.

Key Features:

• Integration with AAS: Synapse can be a powerful source for AAS models.
• Spark Pools: Leverage Apache Spark for advanced data processing.
• SQL Pools: High-performance data warehousing for structured data.
• Pipelines: Similar to ADF, Synapse pipelines can orchestrate data flows.

Synapse can be used to prepare large datasets, which can then be efficiently loaded into Azure Analysis Services, either directly or via ADF integration.

Power BI

While primarily a visualization tool, Power BI can also play a role in data integration, especially for smaller datasets or for users who are already working within the Power BI ecosystem.

Key Features:

• Power Query: A powerful data transformation and preparation engine within Power BI Desktop.
• DirectQuery and Import Modes: Connects to AAS using these modes.
• Dataflows: Power BI dataflows allow you to build reusable ETL logic in the cloud that can be consumed by Power BI datasets.

Power BI dataflows can pre-process data from various sources and store it in Azure Data Lake Storage, which can then be utilized by Azure Analysis Services.

Custom Applications

For scenarios requiring highly specific logic or integration with internal systems, custom applications can be developed.

Key Methods:

• Tabular Object Model (TOM): A .NET library for programmatic interaction with AAS.
• AMO (Analysis Management Objects): .NET assemblies for managing Analysis Services objects.
• XMLA (XML for Analysis): A SOAP-based protocol for communicating with Analysis Services.
• REST APIs: For certain management and data operations.

Custom applications can read data from any source, transform it, and then push it into AAS, creating tables, partitions, and processing them as needed.

Example using TOM to create a table:

using Microsoft.AnalysisServices.Tabular;

// ...

Server server = new Server();
server.Connect("your_aas_server_name"); // e.g., "asazure://southeastasia.asazure.windows.net/yourworkspace"

Database database = server.Databases.FindByName("YourDatabaseName");
Table table = database.Model.Tables.Add("NewSalesData");
table.KeyColumns.Add("ProductID"); // Example key column

// Define columns and data types here...

database.Model.SaveChanges();