Leave SAS, Control-M and the mainframe behind — safely.
Structured, low-risk exits from expensive legacy platforms — SAS, Control-M, IBM mainframes — onto open, cloud-native equivalents you control.
Legacy platforms carry punishing licence costs, scarce skills, and lock-in that compounds every year. But a "big bang" rewrite is how these programs fail. The hard part is exiting without breaking the business.
If a few of these sound familiar, this is the work to start.
- 01Licence renewals arrive with double-digit increases and little leverage.
- 02A handful of people understand the platform, and they are retiring.
- 03Simple change requests take months because the platform is fragile.
- 04You pay for a fraction of the features you actually use.
What we do
SAS exit
Migrate SAS analytics and ETL to Python, Spark, and open data stacks — preserving logic and results, retiring the licence.
Control-M / scheduler exit
Replace Control-M and legacy schedulers with modern, code-defined orchestration (Airflow, Step Functions, event-driven jobs).
Mainframe to AWS
Move IBM mainframe workloads to AWS — re-platform, re-architect, or re-host COBOL/DB2/CICS with data and batch fidelity intact.
Automated code & logic conversion
Tooling-assisted translation with parallel-run validation, so migrated jobs prove parity before the old system is switched off.
Consider a bank running 400+ SAS jobs behind its regulatory reports. We inventory every job and consumer, translate them to Python and Spark, and run both stacks in parallel — reconciling outputs to the cent — until they match across a full reporting cycle. Only then is SAS switched off, and the licence spend disappears without a single missed report.
Illustrative scenario — not a specific client.
Inventory every job, dependency, and downstream consumer of the legacy platform.
Prioritize by risk and cost, then migrate in slices with automated equivalence tests.
Run old and new in parallel until output parity is proven.
Decommission the legacy platform and stop the licence clock.
Concrete artifacts, not a slide deck.
- A complete inventory of jobs, dependencies, and downstream consumers.
- Migrated workloads on open, cloud-native equivalents you control.
- Automated equivalence tests proving output parity, job for job.
- A parallel-run period with reconciled results before cut-over.
- A decommissioned legacy platform and a stopped licence clock.
Good questions.
Isn't a migration this big too risky?
The risk lives in big-bang cut-overs, which we never do. We migrate in slices and prove parity in parallel, so the business keeps running on the old system until the new one is trusted.
What if the original logic isn't documented?
That is the norm. We reconstruct behavior from the jobs themselves and validate against real outputs, so undocumented logic is captured by equivalence testing rather than guesswork.
Which platforms do you exit?
Most often SAS, Control-M and other schedulers, and IBM mainframe (COBOL/DB2/CICS) — but the parallel-run method applies to almost any legacy platform.