Design and build composite models, including aggregations

Designing and building composite models in the context of implementing enterprise-scale analytics solutions using Microsoft Azure and Microsoft Power BI is a critical aspect of creating efficient and interactive analytics dashboards. Composite models allow you to combine different data sources, leverage aggregations, and optimize performance for large datasets. In this article, we will explore the concepts and techniques involved in designing and implementing composite models.

Understanding Composite Models

Composite models enable the integration of multiple data sources in a single model, including DirectQuery, Import, and live connections. With composite models, you can seamlessly query and analyze data from various sources without the need for data duplication or complex data transformation.

There are three primary components that make up a composite model:

1. Imported Tables: These are traditional tables that are loaded into memory and can be used in direct calculations, such as DAX expressions or measures. Imported tables are typically used for smaller datasets or when you need to perform complex calculations.
2. DirectQuery and Live Connection Tables: These tables are queried directly from the underlying data source at query time. They are not loaded into memory but can be used alongside imported tables in calculations. DirectQuery or live connection tables are commonly used for larger datasets that are not suitable for complete import due to size or real-time requirements.
3. Aggregations: Aggregations are pre-calculated summaries of data that are stored in memory to improve query performance. By creating aggregations on DirectQuery or live connection tables, you can significantly enhance the query response time. Aggregations can be defined at various levels of granularity, providing different levels of summary data.

Implementing Composite Models

To implement composite models in Power BI, you follow these general steps:

Step 1: Import necessary tables

Identify the key tables that need to be imported into Power BI for direct calculations. These tables should be relatively small and used frequently for calculations.

Step 2: Configure DirectQuery or live connection

Connect to the data sources for the larger datasets that don’t fit the complete import criteria. Configure DirectQuery or live connections to ensure real-time data availability and query optimization.

Step 3: Create relationships

Establish relationships between the imported tables and the DirectQuery or live connection tables. This allows you to seamlessly combine data from different sources when building reports and visualizations.

Step 4: Define aggregations

To improve performance, define aggregations on the DirectQuery or live connection tables. Aggregations summarize data at different granularities and provide faster query response times.

Step 5: Utilize aggregations in reports

Leverage the defined aggregations in your reports and visuals. Power BI automatically uses the appropriate aggregation level based on the user’s interaction and query.

Step 6: Monitor and optimize performance

Regularly monitor the performance of your composite models and make necessary adjustments. Fine-tuning aggregations, optimizing data sources, or refining data transformations can help enhance overall performance.

Aggregations in Composite Models

Aggregations play a vital role in optimizing the query performance of composite models. They provide summary levels of data and reduce the query load on the direct query or live connection tables. When designing aggregations, consider the following:

• Define appropriate aggregation levels based on the analysis requirements. Aggregate data at different levels of granularity to balance performance and detail.
• Utilize automatic aggregations where possible. Power BI can automatically generate aggregations for commonly accessed tables without the need for manual configuration.
• Take advantage of the query routing feature in Power BI. This feature directs queries to the most suitable aggregation based on the specific needs of the report or visualization.
• Regularly monitor query performance and make adjustments as necessary. Fine-tuning aggregations and adjusting granularity levels can lead to significant performance improvements.

Conclusion

Designing and building composite models are essential steps in implementing enterprise-scale analytics solutions using Microsoft Azure and Microsoft Power BI. By effectively utilizing composite models, you can seamlessly integrate different data sources, leverage aggregations, and optimize query performance for large datasets. Remember to carefully consider the requirements of your analysis and fine-tune your aggregations to strike the right balance between performance and data granularity. With composite models, you can create interactive and efficient analytics dashboards that empower users to gain valuable insights from their data.