dbmigrateModel the database.
Or rehearse the change.
MySQL Workbench brings database modeling and synchronization together. Compare that workflow with dbmigrate’s focused live schema review, shadow dry runs, recovery SQL, and masked data refresh.
$10.95 monthly or $99 yearly One developer, up to two devices
MySQL / distinguish modeling from change rehearsal.
Choose the MySQL workflow you need.
Built for modern engineering teams running MySQL 8.0 and 8.4 LTS in production.
Solving MySQL’s Implicit Commit Risk
MySQL can implicitly commit DDL regardless of the client used. A multi-statement script can leave earlier changes applied after a later failure. Rehearse the plan, inspect the generated recovery SQL, and keep an appropriate backup for data recovery.
Shadow dry run verification + Pre-generated rollback SQLDesktop Review on macOS & Windows
dbmigrate offers macOS Apple Silicon, macOS Intel, and Windows x64 builds. Compare the review flow and supported platform requirements with your existing tools; this page does not claim a measured speed or stability advantage.
macOS Apple Silicon + Intel / Windows x64Referential Data Copying & PII Masking
dbmigrate combines selected row copying with parent/child relationship handling and reviewed in-memory masking. Use these controls to build a useful test dataset, then assess the result for remaining sensitive values.
Copy orders + customer rows with in-memory deterministic maskingAutomated CI/CD with dbmt CLI
dbmt makes schema comparison and plan generation repeatable from a terminal. Build an expected-state database with your migration tooling, compare the selected target, and configure your CI decision from the reviewed results.
dbmt compare --profile prod-sync · Exit code 0 on parityCompare dbmigrate and MySQL Workbench.
Detailed comparison of migration safety, environment promotion, and developer experience.
| Capability | dbmigrate | MySQL Workbench |
|---|---|---|
| Architecture & UX | Native desktop app (macOS & Windows) + headless CLI | Desktop C++ app |
| Database Engine Support | MySQL (8.0 & 8.4 LTS) & PostgreSQL (13–17) | MySQL only |
| DDL Safety & Dry Run | Shadow dry run on temporary target + pre-generated rollback scripts | Review generated synchronization script only |
| Collation & Generated Columns | Full support for utf8mb4_0900_ai_ci, stored & virtual generated columns | Model and synchronization settings; verify handling for your MySQL version |
| Referential Data Copying | Included: WHERE filters, PK picks, parent/child relation cascades | Basic export wizard (no referential relations) |
| In-Memory PII Masking | Reusable rule dictionary: Faker, salted hashes, regex | None |
| CI/CD Headless Automation | dbmt CLI with deterministic exit codes (0, 1, 2, 3) | None |
| Credential Security | AES-256-GCM encrypted local vault with master password | OS Keychain / plaintext workbench configuration files |
MySQL Workbench comparison details.
How do MySQL implicit commits affect schema synchronization?
MySQL DDL such as ALTER TABLE can implicitly commit. If a later statement fails, earlier successful schema changes remain applied. dbmigrate uses shadow dry runs to help identify plan errors before a live run and generates recovery SQL for review; a rehearsal cannot guarantee that a live deployment will succeed.
What does MySQL Workbench synchronization support?
Workbench compares models, live databases, and scripts, lets you choose synchronization direction and object updates, and can generate an ALTER script instead of immediately executing changes. These modeling features may be valuable alongside a focused environment synchronization tool.
Which platforms and database versions does dbmigrate support?
The MySQL edition supports MySQL 8.0 and 8.4 LTS on macOS Apple Silicon, macOS Intel, and Windows x64. The PostgreSQL edition is a separate product. Source and target must use the same engine.
Can dbmigrate refresh a test database with masked rows?
Yes. Select tables or rows with filters, include related parent and child records, and review masking rules before copying. Sensitive values are transformed locally before they are written to the target. Validate the resulting dataset against your privacy and testing requirements.