Open Source Database

Choose based on the main access pattern. If you need joins, constraints, transactions, and reporting, a relational database is usually the safer starting point. If each record is mostly read and written as a whole, a document database can reduce mapping code. For counters, events, search-like access, graphs, or time-series workloads, specialized engines can help, but they narrow your future query options.

Often yes, but the license still matters. Permissive licenses usually allow commercial use with notice requirements. Copyleft licenses may add obligations when you distribute modified software. Network-focused licenses can affect hosted database services. Review the actual license text, including client libraries and extensions, before embedding or reselling anything. The database engine and surrounding tooling may not all use the same license.

Self-hosting gives you control over configuration, upgrade timing, network placement, and unusual extensions, but it also makes backups, monitoring, failover, and security patching your responsibility. A managed service reduces operational load and is often the better choice for small teams, but it can limit version choices, superuser access, file system access, and extension support. Test the managed service behavior, not just the upstream database behavior.

The hard parts are usually schema translation, data type differences, stored procedures, triggers, indexes, and application queries. Flat table data can often move through dumps, CSV files, or replication tools, but behavior does not always transfer cleanly. Plan for a validation pass that compares row counts, constraints, null handling, timestamps, sorting, and transaction behavior. A dual-write or read-replica phase can reduce cutover risk.

Look for role-based access control, transport encryption, encryption at rest options, audit logging, secure defaults, and clear privilege boundaries between administration and application access. For multi-tenant applications, row-level or collection-level controls may matter. Also check how secrets are handled, how authentication integrates with your identity system, and whether backups are encrypted. Security is as much about operational practice as engine capability.

Do not accept backup support on paper. Restore a real backup into a clean environment and measure how long it takes. Check point-in-time recovery, snapshot consistency, replica lag, failover behavior, and whether backups include users, permissions, extensions, and configuration. Replication protects availability, not accidental deletes or bad migrations. Your recovery point objective and recovery time objective should be tested before production data lands there.

Not automatically. Lock-in can come from SQL dialects, custom data types, stored procedures, query extensions, indexing features, and operational tooling. You can reduce it by keeping business logic in application code when practical, documenting non-portable features, using standard export formats, and testing restore into a separate environment. Avoiding lock-in is a design habit, not a property granted by the license.

Your risk depends on how portable the data and workload are. A mature on-disk format, standard dump tools, common wire protocol, and broad driver support make a transition easier. Heavy use of niche extensions or unusual query behavior makes it harder. Before adoption, check release history, governance, contributor diversity, and whether there are credible forks or compatible services. Keep tested exports so you are not negotiating with failure later.