Supermetrics can deliver your data to Azure Storage or Azure SQL. Microsoft Fabric can then connect to these sources, making your data available for analytics, dashboards, and AI without waiting for a direct Supermetrics-to-Fabric connector. Microsoft and Supermetrics recommend this method.
Instructions
Step 1: Deliver data from Supermetrics to Azure Storage or Azure SQL
First, you will need your data available in either Azure Storage or an Azure SQL database to connect with Microsoft fabric. You will find our instructions on how to configure these as a Supermetrics destination in the following articles:
Step 2: Connect Microsoft Fabric to your data
Option 1: If your data is in Azure Storage, use Fabric Shortcuts for fast access
Open Microsoft Fabric and go to your workspace.
Create a new Lakehouse or Dataflow Gen2.
In the Lakehouse, select Add shortcut.
Choose either Azure Data Lake Storage Gen2 or Azure Blob Storage.
Enter your storage account details and authenticate.
Select the container or folder with your Supermetrics data.
The shortcut appears in your Lakehouse.
For more details, see Microsoft’s documentation on creating a shortcut to Azure Data Lake Storage Gen2 in OneLake.
Alternative: Use Data Factory for ETL
If you need to transform or copy data in Fabric’s OneLake for performance or historical snapshots, use Fabric’s built-in Data Factory.
Option 2: If your data is in Azure SQL, use Fabric Mirroring for real-time sync
Open Microsoft Fabric and go to your workspace.
Select New → Mirror.
Choose Azure SQL Database as your source.
Enter your Azure SQL connection details.
Authenticate and select the tables to mirror.
Configure mirroring options, either full or incremental.
Start mirroring.
For more details, see Microsoft’s documentation on Mirroring Azure SQL Database.
Step 3: Analyze and visualise in Microsoft Fabric
When you have Microsoft Fabric connected to your data in either Azure Storage or Azure SQL Database, you can use Power BI, Notebooks, or Dataflows in Fabric to analyze and visualize your Supermetrics data.
Both Lakehouse and Warehouse options support SQL queries and Power BI integration. Microsoft’s documentation can help you choose which option is best for you.
Best practices
Using Shortcuts is faster for access and freshness, but may be slower for heavy analytics. We recommend using Data Factory or mirroring for large and complex workloads for better performance.
For better performance, use fewer, larger files (for example, daily Parquet files) are preferred over thousands of smaller files.
Partition your data by date, customer for efficient querying.
Set up alerts for failed transfers or refreshes.
Separate your data by team or use case for security and manageability.
Ensure both Supermetrics and Fabric have the right access to your Azure resources.