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+# Lakehouse: Security \& Governance
+
+Costa Rica
+
+[](https://github.com/)
+[brown9804](https://github.com/brown9804)
+
+Last updated: 2025-05-08
+
+------------------------------------------
+
+
+List of References (Click to expand)
+
+- [Workspace roles in Lakehouse](https://learn.microsoft.com/en-us/fabric/data-engineering/workspace-roles-lakehouse)
+- [How lakehouse sharing works](https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-sharing)
+
+
+
+
+Table of Contents (Click to expand)
+
+- [Read all SQL endpoint data](#read-all-sql-endpoint-data)
+- [Lakehouse Semantic Model](#lakehouse-semantic-model)
+- [SQL Analytics Endpoint](#sql-analytics-endpoint)
+
+
+
+> `Lakehouse`is a `specific type of data architecture within Microsoft Fabric`that combines the features of data lakes and data warehouses. `It allows for the storage and processing of both structured and unstructured data`, providing the flexibility of a data lake with the performance and management features of a data warehouse.
+
+
+
+
- `Data Factory Pipelines`: Reading from or writing to SQL endpoints as part of ETL/ELT processes.
- `OneLake / Gen2 Data Lake`: SQL endpoints can expose structured views over data stored in the lake.
- `Data Activator / Agents`: Agents may use SQL endpoints to monitor or trigger actions based on data changes.
- `Excel / Office Integration`: Connecting Excel to SQL endpoints for live data refresh and pivot analysis.
- `Third-party BI Tools`: Using Tableau, Qlik, etc., to connect to SQL endpoints.
- `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.
- `Machine Learning`: Training models using Spark MLlib or integrating with Azure ML.
- `Data Science Workloads`: Performing large-scale data analysis or feature engineering.
- `Copilot & Agents`: If they need to interact with Spark jobs or listen to Spark events (e.g., job completion).
- `Streaming Analytics`: Real-time data processing using Spark Structured Streaming.
- `Data Engineering Pipelines`: Complex transformations and joins across large datasets.
- `Event-Driven Automation`: Triggering workflows or alerts based on Spark job events.
- `Integration with Delta Lake`: Managing transactional data lakes with ACID guarantees. |
+
+
+
+## Read all SQL endpoint data
+
+> Permissions:
+>
+> - Read
+> - Read All
+> - Subscribe OneLake Events
+
+> Lakehouse Manage Permissions:
+
+
|
+
+
+
+> When `Read all SQL endpoint data`:
+
+
+
+> `Read` access is granted, you add more permissions.
+
+
+
+## Lakehouse Semantic Model
+
+> Permissions:
+>
+> - Reshare
+> - Build
+> - Write
+
+
+
+
+
+## SQL Analytics Endpoint
+
+> Permissions:
+>
+> - Read
+> - Read Data
+> - Read All
+
+
+
+
+
+
+
Total Visitors
+
+
-
-
-
-
- `Data Factory Pipelines`: Reading from or writing to SQL endpoints as part of ETL/ELT processes.
- `OneLake / Gen2 Data Lake`: SQL endpoints can expose structured views over data stored in the lake.
- `Data Activator / Agents`: Agents may use SQL endpoints to monitor or trigger actions based on data changes.
- `Excel / Office Integration`: Connecting Excel to SQL endpoints for live data refresh and pivot analysis.
- `Third-party BI Tools`: Using Tableau, Qlik, etc., to connect to SQL endpoints.
- `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.
- `Machine Learning`: Training models using Spark MLlib or integrating with Azure ML.
- `Data Science Workloads`: Performing large-scale data analysis or feature engineering.
- `Copilot & Agents`: If they need to interact with Spark jobs or listen to Spark events (e.g., job completion).
- `Streaming Analytics`: Real-time data processing using Spark Structured Streaming.
- `Data Engineering Pipelines`: Complex transformations and joins across large datasets.
- `Event-Driven Automation`: Triggering workflows or alerts based on Spark job events.
- `Integration with Delta Lake`: Managing transactional data lakes with ACID guarantees. |
-
-https://github.com/user-attachments/assets/2974bdee-4b02-4750-ba6c-b745215e0f82
-
+- [Lakehouse Permissions](./LakehousePermissions.md): Lakehouse, Semantic Model, SQL Endpoint
Total Visitors
-