This repo contains the quickstarts demonstrating the usage of Power BI on Databricks SQL. The objective of these quickstarts is to demonstrate reference implementation and some of the best practices using Power BI on Databricks SQL.
For quick access to this repository and Best Practices Cheat Sheet please use the QR-code below. 👇
| QuickStart Samples repo | Best Practices Cheat Sheet |
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| # | Folder | Description |
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| 00 | Best Practices Cheat Sheet | Power BI on Databricks Best Practices Cheat Sheet |
| 01 | Connection Parameters | Use Power BI parameters to efficiently manage connections to Databricks SQL |
| 02 | Storage Modes | Use storage modes efficiently - DirectQuery vs Dual vs Import |
| 03 | Logical Partitioning | Improving data refresh performance with Power BI partitioning |
| 04 | Query Parallelization | Improve Power BI DirectQuery performance by tuning query parallelization |
| 05 | User-defined Aggregations | Improve Power BI DirectQuery performance by using User-defined aggregations |
| 06 | Dynamic M Query Parameters | Use Dynamic M Query Parameters for better control over SQL-query generation and performance optimization |
| 07 | Query optimization using PK | Query optimization using primary key constraints |
| 08 | Automatic aggregations | Improve Power BI DirectQuery performance by using Automatic aggregations |
| 09 | Private Connections | Private connections to Databricks Workspaces from Power BI Service |
| 10 | Pushdown Calculations | Improve Power BI DirectQuery performance by pushing calculations down to Databricks SQL |
| 11 | Generated vs Persisted dimensions | Improve Power BI DirectQuery performance by using generated vs persisted dimension tables |
| 12 | Collations | Use Collations for case-insensitive search and filtering |
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© 2025 Databricks, Inc. All rights reserved. The source in this notebook is provided subject to the Databricks License. All included or referenced third party libraries are subject to the licenses set forth below.
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