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Database migration

A database migration is the process of changing a database from one state to another—such as moving to a new schema, upgrading database software, or transferring data between systems—while keeping the application working correctly. It often involves creating, modifying, or removing tables, columns, indexes, constraints

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  1. What “database migration” means

    A database migration is the process of changing a database from one state to another—such as moving to a new schema, upgrading database software, or transferring data between systems—while keeping the application working correctly. It often involves creating, modifying, or removing tables, columns, indexes, constraints, and relationships, and then updating existing data to match the new structure.

  2. Common types of migrations

    Typical migrations include: (1) schema migrations (e.g., adding columns, changing data types, renaming fields), (2) data migrations (e.g., transforming or backfilling data to new formats), (3) environment migrations (e.g., moving from staging to production or between cloud providers), and (4) versioned migrations managed through tools or scripts so changes are repeatable and auditable.

  3. How migrations are usually handled safely

    Teams commonly use version control for migration scripts, run migrations in a test environment first, and apply them in a controlled order. Practices may include backups, transaction support where possible, feature flags or backward-compatible changes, and rollback plans. Monitoring after deployment helps confirm that queries, performance, and application behavior remain stable.

FAQ

What is a migration script?

A migration script is code (often SQL or a framework-specific format) that applies a specific change to the database, typically tracked by version.

Is a migration the same as a database backup?

No. A backup saves data for recovery, while a migration changes the database structure and/or data to a new version.

How do teams reduce downtime during migrations?

They may use backward-compatible schema changes, phased rollouts, background data backfills, and careful scheduling with monitoring.

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