Integrating Power BI and Microsoft Azure Synapse Analytics allows organizations to deal with much larger datasets while the analytics and reporting stay very responsive in performance. Here's how to achieve integration efficiently and build highly scalable data models.
The first step is to enable the established Power BI connection to Synapse using the Azure Synapse Analytics connector built into Power BI, which supports both DirectQuery and Import modes. Use DirectQuery, which involves analyzing real-time data with a very small movement of data; however, ensure that your Synapse Database is performance-optimized for queries. Use Import mode for relatively simple transformations in which data gets cached in Power BI's in-memory engine, boosting performance for recurring queries. Partitioning large tables in Synapse can increase performance since it makes access to particular slices of data much quicker.
Next in line in the stage is optimizing the data model that Power BI is using. Then, use aggregation tables in Synapse to reduce the complexity of queries and make use of materialized views for pre-computed results of some common queries. Take advantage of Power BI to allow the creation of models comprising aggregate data, which takes fast queries and detailed data for drill-through analysis. Design your data model with relationships and hierarchies and calculate measures in Power BI to reduce dependencies on real-time computations in Synapse.
To acquire proper tuning of performance and managing resources in Azure Synapse. Use Azure Synapse SQL Pools with appropriate scaling to allocate additional resources needed due to heavy-running workloads. Use Synapse SQL-query optimization techniques for indexing, limiting joins, and avoiding heavy operations. In Power BI, use query folding so that transformations can happen in Synapse and have less execution on the engine Power BI. Continuous performance monitoring can be achieved via the performance insights of Synapse and the Performance Analyzer of Power BI.
