Migrating a MySQL database to PostgreSQL in an enterprise environment requires three things no homegrown ETL delivers: rigorous data type mapping, row-by-row validation pre and post cutover, and a rollback plan executable in under fifteen minutes. The difference between a clean migration and one that breaks production is those first two weeks of discovery — not the tools. Here's the plan proven on real projects with millions of rows.
Why migrate to PostgreSQL in 2026?
The five reasons we see consistently across enterprise clients in LATAM.
- Native JSONB: business logic with semi-structured data without losing relational integrity
- Materialized views with concurrent refresh — something MySQL handles poorly
- Native Row Level Security, essential for multi-tenant LATAM deployments
- Powerful extensions: PostGIS for geo, pg_trgm for fuzzy search, pgvector for AI
- Open source license with no enterprise gotchas like Oracle MySQL's
If your company grew on MySQL Community and now handles geospatial data, AI workloads, or multiple tenants, Postgres is the natural next step.
Type mapping: the phase that breaks the most migrations
| MySQL Type | PostgreSQL Type | Gotcha |
|---|---|---|
| TINYINT(1) | BOOLEAN | Zero to false, non-zero to true |
| DATETIME | TIMESTAMP or TIMESTAMPTZ | Decide timezone explicitly |
| AUTO_INCREMENT | SERIAL or IDENTITY | Reset sequence after load |
| TEXT, MEDIUMTEXT, LONGTEXT | TEXT (just one) | Postgres doesn't differentiate |
| ENUM | CREATE TYPE custom enum | Stricter, validates more |
| DOUBLE | DOUBLE PRECISION | Nearly identical, no gotcha |
| GROUP_CONCAT | string_agg | Different syntax |
| ON UPDATE CURRENT_TIMESTAMP | Explicit trigger | Postgres has no shortcut |
Every one of these types has a scenario where silent conversion breaks business logic. The boolean is the most painful: a TINYINT(1) with value 2 in MySQL is truthy; after migration it may become true or null depending on the tool. Row-by-row validation catches it; aggregate auditing does not.
The serious migration workflow in seven phases
- Full inventory: tables, views, triggers, stored procedures, foreign keys
- Type and constraint mapping, table by table, with unit tests
- Set up the destination Postgres instance with an identical schema before loading data
- Initial load with pgloader or custom ETL, master tables before detail tables
- Row-by-row validation: row count, checksum aggregates, 1,000-row sample per table
- Logical replication for continuous sync during the cutover window
- Application cutover with a feature flag, active monitoring, documented rollback plan
Real case: 98M-row snapshot in 48 hours
We worked with a multinational distributor in Guatemala with SQL Server as the source (not MySQL, but the principle applies identically). Full snapshot with no destination schema: 239 countries, 446 offices, 2.7 million customers and contacts, 98 million rows of historical data.
- Python snapshot worker with pymssql + PyArrow + Parquet
- 8-parallel chunking, 50,000-row batches, 10-queries-per-second throttle
- 197 tables snapshotted
- 2.7 GB of Bronze Parquet in Supabase Storage
- 2,528 files in bucket
- Triple verification: source = Bronze = Silver
- Zero orphan FKs in destination population
- 204 real operative offices identified
The opposite of a nightly mysqldump and restoring on top of Postgres and hoping nothing breaks.
The four cutover strategies
Depending on volume and downtime tolerance, one of these four fits your scenario.
| Strategy | Downtime | Suitable Volume |
|---|---|---|
| Dump and restore | Hours to one day | Up to 100GB, weekend window |
| Batch ETL with freeze | 2 to 8 hours | 100GB to 1TB, overnight window |
| Logical replication with switchover | Minutes | 1TB and above |
| Parallel dual-write deployment | Zero | Critical for banking, healthcare, 24/7 retail |
Parallel deployment is the gold standard but requires building an application layer that temporarily writes to both databases. Worth it for mission-critical operations.
The seven mistakes that break production
- Not backing up before cutover: rollback becomes impossible if something fails
- Skipping row-by-row validation: you discover discrepancies weeks later
- Changing collation without testing: PostgreSQL handles Unicode differently and sort order changes
- Ignoring timezones: TIMESTAMP without TZ loses an hour twice a year
- Foreign keys loaded out of order: integrity violations and mass rollback
- No rollback plan: if the app goes down, you have thirty minutes before it escalates to the CEO
- Zero post-cutover monitoring: silent bugs surface when a customer complains
Build it in-house or bring in outside help?
For volumes under 50GB with a simple schema, an internal DBA with two dedicated weeks can execute this well. For larger volumes, integrations with five or more systems, or a zero-downtime requirement, the external investment pays for itself.
What used to take thirty engineers and eighteen months, we deliver in twelve weeks with MAGIA / Forge methodology and rigorous architecture. No retainers, no tied licenses, code in your name.
Real costs by volume
| Volume | Internal cost (2 DBAs, 6 weeks) | MAGIA / Forge cost |
|---|---|---|
| Up to 50GB, simple schema | $12,000 | $8,000 |
| 50–500GB, 5 systems | $60,000 | $20,000 flat |
| Over 1TB, mission critical | $150,000 | From $20,000 with scoped engagement |
Beyond price, the key difference is operational documentation. A Catalizadora migration delivers a runbook, rollback plan, and KPIs baseline. Zero post-delivery retainer.
Next steps
If your MySQL is no longer holding up under volume, or you need modern extensions like JSONB, multi-tenant RLS, or pgvector for AI, a free technical session saves you months of costly mistakes. MAGIA / Forge delivers custom software in twelve weeks with active CI/CD and automated testing. MAGIA / Core covers scenarios where you also need a unified data lake and dashboards.
A technical call with the team that actually builds — not with an SDR. No pitch deck.