majesticlabs-dev Data Pipeline Testing
Test pipeline semantics, not only whether a job ran. Small readable fixtures and strong reconciliation oracles beat large opaque random datasets.
Inputs
Establish:
- Pipeline contract and stage grains.
- Source and target schemas.
- Incremental cursor and write strategy.
- Validation and quarantine behavior.
- Known incidents and edge cases.
- External systems and available test substitutes.
Workflow
1. Build a behavior matrix
Map each contract to a test layer:
| Behavior | Preferred test |
|---|---|
| Pure field transformation | Unit example or property test |
| Join and aggregation grain | Small table fixture with explicit expected output |
| Schema and semantic contract | Contract or validation test |
| Source and target integration | Integration test against realistic boundary |
| Incremental cursor and upsert | Multi-run stateful test |
| Backfill and replay | Historical range test plus reconciliation |
| Failure recovery | Fault injection and resume test |
| Published dataset | End-to-end consumer invariant |
Do not push every case into a slow end-to-end test.
2. Design fixtures from risks
Use fixture-design.md. Include only rows needed to prove behavior:
- null and empty distinctions
- duplicate keys
- boundary values
- malformed records
- unknown categories
- one-to-many joins
- unmatched relationships
- timezone and period boundaries
- late updates and deletes
- repeated delivery
- out-of-order records
Keep identifiers and expected outputs readable.
3. Test transformation logic
Assert values, grain, and invariants. Cover:
- filters
- type and unit conversions
- join cardinality
- deduplication tie-breakers
- aggregations and rounding
- null propagation
- deterministic ordering when required
A row-count assertion cannot prove transformation correctness.
4. Test stateful behavior
Run the pipeline through sequences:
- initial load
- same input again
- new records
- update to an existing key
- late-arriving record
- delete or tombstone
- failed partial attempt
- retry
- backfill overlapping live data
Verify target state and checkpoint after each run.
5. Test failures and quarantine
Inject:
- transient source failure
- target write failure
- malformed batch
- validation blocker
- individual bad record
- unavailable checkpoint store
Verify retry limits, atomicity, quarantine evidence, alerting, and safe resume.
6. Reconcile independently
Use an oracle independent of the implementation where possible. Compare:
- source and target key coverage
- accepted plus rejected totals
- sums or hashes by partition
- duplicate counts
- expected inserts, updates, and deletes
- checkpoint range and published partitions
7. Test schema evolution
Cover additive fields, missing required fields, type changes, renamed fields, semantic changes, and mixed-version inputs. Verify both compatibility and intended rejection.
8. Control generated data
Use generated or property-based fixtures for broad ranges and invariants, but fix seeds and preserve minimal failing examples. Do not let random fixtures replace named regression cases.
Deliverable
Return:
- Contract-to-test matrix
- Fixture inventory
- Unit, integration, and end-to-end boundaries
- Multi-run incremental scenarios
- Failure-injection scenarios
- Reconciliation oracles
- Schema-evolution coverage
- Commands and environment requirements
- Remaining untested risks
Quality Gate
- Tests assert values and grain, not only execution or row count.
- Incremental tests span multiple runs.
- Idempotency, late data, deletes, and backfills are covered.
- Failures prove atomicity and recovery.
- Fixtures are deterministic and readable.
- Reconciliation uses an independent oracle where feasible.