Move from IBM DataStage with a clear view of the logic you need to carry forward. Choose a destination, then plan the engineering and evidence around it.
Bring DataStage logic into a Microsoft data platform. Plan notebook execution, lakehouse tables and operational ownership together.
Explore the migration pathTranslate transformation logic for Spark. Make runtime configuration, Delta tables and workload testing part of the move.
Explore the migration pathRework DataStage processing for Snowflake. Review SQL and Snowpark logic alongside warehouse, role and data model decisions.
Explore the migration pathBring the job’s logic into the conversation.
Explain transformations, review saved code and plan validation with your selected DataStage job as context.
The project connects the people, source jobs and delivery plan. Explore how the work stays connected through migration and release.
Connect the outcome, Catalog, owners and migration waves in one project.
Scope & assignSee what the XML captured, what needs investigation and which findings affect each destination.
Jobs, sequences & dependenciesGroup related jobs, choose a destination and keep code revisions, owners and review decisions connected to the source.
Validate & acceptTake prepared Fabric packages into repository review, checks and runtime validation.
Release & hand overFollow assigned work, blockers, current acceptance evidence and operational handover.
Explain job logic, discuss migration risks and review saved code. Explore the AI Migration Assistant.
The first question is what your pipelines do today: which data they read, how they transform it and who depends on the result. Establish that context before choosing an execution pattern on the target.
Pick a workload with representative transformation logic, known outputs and an accountable owner. Use it to test translation, target operations and reconciliation. Feed what you learn into the next wave.