Databases
Database Seed Tenant Cleanup: Remove Demo Tenants After Onboarding Changes
Database seed script cleanup begins when demo accounts, fixture rows, migration helpers, and onboarding data keep creating states the product no longer supports.
For stale seeded demo tenants and onboarding records, cleanup should start with lineage, reader evidence, retention rules, and a tested recovery path. The useful output is a seed script cleanup pull request with consumer search, removed data, replacement fixtures, setup test, and rollback note: Replace broad seed data with narrow factories before deleting useful scenarios, protect scheduled consumers, and make the restore or recreate path visible before final removal.
Key takeaways
- Review stale seeded demo tenants and onboarding records through Seed purpose, Consumers, Data safety, not age alone.
- Use one onboarding, test, and demo refresh cycle before deciding that quiet means unused.
- Start with the reversible move: replace broad seed data with narrow factories before deleting useful scenarios.
- Slow down when deleting seed data that still reproduces permissions, billing, or onboarding edge cases is still plausible.
- Prevent repeat cleanup by making teams create seed scripts with owner, scenario, data sensitivity, and retirement trigger.
Identify the Data Contract
Start with one application or database schema across seed scripts, fixtures, migrations, local setup docs, test data, and demo environments. The best cleanup scope is small enough that owners can answer quickly but wide enough to include the attachments that make removal risky.
| Field | Why it matters |
|---|---|
| Owner | Cleanup needs a person or team that can accept the decision |
| Current purpose | A short reason to keep the item, written in present tense |
| Last meaningful use | read/write activity, size, query plans, job dependencies, and retention rules |
| Dependency evidence | database metrics, query logs, application references, and reporting schedules |
| Risk if wrong | The outage, data loss, access failure, or rollback gap the review must avoid |
| Next action | Keep, reduce, archive, disable, remove, or investigate |
Do not make the inventory larger than the decision. A short list with owners and evidence beats a perfect spreadsheet that nobody is willing to act on.
Database Evidence to Collect
The useful question is not “how old is it?” It is “what would break, become harder to recover, or lose accountability if this disappeared?” For database seed script cleanup, collect enough evidence to answer that without relying on naming conventions.
| Check | What to look for | Cleanup signal |
|---|---|---|
| Seed purpose | Demo flow, test case, migration state, permission scenario, and original owner | The scenario no longer ships or has better coverage |
| Consumers | Local setup, CI jobs, tests, support scripts, docs, and onboarding guides | No current workflow runs the seed path |
| Data safety | Generated credentials, customer-like records, PII risk, and retention policy | Seed output should be removed or regenerated |
| Replacement coverage | Factories, migrations, snapshots, or smaller fixtures | Useful behavior remains covered after cleanup |
Use several signals together. Activity can miss monthly jobs and incident-only paths. Ownership can be stale. Cost can distract from security or recovery risk. The strongest case combines runtime data, dependency checks, owner review, and a rollback plan.
If the evidence conflicts, label the item “investigate” with a named owner and review date. That is still progress because the next review starts with a narrower question.
Archive Before Removal
Use the least permanent move that proves the decision. In database seed script cleanup, removal is only one possible outcome; reducing size, narrowing permission, shortening retention, archiving, or disabling a trigger may produce the same benefit with less risk.
- Replace broad seed data with narrow factories before deleting useful scenarios.
- Remove generated credentials and stale demo records first.
- Run local setup and CI from an empty database before final removal.
Track the cleanup candidate with a simple priority score:
| Score | Good sign | Bad sign |
|---|---|---|
| Impact | Meaningful spend, risk, toil, noise, or confusion disappears | The item is cheap and low-risk but politically distracting |
| Confidence | Owner, purpose, and dependency path are understood | The team is guessing from age or name |
| Reversibility | Restore, recreate, re-enable, or rollback path exists | Deletion would be the first real test |
| Prevention | A rule can stop recurrence | The same pattern will return next month |
Start with high-impact, high-confidence, reversible candidates. Defer confusing items only if they get an owner and a date; otherwise “defer” becomes another word for keeping waste permanently.
