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Interactive Aggregate Time Windows Explorer

See time-windowed aggregation in action! Use the interactive explorer below to step through 5 stages of data transformation. Watch as high-frequency event streams are progressively aggregated into meaningful analytics summaries.

How to Use This Explorer​

  1. Navigate using arrow keys (← →) or click the numbered stage buttons
  2. Compare the Input (left) and Output (right) JSON at each stage
  3. Observe how data is grouped (blue highlight) and aggregated (green highlight)
  4. Inspect the YAML code showing exactly what transformation was added
  5. Learn from the stage description explaining the windowing technique and analytics benefit

Interactive Aggregate Time Windows Explorer​

Original High-Frequency Events

Raw sensor data streaming at 60 events/minute per sensor, creating overwhelming data volume that needs aggregation.

Use ← → arrow keys to navigate
📥Input
{
"sensor_id": "temp_001",
"temperature": 72.3,
"humidity": 45.2,
"timestamp": "2025-01-15T10:23:45.123Z"
}
📤Output
{
"sensor_id": "temp_001",
"temperature": 72.3,
"humidity": 45.2,
"timestamp": "2025-01-15T10:23:45.123Z"
}
Added/Changed
Removed
Completed Step
Current Step
Not Done Yet
📄New Pipeline Stepinput.jsonl
# No processing - just input pass-through
input:
  file:
    paths: ["sensor-data.jsonl"]

pipeline:
  processors: []  # No transformation

output:
  stdout:
    codec: lines

Try It Yourself​

Ready to build this aggregation pipeline? Follow the step-by-step tutorial:

Deep Dive into Each Step​

Want to understand each windowing technique in depth?

Next: Set up your environment to build this aggregation pipeline yourself