Database Migration
The process of transforming a database schema or moving data between databases in a controlled, versioned manner. Migration tools track which changes have been applied, enabling reproducible database evolution across development, staging, and production environments.
Database migrations version-control schema changes alongside application code. Each migration is a script that applies a forward change and optionally defines a rollback. Migration tools like Flyway, Alembic, Prisma Migrate, and Knex track which migrations have been applied to each environment, ensuring consistency. Migrations should be backward-compatible to support zero-downtime deployments where old and new application versions coexist briefly.
For AI product teams, database migrations must accommodate the evolving data needs of AI features: adding columns for new feature values, creating tables for experiment tracking, altering indexes for new query patterns, and expanding schemas for model metadata. Growth teams generate migration needs when adding experiment tracking tables, usage analytics columns, or cohort definition schemas. The key discipline is making migrations reversible and backward-compatible: adding a new column with a default value is safe, but renaming or removing a column that existing code references causes outages. Large data migrations should run as background jobs rather than blocking the migration process, especially for tables that feed AI feature pipelines.
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Platform as a Service
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Infrastructure as a Service
A cloud computing model that provides virtualized computing resources over the internet. IaaS offerings like AWS EC2, Google Compute Engine, and Azure Virtual Machines give teams full control over servers, storage, and networking without owning physical hardware.