Apply corrected readings without overwriting source history
A device sends a corrected cumulative reading two days late; a blind upsert destroys the value used in yesterday's report.
- Focused work estimate
- 5h + prerequisites
- Priority in the scenario
- Medium
- Engineering practice
- Temporal data · Append-only design
Estimated field mix
- Data engineering70%
- Database engineering30%
Field percentages are editorial estimates of the ticket's engineering focus. They total 100%; they are not measured time, proficiency scores, or ownership evidence.
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Project context
A fictional energy dashboard receives hourly device batches. Corrupt archives and late corrections leave operators unsure which readings reached reports.
Setup prerequisites
- Create synthetic device batches and local object-store fixtures.
- Understand checksums and bounded streaming.
Preceding work
Complete these dependencies, or supply their agreed outputs before taking this ticket.
- AINGEST-101 · Record a manifest before decoding a telemetry batch
- AINGEST-102 · Cap decompressed telemetry bytes before archive expansion
- AINGEST-103 · Normalize sensor units through a versioned conversion table
- AINGEST-104 · Commit telemetry rows and the batch checkpoint atomically
- AINGEST-105 · Quarantine malformed readings with usable row coordinates
Acceptance criteria
- Append corrections linked to source reading and revision.
- Define the effective reading deterministically.
- Reject contradictory equal-revision corrections for review.
Implementation constraints
- Preserve original ingestion time separately from measurement time.
Verification to include
- Apply a higher revision and inspect both historical values.
- Submit equal revision with changed value and retain the last valid projection.
Deliverables
- Correction model and conflict cases
Rollout and recovery
Enable corrections for one synthetic device; rebuild projections from retained history on rollback.
Value of the work
For the engineer: Practice batch integrity, replay and data-quality boundaries.
For the team: Review whether operational data can be traced, corrected and recovered.
Evidence boundaries
Outcome Evidence: Tests, patches, and runbooks are requested deliverables. They become Outcome Evidence only through a qualified Mission and immutable Evidence IDs.
Ownership Evidence: Independent adaptation must be observed under a declared verification policy and cite immutable Evidence IDs. Completing a planning ticket establishes no Ownership Evidence.