Designs optimal windowing strategies for stream processing
Scanned 9/2/2026
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---
name: Stream Processing Windowing Designer
description: Designs optimal windowing strategies for stream processing
version: 1.0.0
category: Streaming
skillId: SK-DEA-018
allowed-tools:
- Read
- Write
- Edit
- Glob
- Grep
- Bash
graph:
domains: [domain:data-engineering]
specializations: [specialization:data-engineering-analytics]
skillAreas: [skill-area:streaming-realtime-processing, skill-area:kafka-stream-processing]
roles: [role:data-engineer, role:analytics-engineer]
workflows: [workflow:data-pipeline-deployment]
topics: [topic:event-driven-architecture]
---
# Stream Processing Windowing Designer
## Overview
Designs optimal windowing strategies for stream processing. This skill provides expertise in window types, watermarks, and trigger strategies for streaming applications.
## Capabilities
- Window type selection (tumbling, sliding, session, global)
- Watermark strategy design
- Late data handling
- Trigger configuration
- Window aggregation optimization
- State management recommendations
- Exactly-once semantics configuration
## Input Schema
```json
{
"useCase": "string",
"eventTimeField": "string",
"latencyRequirements": {
"maxLatencyMs": "number",
"allowedLateMs": "number"
},
"aggregations": ["object"]
}
```
## Output Schema
```json
{
"windowConfig": {
"type": "string",
"size": "string",
"slide": "string"
},
"watermarkConfig": "object",
"triggerConfig": "object",
"lateDataHandling": "object"
}
```
## Target Processes
- Streaming Pipeline
- Feature Store Setup
## Usage Guidelines
1. Define use case and event time field
2. Specify latency requirements
3. List aggregation operations needed
4. Consider late data arrival patterns
## Best Practices
- Choose window type based on business requirements
- Configure watermarks based on expected lateness
- Use appropriate triggers for latency vs completeness tradeoff
- Plan state management for long windows
- Test with realistic event time distributions
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