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Interactive Schema Validation Explorer

See data quality enforcement in action! Use the interactive explorer below to step through 4 stages of JSON Schema validation. Watch how malformed data gets caught at the edge before corrupting your analytics systems.

How to Use This Explorer​

  1. Navigate using arrow keys (← →) or click the numbered stage buttons
  2. Compare the Input (left) and Output (right) showing validation at each stage
  3. Observe how invalid fields are detected and flagged (highlighted in red)
  4. Inspect the YAML code showing the JSON Schema definition
  5. Learn from the stage description explaining the data quality benefit

Interactive Schema Validation Explorer​

No Validation

Without schema validation, malformed data reaches your analytics systems, causing downstream failures, corrupted dashboards, and broken alerts. Invalid types, missing fields, and out-of-range values pollute your data lake.

Use ← → arrow keys to navigate
📥Input
{
"sensor_id": "sensor-42",
"timestamp": "not-a-valid-timestamp",
"readings": {
"temperature_celsius": "twenty-three",
"humidity_percent": 150
}
}
📤Output
❌ Downstream Impact:
Analytics: data parse failures
Alerts: false positives from bad values
Dashboard: "NaN" appears in graphs
ML Models: training on corrupted data
❌ Malformed Data Stored:
"timestamp": "not-a-valid-timestamp"
"temperature_celsius": "twenty-three"
"humidity_percent": 150 (impossible!)
Added/Changed
Removed
Completed Step
Current Step
Not Done Yet
📄New Pipeline Stepstep-0-no-validation.yaml
pipeline:
  processors: []
  # ❌ No validation
  # ❌ No schema checking
  # ❌ Malformed data flows through

Try It Yourself​

Ready to build schema-validated data pipelines? Follow the step-by-step tutorial:

Deep Dive into Each Step​

Want to understand each validation technique in depth?

Next: Set up your environment to build schema-validated pipelines yourself