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Key insights

• OneLake Integration allows Power BI Semantic Model data to be automatically written to Delta tables in OneLake, promoting seamless data access and higher performance across various tools and platforms.


• Automatic Export of data to Delta Tables ensures that import-mode table data is readily available for other workloads within Microsoft Fabric. This feature supports a wide range of tools including T-SQL, Python, Scala, PySpark, Spark SQL, and R.


• The integration supports only specific SKUs: Power BI Premium P and Microsoft Fabric F SKUs. It requires the semantic model to be in import mode with large semantic model storage format enabled.


• To enable OneLake Integration: Navigate to your semantic model settings, expand the OneLake integration section, toggle it "On," and apply changes. Admins can control this through the Power BI admin portal settings.


• Considerations and Limitations: During preview, certain features may have limitations such as precision loss in currency data types with more than 18 decimal points. Only one version of Delta tables is stored; old versions are deleted after export.


• Shortcuts can be created for Lakehouse tables from exported Delta tables to provide quick access from other workloads within Fabric. This enhances collaboration across different teams and projects.

Introduction to OneLake Integration for Power BI Semantic Models

Power BI users now have the opportunity to integrate their semantic model data with Microsoft Fabric's OneLake, as showcased in a recent video by "Guy in a Cube." This integration allows data imported into model tables to be automatically written to Delta tables in OneLake. Consequently, this setup facilitates seamless data access across various tools and platforms, enabling data professionals to utilize the same data that drives Power BI reports.

Key Features of OneLake Integration

The integration of OneLake with Power BI semantic models offers several notable features:

• Automatic Export to Delta Tables: When enabled, OneLake integration exports data from import-mode tables in your semantic model to Delta tables in OneLake. This ensures that data is readily available for other workloads within Microsoft Fabric.
• Broad Data Accessibility: The exported Delta tables can be accessed using various tools and languages, including Python, T-SQL, Scala, PySpark, Spark SQL, and R. This flexibility allows data scientists, analysts, and developers to work with the data using their preferred tools.
• Integration with Lakehouse: By creating shortcuts to the exported Delta tables in Lakehouse, users can provide quick and easy access to them from other workloads in Fabric. This setup promotes efficient data sharing and collaboration across different teams and projects.

Prerequisites and Enabling OneLake Integration

Before enabling OneLake integration, there are specific prerequisites and steps to follow:

• Supported SKUs: OneLake integration is supported on Power BI Premium P and Microsoft Fabric F SKUs. It is not available for Power BI Pro, Premium Per User, or Power BI Embedded A/EM SKUs.
• Model Requirements: Your semantic model must be in import mode and have the large semantic model storage format enabled.
• Enabling Integration: In your semantic model settings, expand the OneLake integration section, toggle the slider to "On," and select "Apply." Additionally, global and tenant admins can control OneLake integration using settings in the Power BI admin portal.

Considerations and Limitations

While OneLake integration offers numerous benefits, there are also considerations and limitations to keep in mind:

• Preview Phase Limitations: During the preview phase, certain features and scenarios may have limitations. For instance, currency data types with values larger than 18 decimal points may experience precision loss when exported to Delta files. Additionally, semantic models in BYOK-enabled workspaces are not supported during the preview.
• Data Versioning: Only a single version of the Delta tables is exported and stored in OneLake. Old versions are deleted after a successful export, which may cause transient failures in other execution engines using the older data.

Challenges and Tradeoffs

Implementing OneLake integration involves balancing different factors and addressing challenges:

• Data Accessibility vs. Security: While broad data accessibility is a significant advantage, ensuring data security and compliance is crucial. Organizations must carefully manage permissions and access controls to protect sensitive data.
• Performance vs. Complexity: The integration enhances performance by enabling seamless data access. However, it also introduces complexity in managing data exports and ensuring compatibility with existing systems and workflows.
• Cost Considerations: Although the operation of exporting the model to OneLake is not billed during the preview, compute and storage usage of the exported model on OneLake is billed. Organizations need to evaluate these costs against the benefits of integration.

Conclusion and Future Outlook

In conclusion, the integration of OneLake with Power BI semantic models presents a valuable opportunity for organizations to enhance data accessibility and performance. While there are challenges and tradeoffs involved, the potential benefits make it a worthwhile consideration for data professionals. As the integration continues to evolve, staying informed about updates and best practices will be essential for maximizing its value.

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