In today’s data-driven environment, organizations rely heavily on both scalable data processing platforms and powerful visualization tools. Databricks provides the computational engine for data engineering and analytics, while Power BI enables users to visualize insights interactively.
Understanding the Integration
Databricks acts as the compute engine where data is processed, cleaned, transformed, and stored in catalogs and schemas. Power BI connects to this data through a dedicated Databricks connector, allowing business analysts and stakeholders to build dashboards without needing to understand the underlying compute.
This workflow ensures that:
• Data remains unified inside Databricks
• Power BI handles interactive dashboards
• Users can build BI solutions without data duplication
• Data refresh cycles remain consistent through the gateway
Architecture Overview
The connection follows a simple but powerful architecture:
1. Databricks stores data inside catalogs, schemas, and Delta tables.
2. Power BI connects using Databricks SQL warehouse.
3. Authentication is done via Server Hostname and HTTP Path.
4. Selected tables are imported into Power BI.
5. Transformations and visuals are created on top of these imported tables.
This setup ensures high-performance BI with scalable data processing.
To understand the flow chart we have a practical demo as follows. To get started open Power BI (here we are using the desktop app).
Step 1
FIG (1)
Power BI Desktop provides multiple data connectors FIG (1). To connect to Databricks, start by clicking the ‘Get Data’ dropdown. This opens the selection panel for all supported sources.
Step 2
FIG (2)
In the search bar FIG (2), type ‘Databricks’. Power BI will display its native connector.
This connector is optimized for:
• Fast metadata retrieval
• Secure communication to Databricks
• Loading Delta tables efficiently
Step 3
FIG (3)
The Databricks connector requires two fields:
• Server Hostname
• HTTP Path
Both values are found inside Databricks under the SQL warehouse connection settings.
Once entered, Power BI establishes a secure connection to the warehouse as shown below.
FIG (4)
The above fig (4) is required values to be filled as shown in the below fig (5).
Fig (5)
Step 4
FIG (6)
After the connection is established, the Navigator window appears. This window provides a tree view of all catalogs, schemas, and tables accessible via the SQL warehouse.
You can select multiple tables by checking their boxes. The preview window on the right shows sample data for verification.
The data is successfully drawn from the databricks catalog and loaded in the Power BI and hence is ready for some insightful reporting.
Reporting:
This the required reporting processed in Power BI from the data drawn from Databricks.
Conclusion:
- Connecting Databricks to Power BI is a straightforward process involving:
- Extracting data using Get Data
- Searching for the Databricks connector
- Entering Server Hostname & HTTP Path
- Selecting and importing the required tables
- Loading them into Power BI
- Once the data is imported, you can apply transformations, filters, and business operations on the tables to generate insights.
- This integration ensures a smooth workflow for combining the power of Databricks compute with the visualization capabilities of Power BI.