Notebook Cleanup: Turn Ad Hoc Analysis Into Owned Assets

Notebook cleanup is not just deleting old .ipynb files. A notebook can be a scratchpad, a model experiment, a finance reconciliation, a customer investigation, or the only reproducible explanation for a product decision. The cleanup problem is deciding which notebooks should become owned assets and which can be archived or removed.

The useful output is a notebook disposition: keep as source material, convert to a scheduled pipeline, export a static result, archive with data snapshot, or delete after owner approval.

Key takeaways

• Check referenced datasets, output artifacts, dashboards, model handoffs, and decision links.
• Archive notebooks with required data snapshots when reproducibility matters.
• Do not rush analysis used for audits, experiments, customer incidents, or metric definitions.
• Convert recurring notebooks into owned jobs or documented reports.
• Prevent repeat clutter by requiring purpose and review date for shared notebooks.

Classify the Notebook’s Job

Start with one slice of data teams where the cleanup candidates are visible to both the owner and the person paying the operational cost. 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 notebook cleanup, collect enough evidence to answer that without relying on naming conventions.

Check	What to look for	Cleanup signal
Owner trail	Tags, labels, CODEOWNERS, tickets, runbooks, and service catalog entries	No owner can explain the current purpose
Runtime use	Recent requests, writes, reads, executions, deploys, errors, or alerts	Activity is absent across the review window
Dependency path	DNS, queues, jobs, dashboards, policies, manifests, and downstream consumers	No dependent system still points at it
Recovery path	Backup, export, recreate command, rollback plan, or retained configuration	The team can recover if the decision is wrong

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.

Use a small manifest to separate disposable exploration from owned analysis:

notebook: churn-cohort-review.ipynb
owner: data-science
source_tables:
  - analytics.subscriptions
outputs:
  - q2-retention-deck.csv
decision: archive_with_snapshot

The manifest captures why the notebook matters. It does not guarantee reproducibility unless the data snapshot, package versions, and output artifacts are preserved.

Archive Before Removal

Use the least permanent move that proves the decision. In notebook 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.

• Add or repair ownership metadata before changing anything ambiguous.
• Reduce scope, size, retention, replicas, or permissions before permanent removal when the blast radius is uncertain.
• Disable or detach during a monitored window, then remove only after the owner accepts the evidence.

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:

• Rare scheduled work that runs monthly, quarterly, or only during incidents.
• Customer-specific integrations that do not show up in average traffic charts.
• Recovery, audit, compliance, rollback, or legal-retention paths.

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 notebook cleanup as a decision review, not an open-ended hygiene project.

1. Pick the narrow scope and export the candidate list.
2. Add owner, current purpose, last-use evidence, dependency checks, and risk if wrong.
3. Remove obvious false positives, then ask owners to choose keep, reduce, archive, disable, remove, or investigate.
4. Apply the least permanent useful change first.
5. Watch the signals that would reveal a bad decision.
6. Complete the final removal only after the review window closes.
7. Save a short decision record with owner, evidence, change made, rollback path, and recurrence rule.

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 notebook cleanup, the useful prevention fields are data owner, retention policy, recreate path, and review date. Make those fields part of normal creation and review.

• Require owner and review-date metadata at creation time.
• Put the cleanup decision near the system of record: infrastructure code, runbook, ticket, or service catalog.
• Review the top unresolved candidates on a recurring schedule instead of running one large cleanup project.

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 notebooks in data teams
Why it looked stale	Low recent activity, unclear owner, or no current consumer after the first review
Evidence checked	Owner trail, Runtime use, and owner confirmation
First reversible move	Add or repair ownership metadata before changing anything ambiguous
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 a window long enough to include scheduled and low-frequency use, not just a quiet afternoon
Prevention rule	Require owner and review-date metadata at creation time

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 notebook cleanup?

Use a window long enough to include scheduled and low-frequency use, not just a quiet afternoon 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, add or repair ownership metadata before changing anything ambiguous. That creates a visible test before permanent deletion.

What should not be removed quickly?

Do not rush anything connected to rare scheduled work that runs monthly, quarterly, or only during incidents. 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.