https://github.com/databricks-solutions/power-bi-on-databricks-quickstarts
The repo contains quickstart templates and best practices using Power BI on Databricks SQL, focusing on performance, scalabilty, and operational and cost efficiency
https://github.com/databricks-solutions/power-bi-on-databricks-quickstarts
best-practices databricks databricks-sql dbsql performance-optimization power-bi powerbi
Last synced: 5 months ago
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The repo contains quickstart templates and best practices using Power BI on Databricks SQL, focusing on performance, scalabilty, and operational and cost efficiency
- Host: GitHub
- URL: https://github.com/databricks-solutions/power-bi-on-databricks-quickstarts
- Owner: databricks-solutions
- License: other
- Created: 2025-07-07T15:53:48.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-12-22T10:31:28.000Z (6 months ago)
- Last Synced: 2025-12-23T20:58:27.799Z (6 months ago)
- Topics: best-practices, databricks, databricks-sql, dbsql, performance-optimization, power-bi, powerbi
- Language: Python
- Homepage:
- Size: 14.1 MB
- Stars: 37
- Watchers: 1
- Forks: 15
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Codeowners: CODEOWNERS.txt
- Security: SECURITY.md
- Notice: NOTICE.md
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README
# :clipboard: Power BI on Databricks SQL - QuickStarts :rocket:
## Introduction
This repo contains the quickstarts demonstrating the usage of [Power BI](https://powerbi.microsoft.com/) on [Databricks SQL](https://www.databricks.com/product/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](https://www.databricks.com/sites/default/files/2025-04/2025-04-power-bi-on-databricks-best-practices-cheat-sheet.pdf) please use the QR-code below. 👇
| QuickStart Samples repo | Best Practices Cheat Sheet |
| ------ | ----------- |
|
| 
|
## Table of Contents
| # | Folder | Description |
| -- | ---------------------------------------------------------------------------------------------- | ---------------------------------------------------- |
| 00 | [Best Practices Cheat Sheet](00.%20Best%20Practices%20Cheat%20Sheet/) | Power BI on Databricks Best Practices Cheat Sheet |
| 01 | [Connection Parameters](01.%20Connection%20Parameters/) | Use Power BI parameters to efficiently manage connections to Databricks SQL |
| 02 | [Storage Modes](./02.%20Storage%20Modes/) | Use storage modes efficiently - DirectQuery vs Dual vs Import |
| 03 | [Logical Partitioning](./03.%20Logical%20Partitioning/) | Improving data refresh performance with Power BI partitioning |
| 04 | [Query Parallelization](./04.%20Query%20Parallelization/) | Improve Power BI DirectQuery performance by tuning query parallelization |
| 05 | [User-defined Aggregations](./05.%20User-defined%20Aggregations/) | Improve Power BI DirectQuery performance by using User-defined aggregations |
| 06 | [Dynamic M Query Parameters](./06.%20Dynamic%20M%20Query%20Parameters/) | Use Dynamic M Query Parameters for better control over SQL-query generation and performance optimization |
| 07 | [Query optimization using PK](./07.%20Query%20optimization%20using%20PK/) | Query optimization using primary key constraints |
| 08 | [Automatic aggregations](./08.%20Automatic%20aggregations/) | Improve Power BI DirectQuery performance by using Automatic aggregations |
| 09 | [Private Connections](./09.%20Private%20Connections/) | Private connections to Databricks Workspaces from Power BI Service |
| 10 | [Pushdown Calculations](10.%20Pushdown%20Calculations/) | Improve Power BI DirectQuery performance by pushing calculations down to Databricks SQL |
| 11 | [Generated vs Persisted dimensions](./11.%20Generated%20vs%20Persisted%20dimension/) | Improve Power BI DirectQuery performance by using generated vs persisted dimension tables |
| 12 | [Collations](./12.%20Collations/) | Use Collations for case-insensitive search and filtering |
| 13 | [M2M OAuth Credentials Management](./13.%20M2M%20OAuth%20Credentials%20Management/) | Use M2M OAuth authentication for non-interactive workloads |
| 14 | [Data Source Default Max Connections](./14.%20Data%20Source%20Default%20Max%20Connections/) | Use Data Source Default Max Connections for high-concurrency workloads |
| 15 | [Calendar-based Time Intelligence](15.%20Calendar-based%20Time%20Intelligence/) | Use Calendar-based Time Intelligence for more efficient SQL-queries |
## How to get help
Databricks support doesn't cover this content. For questions or bugs, please open a GitHub issue and the team will help on a best effort basis.
## License
© 2025 Databricks, Inc. All rights reserved. The source code in this repository is provided subject to the [Databricks License](https://databricks.com/db-license-source). All included or referenced third party libraries are subject to the licenses set forth below.
| library | description. | license | source |
|----------------------------------------|----------------------------|------------|-------------------------------------------------------------------|
| PowerBI-Developer-Samples | Power BI Developer Samples | MIT | [Github](https://github.com/microsoft/PowerBI-Developer-Samples/) |