Close: the deployment path exists, but specific checks are needed first, typically raising tests, auditing serialized data, or respecting a shared schema with expand and contract. Test coverage is thin; ZDD shifts the safety net onto automation.

• Serialized state or messages in flight (Redis or queues): new code must deserialize objects written by the previous version

1. Adopt the four-stage discipline from the ZDD engineering standard: pre-deployment compatibility, deployment, feature activation behind a flag, post-release cleanup.
2. Raise automated test coverage first: ZDD trades the end-of-sprint manual gate for continuous automated confidence, so the tests are the entry ticket.
3. Make every change dual-version safe: backwards-compatible APIs (new major version for breaking wire changes), expand and contract for any schema change, and consumers tolerant of unknown fields.
4. Audit serialized data: anything in Redis, queues or shared caches must deserialize cleanly across adjacent versions.
5. Wrap behaviour changes in feature flags so code deploys dark and release becomes a business decision, not a deployment event.
6. Route deployments through the Workflow Engine's automated drain, deploy, health-check, return cycle (the path proven by the first automated production run in mid-2026).
7. Verify with area-scoped regression rather than the whole-platform cycle, and watch error rates after rollout with automated rollback on alert.

The database is solely owned by this system, so the migration is self-contained. Recommended target: PostgreSQL.

1. Inventory the schema and query surface of sqlserver:BookingCommissionRate: tables, stored procedures, triggers, cross-database joins and anything else engine-specific.
2. Confirm the target: PostgreSQL (relational model; keep transactions and joins, swap the engine).
3. Data access is not through an ORM, so port the SQL by hand: translate T-SQL constructs, replace stored procedures with application code or target-native equivalents, and add integration tests around the queries that matter first.
4. Stand up the target alongside SQL Server, backfill with an ETL pass (pgloader or an export/import job), then dual-write or replicate until parity is proven.
5. Move reads to the target, watch correctness and latency, then cut writes over and keep SQL Server read-only until confidence is earned.
6. Decommission the SQL Server database and remove the connection strings and firewall paths that pointed at it.

Modern runtime, no hosted legacy transport: this service can follow the estate's proven container path (Dockerfile, shared pipeline template, ECS via Terraform).

1. Add a Dockerfile and make the app read all configuration from environment variables and the secrets manager, never from machine-local files.
2. Log to stdout and expose a health check endpoint so ECS can supervise it.
3. Adopt the shared ci.yml pipeline template with the container parameters (awsRegion, containerAfterBuild, ecsTerraformRepo), which is exactly how the estate's existing AWS services deploy.
4. Define the service in the Terraform repo, deploy to a non-production environment, then shift traffic and disarm the deployment-group release pipeline.