Data Management in Azure Analysis Services

This section covers the essential aspects of managing data within your Azure Analysis Services (AAS) models. Effective data management is crucial for ensuring data accuracy, performance, and efficient refresh operations.

Data Sources

Azure Analysis Services supports a variety of data sources. You can connect to on-premises data sources using an On-premises data gateway or connect directly to cloud-based sources. Supported sources include:

Azure SQL Database
Azure SQL Data Warehouse (Synapse Analytics)
Azure Blob Storage
Azure Data Lake Storage Gen2
SQL Server
Oracle
And many more...

The choice of data source often depends on where your data resides and the performance characteristics required.

Data Import and Refresh

Data is loaded into your Analysis Services model using tabular models or multidimensional models. Once the model is deployed, you'll need to refresh the data periodically to reflect the latest changes from your source systems.

Incremental Refresh

For large datasets, performing a full data refresh can be time-consuming and resource-intensive. Incremental refresh allows you to update only the new or changed data since the last refresh. This significantly reduces refresh times and improves efficiency.

To configure incremental refresh, you typically:

Define a query in your data source to identify new or modified rows (e.g., using a date column or a watermark column).
Configure the incremental refresh settings in your Analysis Services model, specifying the date/time column and the range of data to be processed.

Note: Incremental refresh is primarily supported for Tabular models in Azure Analysis Services.

Scheduled Refresh

You can automate data refreshes using Azure Data Factory or other orchestration tools. This ensures that your data remains up-to-date without manual intervention. Common scheduling strategies include:

Daily refreshes
Hourly refreshes
Refreshes based on event triggers

Data Transformations

Before data is loaded into your Analysis Services model, it's often necessary to clean, shape, and transform it. Azure Analysis Services integrates with Power Query (available in tools like Visual Studio with Analysis Services projects or SQL Server Data Tools) to perform these transformations. Common transformations include:

Filtering rows and columns
Merging and appending queries
Creating calculated columns
Unpivoting data
Handling missing values

Tip: Optimize your data transformations to reduce the amount of data that needs to be processed during refresh, which can significantly improve performance.

Data Partitioning

For very large models, partitioning your data can improve query performance and manageability. Partitions divide a table into smaller, more manageable segments. This allows you to refresh, process, or query specific subsets of data more efficiently.

Key benefits of partitioning include:

Faster data refreshes by processing only changed partitions.
Improved query performance by allowing the engine to scan only relevant partitions.
Better resource utilization.

Monitoring and Performance Tuning

Regularly monitoring the performance of your Azure Analysis Services model is essential. Key areas to monitor include:

Data refresh times and success rates.
Query execution times.
Resource utilization (CPU, memory).

Tools like Azure Monitor and SQL Server Management Studio (SSMS) can be used for monitoring. Performance tuning might involve optimizing DAX queries, refining data models, or adjusting partitioning strategies.

Important: Ensure that your data model design is efficient and aligns with the intended usage patterns. A well-designed model is the foundation for good data management and performance.