Power BI Integration with Databricks

[Abhilash_P]

There are multiple ways to connect Power BI to Databricks, including the native Azure Databricks connector (DirectQuery or Import mode) and publishing datasets directly from Databricks.

A. Direct Integration (Publish to Power BI):

If your organization uses Unity Catalog and Databricks SQL Warehouses, you can directly publish semantic models from Databricks to Power BI. This method makes curated tables instantly available for analysis without needing manual connection setups.

Steps:

1. Navigate to your target schema or table in Unity Catalog.
   
   
2. Click “Use with BI tools” or “Open in a dashboard.”
   
   
3. Select “Publish to Power BI workspace.”
   
   
4. Log in to Power BI, choose your workspace and dataset options.
   
   
5. Click Publish

Within seconds, the semantic model becomes available in Power BI Service, ready for report creation and analysis.

Benefits

1. No manual connection or credential management
   
   
2. Automatic synchronization of table changes and relationships
   
   
3. Single source of truth (entity relationships defined in Unity Catalog are reflected in Power BI)
   
   
4. Faster handoff between data engineering and analytics teams

B. Manual Connection via Power BI Connector:

The Azure Databricks Power BI Connector allows users to connect manually from Power BI Desktop to Databricks. This method supports Azure Active Directory (AAD) authentication and Single Sign-On (SSO) for the Power BI Service, ensuring secure and seamless access.

Steps:

1. In Power BI Desktop, navigate to Get Data → select Azure Databricks as the data source.
   
   
2. Authenticate using your Azure AD credentials.
   
   
3. Enter the Databricks compute details, including:
   
   
   • Server Hostname
     
     
   • HTTP Path (for your Databricks cluster or SQL Warehouse).
     
     

This approach is useful when you want more control over the connection or need to test connections before publishing datasets to Power BI Service.

 C. Partner Connect Integration

Another convenient way to connect Power BI to Databricks is through Partner Connect, which simplifies the setup by generating a pre-configured connection file.

Steps :

1. In Databricks, go to Marketplace → Partner Connect → View All
2. Select the Power BI tile.
3. In the Connect to Partner dialog:
   
   
   • For Compute, choose the cluster or SQL Warehouse you want to connect.
     
     
   • Download the connection file (.pbids).
     
     
4. Open the .pbids file to launch Power BI Desktop.
   
   
5. Enter your authentication details:
   
   
   • Personal Access Token (PAT) for Databricks
     
     
   • Or Microsoft Entra ID (AAD) for seamless login.
     
     
6. Click Connect and select your Databricks data in the Power BI Navigator.
   
   

Partner Connect is especially helpful for new users, as it avoids manual configuration and ensures connections are set up correctly.

Cost Optimization with Databricks SQL Warehouses

Databricks SQL offers serverless SQL Warehouses, delivering optimal performance and predictable pricing. Compared to traditional Pro or Classic warehouses, serverless warehouses provide:

• Instant and elastic compute: Eliminates long startup times and prevents over-provisioning during demand spikes.
• Minimal management overhead: Databricks manages capacity, upgrades, and performance optimization.
• Lower Total Cost of Ownership (TCO): Automatic provisioning and scaling minimize idle costs and avoid over-provisioning.

Performance

Databricks Serverless SQL Warehouses are optimized for BI workloads, supporting rapid query execution and cost-efficient scaling. The Key features includes:

• Rapid startup times: Typically between 2–6 seconds.
  
  
• Rapid scaling up/down: Ensures consistent performance while controlling costs.
  
  

Tips for Maximizing Power BI + Databricks Performance

• Choose the right Power BI storage mode (DirectQuery for near real-time, or Dual for hybrid).
  
  
• Use larger cluster sizes for big datasets to avoid query throttling.
  
  
• Favor SQL views over complex Power Query transformations and DAX formulas (to leverage Databricks’ compute).
  
  
• Limit visuals per report page and avoid unnecessarily large tables (reduce rows and columns).
  
  
• Use user-defined aggregations to pre-aggregate data and speed up queries

Scaling for Demand

Databricks SQL dynamically scales resources to handle varying workloads. Unlike static warehouses, DBSQL uses dynamic concurrency, adjusting compute power in real time to manage spikes in concurrent query loads and maximize throughput.

Each SQL Warehouse size category has a fixed compute capacity per unit, but elastic scaling ensures workloads are handled efficiently without over-provisioning.

Security and Governance

When integrating Databricks with Power BI, it’s critical to ensure that analytics remain secure, compliant, and well-governed. This is where Unity Catalog becomes a key enabler.

Unity Catalog provides a unified governance layer and a centralized data catalog that spans across all Databricks workspaces. With Unity Catalog, organizations can:

• Control access at the table, column, and row level using fine-grained permissions.
  
  
• Track lineage to understand where data originates, how it transforms, and where it’s consumed in Power BI.
  
  
• Enable auditing to monitor data access and usage for compliance.
  
  
• Monitor data quality by leveraging built-in capabilities and integrating with observability tools.
  
  
• Facilitate data discovery for analysts, making it easy to find trusted datasets.
  
  

This unified approach helps ensure that when business users access Databricks data through Power BI, they are working with curated, secure, and compliant data all while IT teams maintain centralized visibility and control.

Conclusion

Integrating Databricks with Power BI allows organizations to deliver trusted, governed, and high-performance analytics to business users, without duplicating pipelines or managing complex infrastructure.

With Unity Catalog for governance, serverless SQL Warehouses for elastic scaling, and multiple integration options (Direct Publishing, Connector, Partner Connect), this architecture provides:

• Seamless collaboration between data engineering and analytics teams
  
  
• Lower operational costs
  
  
• Faster, scalable BI insights
  
  

Whether you’re modernizing your analytics stack or scaling self-service BI, Databricks and Power BI together provide a future-proof, enterprise-grade solution.

[Parthiban_R211]

Insightful!

[TharunChalla]

Insightful Abhilash 

[pankajnamekar25]

Brilliant summary, Abhilash!

Love how you broke down the integration options with clear steps and real-world benefits. 

[polymathy2017]

Fantastic! Thank you.

[mahanandareddy]

Insightful Abhilash 😊

[Kavya_2001]

Very Informative!! Thanks Abhilash

[M_S_MANOHAR]

Great Article and Useful content.

[Balaraju]

@Abhilash_P , very useful information for me, as I am using databricks as my datasoure. thanks

[Hemanth_Elluri2]

Good one @Abhilash_P ,
Provides detailes understading about connecting Power BI with Databaricks

[UdhayakumarT]

Very Informative

[sazidthe1]

@Abhilash_P, Thank you for the detailed demonstration. This is very helpful!