What problem does it solve?
Migrating Databricks notebooks, jobs, Unity Catalog governance structures, and MLflow workflows to Microsoft Fabric traditionally requires manual, error-prone rewrites of dbutils API calls, 3-level namespace references, and job configurations, with no standardized mapping guide to avoid broken code and misconfigured assets.
Core Features & Use Cases
- Exhaustive dbutils to notebookutils Mapping: Complete side-by-side API reference for replacing all Databricks dbutils calls with equivalent Microsoft Fabric notebookutils functions, covering file operations, secret retrieval, notebook orchestration, and session management.
- Namespace & Governance Translation: Step-by-step guidance for collapsing Unity Catalog 3-level namespaces to Fabric Lakehouse 2-level schemas, and mapping Databricks RBAC, row-level security, and column masking to Fabric workspace permissions and Lakehouse security settings.
- Workload Porting Patterns: Ready-to-use patterns for converting Databricks Jobs to Fabric Spark Job Definitions, Delta Live Tables to orchestrated Fabric Data Pipelines, Photon accelerator configurations to Fabric Native Execution Engine, and MLflow tracking to Fabric ML Experiments.
- Real-World Use Case: A data engineering team migrating 50+ production Databricks notebooks to Fabric can use this skill to eliminate manual API lookup, automatically convert namespace references, and reconfigure job schedules without introducing broken code or misconfigured assets.
Quick Start
Use the databricks-migration skill to port your existing Databricks notebooks, jobs, and Unity Catalog structures to Microsoft Fabric by following the provided API substitution, namespace conversion, and workload mapping guides.
