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Schema Enforcement Explorer

Use the stage controls to compare the synthetic input, authored output, and configuration at each step.

Interactive pipeline

ENFORCE SCHEMA

Schema checks and branch configuration

Stage 1 of 4

No Validation

Start with authored records that include missing fields, unexpected types, and out-of-range values.

5HighlightsAuthored emphasis only—not a computed diff.
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Input

{"sensor_id": "sensor-42",Authored highlight: "timestamp": "not-a-valid-timestamp","readings": {Authored highlight: "temperature_celsius": "twenty-three",Authored highlight: "humidity_percent": 150}}

Output

Authored highlight: ❌ Downstream Impact:Analytics: data parse failuresAlerts: false positives from bad valuesDashboard: "NaN" appears in graphsML Models: training on corrupted dataAuthored highlight: ❌ Malformed Data Stored:"timestamp": "not-a-valid-timestamp""temperature_celsius": "twenty-three""humidity_percent": 150 (impossible!)
Stage configuration01-no-validation.yaml
pipeline:
  processors: []
  # ❌ No validation
  # ❌ No schema checking
  # ❌ Malformed data flows through