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Schema Enforcement Explorer

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

How to use this explorer

Move between stages with the arrows or the numbered stage list. Each stage shows the input it receives on the left and the output it produces on the right, with the lines that changed marked. The configuration that makes the change sits below the comparison, and the final stage shows the complete pipeline.

The 4 stages in order

  1. No Validation
    Start with authored records that include missing fields, unexpected types, and out-of-range values.
  2. Define JSON Schema
    Define the required fields, types, and ranges in the example JSON Schema.
  3. Validate & Route
    Route matching and rejected records to separate named outputs for inspection.
  4. Monitor Quality
    Illustrate metadata and metrics fields that a deployment could connect to its own observability system.

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!)
Partial snippet01-no-validation.yaml
pipeline:
  processors: []
  # ❌ No validation
  # ❌ No schema checking
  # ❌ Malformed data flows through