fabric-notebook-manager

You can programmatically edit Fabric notebooks by using REST API operations to add, update, or delete cells atomically. This handles base64-encoded Jupyter payloads and post-update validation internally, ensuring modifications are applied without manual API orchestration.

Automating PySpark pipeline notebooks for Medallion architecture is best handled by programmatically injecting code cells into existing pipeline notebooks. This method applies atomic operations to implement Bronze-to-Silver transformations while maintaining data engineering workflow standards.

Polling for long-running operations during Fabric notebook execution is handled internally by encapsulating the REST API execution cycle. The process triggers on-demand runs with parameters and continuously polls the operation status until completion, ensuring post-update validation confirms successful execution.

You do not need to handle base64 encoding manually when updating Fabric notebooks. The process encapsulates the complex base64 encoding and parsing of Jupyter payloads internally, allowing you to directly provide source code for atomic cell modifications without manual API orchestration.

Yes, you can trigger on-demand runs with parameters for a Fabric notebook. The REST API execution operations support creating new notebooks from scratch or starting runs with specific parameters, including built-in validation and error handling to ensure successful execution.