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Test Strategy for a Data Pipeline

Table output Has example

Test data transformations and quality.

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Create a test strategy for my data pipeline. Pipeline description (sources, transformations, targets, schedule): [PIPELINE_DESCRIPTION]. Tools and language: [TOOLS]. Data volumes: [VOLUMES]. Consumers and how they use the output: [CONSUMERS]. Past data incidents: [PAST_INCIDENTS].

Return tables:
1. Risk map: Stage | Risk (schema change, late data, duplicates, null explosion, wrong joins, timezone errors, rounding, partial loads, silent failure) | Impact | Detection method.
2. Test layers: Layer | What it tests | Tools | Example tests | Where it runs. Cover unit tests for transformation functions, contract tests for source schemas, data quality tests (uniqueness, not null, accepted values, ranges, referential integrity, freshness), reconciliation tests (row counts and totals between stages), and end-to-end tests on small fixtures.
3. Data quality rules: Table | Column | Rule | Severity (block, warn) | Owner.
4. Monitoring: Metric | Threshold | Alert path | On-call action.
5. Release process: testing changes on a copy, comparing output before and after (diff tool), backfill procedures, and rollback.

Provide example test code for three rules and a fixture design. Note which tests are cheap and should run on every commit and which are expensive and should be scheduled. Include a runbook outline for incidents.

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Risk map (excerpt)
Stage | Risk | Impact | Detection
Ingest | Source adds a column or changes type | Breaks load | Schema contract test
Transform | Join duplicates rows | Revenue doubled | Row count and sum reconciliation
Load | Partial load after failure | Missing day | Freshness and count check
Test layers: unit tests for the currency conversion function; data quality tests (order_id unique, amount not null, status in accepted list); reconciliation (sum of amount equal between staging and warehouse within 0.01); end-to-end run on a 1,000-row fixture.
Rule example: freshness fails (block) if the latest event is older than 6 hours.
Cheap on every commit: unit and schema tests. Scheduled nightly: reconciliation and full-volume checks.

• data-pipeline
• testing