Guide
Keep database docs updated by scanning schema changes, refreshing generated documentation, and preserving manual notes.
Keeping database docs up to date is not a writing problem. It is an operating model problem.
If every schema change requires someone to remember a separate documentation task, the docs will drift. The team may care about documentation, but releases, incidents, and customer work will win the calendar.
The better model is to connect documentation to scans. Taavik helps teams keep database docs up to date by generating the technical layer from metadata, recording schema change detection, and exposing it through a database documentation tool.
Most docs go stale because the update workflow is outside the database workflow.
A migration changes a table. A pull request explains the code. A deployment moves the change into production. The database is now different, but the documentation still depends on a person remembering to edit a page.
This creates a lag. At first the lag is harmless. Then a new engineer reads the doc and finds a missing column. A support person asks why a field is no longer populated. A finance export breaks because a view changed. Trust erodes quickly.
The first rule is to separate schema facts from human explanations.
Schema facts should be generated:
Human explanations should be written:
When these layers are separate, automated updates can refresh the facts without overwriting the team's prose.
Documentation is only as fresh as the latest scan.
For active systems, scan on a schedule and after meaningful schema deployments. The right cadence depends on the team. Some teams need hourly scans, others need daily scans, and some need manual scans after release windows.
The important part is consistency. A scan rhythm makes documentation freshness visible and predictable.
A central changelog is useful, but object-level history is more actionable.
If a developer is reading the orders table page, the recent changes for orders should be nearby. If a support analyst is looking at a view used by a report, recent changes to that view should not require a separate search.
Object-level change history turns documentation into an investigation tool. It explains the current shape and the recent movement in the same place.
The fear with generated documentation is that automation will overwrite useful human notes.
That should not happen. The generated layer should update metadata, while manual notes remain stable. If a column type changes, update the type. If a new index appears, add it. If the team wrote a paragraph explaining a billing status, leave it alone.
This is the difference between useful automation and a destructive export.
Manual notes become stale when they try to describe everything.
Assign ownership lightly. Ask each team to document the tables it actually understands. Keep notes short and practical. A sentence that explains when a table is written is often more useful than a long generic description.
The generated layer gives everyone the technical baseline. Manual ownership adds context only where it matters.
Keeping docs current does not mean reviewing every page every week.
Review what changed:
This keeps the review small. It also focuses attention on changes that may affect downstream code, scheduled exports, support workflows, and audits.
Use this operating rhythm:
The goal is not to create a perfect document. The goal is to make the current schema understandable and keep it that way.
Stop maintaining database docs by hand.