Technically eligible one day, but the blockers are structural: hosted WCF or SOAP contracts that cannot run two versions at once, or heavy legacy coupling. Sequence the modernisation work first. Legacy web front end: session state and framework age make mixed-version fleets risky until state is externalised.

• sqlserver:Phenix schema convoy
• sqlserver:EventLog schema convoy

• 1 system(s) depend on this repo; during a rolling deploy they will talk to old and new versions at once, so responses must stay compatible
• Consumes WCF contract packages: never take a breaking client bump mid-feature, and treat obsolete warnings as migration debt
• Shares sqlserver:Phenix with 21 system(s): destructive schema change is prohibited in a single release; expand and contract only
• Shares sqlserver:EventLog with 13 system(s): destructive schema change is prohibited in a single release; expand and contract only

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. Externalise any remaining session state so users can move between nodes mid-session without disruption.
6. Wrap behaviour changes in feature flags so code deploys dark and release becomes a business decision, not a deployment event.
7. 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).
8. Verify with area-scoped regression rather than the whole-platform cycle, and watch error rates after rollout with automated rollback on alert.

sqlserver:Phenix, sqlserver:EventLog, sqlserver:Repository are shared, so this system cannot change engine on its own: every sharer moves in convoy. The stage 4 work (named owner, declarative schema, owned API) has to land first; after that, the steps below apply to whatever slice this system ends up owning.

• sqlserver:Phenix is shared with 21 other system(s) and is a core database
• sqlserver:EventLog is shared with 13 other system(s) and is a core database
• sqlserver:Repository is shared with 1 other system(s)

1. Inventory the schema and query surface of sqlserver:Phenix, sqlserver:EventLog, sqlserver:Repository: 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. Replatform to modern .NET first (see the recommended actions): current PostgreSQL and MongoDB drivers and EF Core providers target modern .NET, and doing one risky change at a time is cheaper than doing two.
4. 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.
5. 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.
6. Move reads to the target, watch correctness and latency, then cut writes over and keep SQL Server read-only until confidence is earned.
7. Decommission the SQL Server database and remove the connection strings and firewall paths that pointed at it.

Appropriate, but the .NET Framework runtime has to go first; container work rides along with the replatform rather than preceding it.

• .NET Framework only runs on Windows; practical (Linux) containers need modern .NET

1. Replatform to modern .NET first (see the recommended actions). Containerising then becomes routine.
2. Add a Dockerfile and make the app read all configuration from environment variables and the secrets manager, never from machine-local files.
3. Log to stdout and expose a health check endpoint so ECS can supervise it.
4. 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.
5. Define the service in the Terraform repo, deploy to a non-production environment, then shift traffic and disarm the deployment-group release pipeline.