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Version: 0.0.37

Operations

The Operations Tab within Custom Models focuses on managing the semantic layer, a critical component that organizes data interaction and processing. This sophisticated process is managed through runtime operations, ensuring seamless integration and adaptation of custom models to their corresponding data sources.

Semantic Layer Operations​

To accurately depict the semantic layer, users are provided with three key operations: Create Semantic Model, Update Semantic Model, and Delete Semantic Model.

Create Semantic Model​

  • Activation Process: Initiates the semantic layer, establishing job definitions in the Operations context for the model. Given the complexity and breadth of data sources involved, this operation might take from several minutes to hours.
  • Purpose: This operation is essential for building a structured framework that enables efficient data analysis and interpretation.

Update Semantic Model​

  • Regular Scheduling: It is advisable to plan updates frequently, especially when there is a change in the data source structure. This ensures the semantic model remains relevant and accurately reflects data characteristics.
  • Use-Case Dependency: The necessity for updates may vary significantly depending on the specific use case and data dynamics involved.

Delete Semantic Model​

  • Caution Required: Removing the semantic layer should be considered carefully, as it leads to voided charts and dashboards dependent on the model. This action is recommended only in case of emergencies or during the initial modeling phase.
  • Impact: Ideally, deletion is part of controlled adjustments or transitional states within the model lifecycle.

Best Practices​

  • Scheduled Updates: Regularly evaluate the state of data sources and adjust the semantic model accordingly to maintain analytical accuracy.
  • Emergency Protocols: Have contingency plans for managing the deletion of semantic models to minimize disruption.
  • Complexity Management: Recognize the complexity inherent in data source integration and allocate sufficient resources for semantic model creation.

Conclusion​

Through these operations, users can maintain a robust semantic layer within Custom Models, optimizing data source interactions and analytical efficiency. Balancing creation, updating, and deletion strategies will ensure the semantic layer remains a valuable asset in the Lakehousecat framework.