Business Intelligence (Power BI)
Power BI with Azure Databricks
Section titled “Power BI with Azure Databricks”Integrating Power BI with Azure Databricks brings scalable data processing and high-performance analytics to business users. This integration allows you to publish data sets directly from Azure Databricks and connect Power BI Desktop to your clusters and SQL warehouses.
Connecting Power BI
Section titled “Connecting Power BI”There are multiple ways to connect Power BI to Azure Databricks:
- Power BI Desktop: Connect directly to Azure Databricks clusters and SQL warehouses using Partner Connect or manual configuration.
- Power BI Service: Publish data sets directly from Azure Databricks to the Power BI service.
Best Practices:
- Use Parameters: Use Power BI parameters for connection details to allow flexibility when switching between environments (e.g., Dev, Test, Prod).
- Unity Catalog: Leverage Azure Databricks Automatic Publishing to publish datasets from Unity Catalog to Power BI directly.
Optimization Cheat Sheet
Section titled “Optimization Cheat Sheet”To ensure your dashboards are efficient and query performance is optimized, adhere to the following guidelines:
Storage Mode
Section titled “Storage Mode”- DirectQuery for Facts: Use DirectQuery for large Fact tables to avoid data duplication and ensure freshness.
- Dual for Dimensions: Use Dual storage mode for Dimension tables to allow them to act as either Import or DirectQuery depending on the context, optimizing performance.
- Composite Models: Combine storage modes (Import, DirectQuery, Dual) in composite models to balance performance and data volume.
Data Access & Performance
Section titled “Data Access & Performance”- Aggregations: Implement user-defined aggregations or automatic aggregations to speed up queries on large DirectQuery datasets by caching pre-aggregated data.
- Incremental Refresh: Use table partitioning and incremental refresh for faster data loading.
- Query Reduction: Add “Apply all slicers” buttons to reports to prevent unnecessary queries from being sent while users are selecting filters.
Data Modeling
Section titled “Data Modeling”- Push Down Transformations: Perform data transformations in Databricks using SQL Views (the “Gold” layer) rather than in Power Query or DAX. The Databricks SQL engine is generally more performant for heavy lifting.
- Precompute Measures: Avoid complex DAX calculated columns. Define this logic directly in your Gold tables.
Monitoring
Section titled “Monitoring”- Performance Analyzer: Use the Power BI Performance Analyzer to identify slow visuals and bottlenecks.
- Parallelism: Check the “MaxParallelismPerQuery” and maximum connections settings to ensure your SQL warehouse is sized correctly to handle the concurrency.
