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npx versuz@latest install a5c-ai-babysitter-library-specializations-data-engineering-analytics-skills-cdc-pagit clone https://github.com/a5c-ai/babysitter.gitcp babysitter/SKILL.MD ~/.claude/skills/a5c-ai-babysitter-library-specializations-data-engineering-analytics-skills-cdc-pa/SKILL.md---
name: CDC Pattern Implementer
description: Implements Change Data Capture patterns for real-time data integration
version: 1.0.0
category: Data Integration
skillId: SK-DEA-013
allowed-tools:
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- Write
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---
# CDC Pattern Implementer
## Overview
Implements Change Data Capture patterns for real-time data integration. This skill provides expertise in CDC configuration and implementation across various database and streaming platforms.
## Capabilities
- Debezium connector configuration
- CDC pattern selection (log-based, trigger-based, timestamp-based)
- Initial snapshot strategy
- Schema change handling
- Exactly-once delivery configuration
- Sink connector setup
- Tombstone handling
- CDC monitoring setup
## Input Schema
```json
{
"sourceDatabase": {
"type": "postgres|mysql|oracle|sqlserver",
"connection": "object"
},
"tables": ["string"],
"targetSystem": "kafka|kinesis|pubsub",
"requirements": {
"latencyMs": "number",
"exactlyOnce": "boolean"
}
}
```
## Output Schema
```json
{
"connectorConfig": "object",
"snapshotStrategy": "object",
"schemaConfig": "object",
"monitoringConfig": "object",
"documentation": "string"
}
```
## Target Processes
- ETL/ELT Pipeline
- Streaming Pipeline
- Data Warehouse Setup
## Usage Guidelines
1. Identify source database and tables for CDC
2. Define target streaming system
3. Specify latency and delivery guarantees
4. Configure appropriate snapshot strategy for initial load
## Best Practices
- Use log-based CDC when possible for minimal source impact
- Plan initial snapshot strategy carefully for large tables
- Implement proper error handling and dead letter queues
- Monitor replication lag and connector health
- Test schema evolution handling before production