SQL Server to PostgreSQL Migration Program Playbook
A wave-based program for migrating SQL Server databases to PostgreSQL. It covers complexity assessment, schema and T-SQL conversion with an anti-corruption layer, validated data movement, and a low-downtime cutover.
Migrating from Microsoft SQL Server to PostgreSQL is a well-trodden path driven by licensing economics and a desire for open, portable infrastructure. PostgreSQL covers the vast majority of SQL Server features, but T-SQL, identity columns, certain data types, and stored-procedure behavior differ enough that conversion needs engineering rigor. This playbook runs the migration as a wave-based program with validation gates.
As with any procedural-code migration, the T-SQL surface (stored procedures, functions, triggers) is where the effort concentrates. Scope it honestly up front.
Phase-by-Phase
Assessment. Inventory databases and consumers, assess T-SQL complexity to size the rewrite, and plan waves so simpler databases build momentum first.
Schema and T-SQL Conversion. Convert schema objects and data types, rewrite T-SQL into PL/pgSQL, and add an anti-corruption layer where applications depend on SQL Server specifics.
Data Movement and Validation. Move data with repeatable pipelines, reconcile record counts and checksums, and validate business-level data quality before cutover.
Cutover. Execute a tested cutover, tune queries against the PostgreSQL optimizer, and decommission SQL Server after a stabilization window.
Team and Roles
A migration architect owns sequencing and target design. DBAs lead schema, conversion, and performance. Data engineers run data pipelines and reconciliation. Backend engineers handle application coupling. QA owns validation and regression.
Risks and Mitigations
T-SQL rewrite effort, data consistency, performance regression, and cutover downtime are the principal risks. Mitigate with complexity scoring, automated reconciliation, load testing before go-live, and CDC-based low-downtime cutover.
Success Criteria
Target verified data parity, query latency at or below the SQL Server baseline, measurable license savings, and zero data loss.
Tooling
Use schema conversion tooling, CDC for synchronization, and PostgreSQL-native observability via Grafana. Run conversion and migration steps through CI for repeatability.