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Author: brown9804

Security/LakehousePermissions.md

@@ -36,7 +36,7 @@ Last updated: 2025-05-08
 | **Permission**                                 | **Definition**                                                                 | **Use Cases**                                                                                                                                                                                                 |
 |-----------------------------------------------|---------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | Read all SQL endpoint data                    | This permission allows access to SQL-based data endpoints in Microsoft Fabric. | - `Power BI`: Connecting to semantic models or datasets using DirectQuery or Import mode.<br/>- `Data Factory Pipelines`: Reading from or writing to SQL endpoints as part of ETL/ELT processes.<br/>- `OneLake / Gen2 Data Lake`: SQL endpoints can expose structured views over data stored in the lake.<br/>- `Data Activator / Agents`: Agents may use SQL endpoints to monitor or trigger actions based on data changes.<br/>- `Excel / Office Integration`: Connecting Excel to SQL endpoints for live data refresh and pivot analysis.<br/>- `Third-party BI Tools`: Using Tableau, Qlik, etc., to connect to SQL endpoints.<br/>- `Custom Applications`: Internal apps querying SQL endpoints for real-time dashboards. |
-| Read all Apache Spark and subscribe to events | This permission relates to Apache Spark workloads, which are more code- and compute-intensive. | - `Notebooks`: Running PySpark, Scala, or SparkSQL code for data exploration and transformation.<br/>- `Machine Learning`: Training models using Spark MLlib or integrating with Azure ML.<br/>- `Data Science Workloads`: Performing large-scale data analysis or feature engineering.<br/>- `Copilot & Agents`: If they need to interact with Spark jobs or listen to Spark events (e.g., job completion).<br/>- `Streaming Analytics`: Real-time data processing using Spark Structured Streaming.<br/>- `Data Engineering Pipelines`: Complex transformations and joins across large datasets.<br/>- `Event-Driven Automation`: Triggering workflows or alerts based on Spark job events.<br/>- `Integration with Delta Lake`: Managing transactional data lakes with ACID guarantees. |
+| Read all Apache Spark and subscribe to events | This permission relates to Apache Spark workloads, which are more code and compute intensive. | - `Notebooks`: Running PySpark, Scala, or SparkSQL code for data exploration and transformation.<br/>- `Machine Learning`: Training models using Spark MLlib or integrating with Azure ML.<br/>- `Data Science Workloads`: Performing large-scale data analysis or feature engineering.<br/>- `Copilot & Agents`: If they need to interact with Spark jobs or listen to Spark events (e.g., job completion).<br/>- `Streaming Analytics`: Real-time data processing using Spark Structured Streaming.<br/>- `Data Engineering Pipelines`: Complex transformations and joins across large datasets.<br/>- `Event-Driven Automation`: Triggering workflows or alerts based on Spark job events.<br/>- `Integration with Delta Lake`: Managing transactional data lakes with ACID guarantees. |
 
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