Automate the repetition. Keep the decisions visible.

Use automation to make source metadata and target code easier to work with, while keeping engineering review connected to the original job.

Put automation around repeatable work

Import job designs, browse stages and dependencies, and generate a target preview. Consistent access to those artifacts gives engineers a common starting point.

Track exceptions explicitly

Review custom routines, unsupported stages and environment-specific behaviour. Keep the unresolved work in the plan rather than treating a generated file as the end of the migration.

Accept with evidence

Configure and execute the target in your environment, compare results and record the reviewer’s decision. Code generation and execution validation are different milestones.

Extend automation through delivery

Import diagnostics make capture gaps and target-specific blockers visible before conversion. Project waves, assignments and current review evidence connect that analysis to delivery. For Fabric, prepared packages can continue into repository review and configured checks.

Explore import diagnostics and Fabric releases and CI/CD to see where each automated step fits.

Work through conversion questions with the AI Migration Assistant

Open a job conversation to discuss imported transformation rules, target implementation choices and saved code excerpts. Ask which source behaviors need closer review and which test cases could expose a difference.

Explore what the AI Migration Assistant can read and how it supports migration.

Continue from here

See the workflowMigration guide