Data You Should Not Rush
Some cleanup candidates are supposed to look quiet. Do not rush these cases:
- Seeds that reproduce permission, migration, billing, or onboarding edge cases.
- Demo data used by sales, support, or documentation screenshots.
- Scripts that are the only known recreate path for old states.
For these cases, use a longer observation window, explicit owner approval, and a staged reduction. The point is not to avoid cleanup; it is to avoid making the first proof of dependency an outage.
Run the Data Review
Run database seed script cleanup as a decision review, not an open-ended hygiene project.
- Pick the narrow scope and export the candidate list.
- Add owner, current purpose, last-use evidence, dependency checks, and risk if wrong.
- Remove obvious false positives, then ask owners to choose keep, reduce, archive, disable, remove, or investigate.
- Apply the least permanent useful change first.
- Watch the signals that would reveal a bad decision.
- Complete the final removal only after the review window closes.
- Save a seed script cleanup pull request with consumer search, removed data, replacement fixtures, setup test, and rollback note.
For broader cleanup planning, use the cleanup library to pair this guide with related notes. If the cleanup has infrastructure impact, pair it with a visible owner, a rollback path, and a measurable business case. For infrastructure cleanup, the main cloud cost optimization checklist is a useful companion.
Keep Retention Explicit
Prevention should change the creation path, not just the cleanup path. For database seed script cleanup, the useful prevention fields are data owner, retention policy, recreate path, and review date. Make those fields part of normal creation and review.
- Create seed scripts with owner, scenario, data sensitivity, and retirement trigger.
- Keep fixtures close to tests when the data exists for coverage.
- Review seeds when product flows, roles, or demo environments change.
The recurring review should be short: sort by impact, pick the unclear items, assign owners, and close the loop on anything nobody claims. If the review keeps producing the same class of candidate, fix the creation path instead of celebrating repeated cleanup.
Example Decision Record
Use a compact record so the cleanup can be reviewed later without reconstructing the whole investigation.
| Field | Example entry for this cleanup |
|---|---|
| Candidate | Stale seeded demo tenants and onboarding records in development databases, demo environments, and automated test setup |
| Why it looked stale | Low recent activity, unclear owner, or no current consumer after the first review |
| Evidence checked | Seed purpose, Consumers, and owner confirmation |
| First reversible move | Replace broad seed data with narrow factories before deleting useful scenarios |
| Watch signal | The metric, alert, job, route, query, or owner complaint that would show the cleanup was wrong |
| Final action | Keep, reduce, archive, disable, or remove after one onboarding, test, and demo refresh cycle |
| Prevention rule | Create seed scripts with owner, scenario, data sensitivity, and retirement trigger |
This record is intentionally small. If the decision needs a long narrative, the candidate is probably not ready for removal yet. Keep investigating until the owner, evidence, reversible move, and prevention rule are clear.
FAQ
How often should teams do database seed script cleanup?
Use one onboarding, test, and demo refresh cycle for the first decision, then set a recurring cadence based on change rate. Fast-moving non-production systems may need monthly review; slower systems can be quarterly if every unclear item has an owner and a review date.
What is the safest first action?
The safest first action is usually ownership repair plus evidence collection. After that, replace broad seed data with narrow factories before deleting useful scenarios. That creates a visible test before permanent deletion.
What should not be removed quickly?
Do not rush anything connected to seeds that reproduce permission, migration, billing, or onboarding edge cases. Also slow down when the cleanup affects recovery, compliance, customer-specific behavior, rare schedules, or security response.
How do you make the decision useful later?
Write the decision as a small operational record: candidate, owner, evidence, chosen action, watch signals, rollback path, final date, and prevention rule. That format helps future engineers, search engines, and AI assistants understand the cleanup without guessing.