Legacy Modernization

Why not big-bang rewrite¶

The statistics are clear: 70% of large rewrite projects exceed budget by 2×+ or fail completely. Reasons:

• Scope creep — “Since we’re doing it again, let’s also add X, Y, Z”
• Knowledge loss — Nobody knows why legacy does what it does. Edge cases appear only in production.
• Dual maintenance — 12-24 months maintaining two systems in parallel
• Big bang risk — D-Day: you switch and hope

The Strangler Fig pattern eliminates all these risks.

7-step modernization playbook¶

Step 1: Stabilization and measurement (2-4 weeks)

Before you start changing, you need to know where you are. We deploy monitoring, collect baseline metrics, identify bottlenecks. Output: legacy system health report with concrete numbers.

Step 2: Domain mapping (1-2 weeks)

Event Storming workshops with domain experts. We map bounded contexts, dependencies, data flows. We identify candidates for first migration — modules with fewest dependencies and highest value.

Step 3: API Gateway (2-3 weeks)

Facade in front of legacy. All requests go through gateway, routing rules decide who serves. New system behind gateway, legacy behind gateway. Switching at endpoint level.

Step 4: First module isolation (4-8 weeks)

We extract the first bounded context into a standalone service. Own database, own deployment, own monitoring. Anti-corruption layer protects the new service from legacy data model.

Step 5: Data migration (2-4 weeks per module)

CDC (Debezium) for real-time replication. Dual-write with reconciliation jobs. Consistency metrics — migration is complete when reconciliation shows 100% match for 7 days in a row.

Step 6: Traffic shifting (1-2 weeks per module)

Canary release: 5% → 25% → 50% → 100%. Automatic metrics decide on continuation. Rollback in seconds. Human intervention only for anomalies.

Step 7: Decommission (1-2 weeks per module)

Old module off. New module monitoring for 30 days after full rollout. Data archiving. Documentation. Celebration.

Data migration — the hardest part¶

Legacy systems have years of technical debt in data: - Inconsistent formats (date as string in 5 different formats) - Missing validation (NULL where it shouldn’t be, duplicates) - Implicit business logic in data (status codes nobody documented)

Our approach: 1. Analysis — Source data profiling, anomaly identification 2. Transformation rules — Mapping + cleaning + validation 3. CDC pipeline — Debezium for real-time sync 4. Reconciliation — Automatic source vs. target comparison 5. Rollback plan — For every step

Measuring success¶

Modernization without metrics is just rewriting. We measure:

• Deployment frequency — From monthly to daily
• Lead time — From weeks to hours
• MTTR — From hours to minutes
• Change failure rate — Under 5%
• TCO — Total cost of operation and development
• Developer satisfaction — Yes, we also measure team satisfaction

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