SAS Migration Workflow
Documented method
Problem being solved
Migrating a SAS workload requires more than translating syntax. Dependencies, missing-value behavior, dates, joins, ordering, and business rules must remain understandable and testable in the target environment.
Required inputs
- SAS programs, macros, formats, schedules, and dependency information.
- Representative input data and baseline outputs, approved for use in the execution environment.
- Target Python and Databricks requirements, including runtime and access constraints.
- Acceptance criteria, comparison tolerances, and an identified reviewer.
Workflow execution stages
- Inventory dependencies and select one bounded workload.
- Capture baseline behavior and expected outputs.
- Propose Python transformations and record assumptions.
- Run approved tests in an isolated development environment.
- Investigate differences and submit evidence for human review.
- Package the conversion, tests, and decisions for a separate deployment approval.
AI tools or agents involved
A code-capable assistant such as Claude or Codex can help explain source programs, propose transformations, and draft tests. No provider-specific agent integration or automated execution package is included here. Tool selection depends on the organization’s data policies and target environment.
Validation and human review
Compare schemas, row counts, key uniqueness, aggregate results, and representative records against the baseline. Include null values, date boundaries, and known exceptional cases. A domain reviewer resolves semantic questions; an engineer reviews code and execution evidence. Failed checks block promotion until explained or resolved.
Expected outputs
A source-to-target mapping, reviewed conversion code, reproducible checks, discrepancy records, and a review decision. These are expected artifacts of applying the method, not deliverables already produced for a specific workload.
Limitations and prerequisites
This is a documented method, not an implemented production migration service or executable workflow. SAS and target-environment expertise, appropriate access, representative data, and agreed acceptance criteria are prerequisites. AI-generated code requires review. Performance and cost depend on the workload and must be measured.
See the migration solution for scope and deliverables, or return to all workflows .