Processing Analysis Services Multidimensional Models

Introduction to Processing Analysis Services

Processing is a critical step in working with SQL Server Analysis Services (SSAS) multidimensional models. It involves loading data from data sources into the Analysis Services cube structure, making it available for querying and analysis. Proper processing ensures data accuracy, performance, and usability.

This document will guide you through the various methods, options, and best practices for processing your Analysis Services multidimensional models.

Understanding Processing Methods

Analysis Services offers several ways to process your data, each suited for different scenarios:

Full Process

A full process rebuilds the entire cube from scratch. This method is typically used when:

• The underlying data source schema has changed significantly.
• You need to ensure absolute data consistency and remove any potential data anomalies from previous processes.
• It's the initial load of data into a new or existing cube.

A full process involves processing all objects in the cube, including dimensions, measure groups, and partitions.

Incremental Process

An incremental process updates only the new or changed data since the last process. This is highly efficient for large datasets where only a subset of data changes frequently.

Key benefits of incremental processing:

• Significantly reduces processing time.
• Minimizes downtime for users.
• Requires careful configuration of source data and dimension relationships.

Incremental processing can be applied at the partition level for measure groups.

Processing Partitions

Partitions allow you to divide large tables or cubes into smaller, manageable units. You can process individual partitions independently.

This is useful for:

• Processing only the latest data without affecting historical data.
• Distributing processing workload across multiple servers or at different times.
• Handling specific data refresh requirements for different time periods.

Processing a partition involves processing its associated dimension and fact data.

Key Processing Options

When initiating a process, you have several options that affect how the data is handled and made available.

Online vs. Offline Processing

Online Processing: Allows users to continue querying the cube while it is being processed. This is achieved by Analysis Services maintaining two copies of the cube data during processing. Once processing is complete, the new data is swapped in. This minimizes disruption but can consume more memory and resources.

Offline Processing: The cube is taken offline and unavailable for querying during the entire processing operation. This method is simpler and can be faster in some cases as it doesn't require maintaining dual copies of data. It is suitable for scenarios where scheduled downtime is acceptable.

Processing Events

Analysis Services provides a rich set of events that are triggered during the processing lifecycle. These events can be used to:

• Log processing status and errors.
• Trigger custom logic or external processes.
• Integrate with ETL tools for more sophisticated data management.

Common events include BeforeProcess, AfterProcess, and events related to specific object processing like dimensions and measure groups.

Best Practices for Processing

• Schedule Processing: Automate your processing tasks using SQL Server Agent jobs or other scheduling tools. Schedule them during off-peak hours to minimize impact on end-users.
• Monitor Performance: Regularly monitor processing times and resource utilization (CPU, memory, disk I/O). Identify bottlenecks and optimize your data sources, queries, and Analysis Services configurations.
• Error Handling: Implement robust error handling in your processing scripts and ETL processes. Log errors effectively and establish procedures for addressing them promptly.
• Dimension Processing: Process dimensions before fact data. Consider batching dimension updates if you have very large dimensions.
• Partitioning Strategy: Design your partitions carefully based on data volume, access patterns, and refresh frequency. This is crucial for efficient incremental processing.
• Full vs. Incremental: Choose the processing method that best suits your data volatility and business requirements. Incremental processing is generally preferred for frequently updated data.
• Test Thoroughly: Always test your processing logic and schedules in a development or staging environment before deploying to production.

Conclusion

Effective processing is fundamental to a well-performing and accurate Analysis Services solution. By understanding the different processing methods, utilizing appropriate options, and adhering to best practices, you can ensure your multidimensional models are always up-to-date and ready for insightful analysis.

For more advanced scenarios, explore scripting with AMO (Analysis Management Objects) or XMLA (XML for Analysis) to programmatically manage processing operations.

<Batch xmlns="http://schemas.microsoft.com/analysisservices/2003/engine" Transaction="true">
    <Alter Action="update" ObjectExpansion="ExpandObjectProperties" State="Visible">
        <Object >
            <DatabaseID>YourDatabaseName</DatabaseID>
            <CubeID>YourCubeName</CubeID>
            <MeasureGroupID>YourMeasureGroupName</MeasureGroupID>
            <PartitionID>YourPartitionID</PartitionID>
        </Object>
        <ObjectDefinition>
            <Partition>
                <Name>YourPartitionID</Name>
                <ProcessingState>Processed</ProcessingState>
            </Partition>
        </ObjectDefinition>
    </Alter>
    <Process xmlns="http://schemas.microsoft.com/analysisservices/2003/engine" State="Unprocessed">
        <Object >
            <DatabaseID>YourDatabaseName</DatabaseID>
            <CubeID>YourCubeName</CubeID>
            <MeasureGroupID>YourMeasureGroupName</MeasureGroupID>
            <PartitionID>YourPartitionID</PartitionID>
        </Object>
        <Type>ProcessFull</Type>
        <Online>true</Online>
    </Process>
</Batch>