data-flow
Data Flow Design Command
Design a data pipeline architecture based on requirements description.
Usage
/sd:data-flow <description>
Arguments
description(required): Natural language description of data flow requirements- Include: data sources, destinations, transformations, latency needs
- Mention any constraints: cost, team expertise, existing infrastructure
Examples
/sd:data-flow Real-time customer activity tracking from web and mobile to analytics dashboard
/sd:data-flow Batch ETL from 5 PostgreSQL databases to Snowflake for BI reporting
/sd:data-flow Event streaming from IoT sensors with 10ms latency requirement for anomaly detection
/sd:data-flow Migrate legacy Oracle data warehouse to cloud lakehouse architecture
Workflow
-
Parse Requirements Extract from description:
- Data sources and types
- Volume and velocity estimates
- Latency requirements
- Transformation complexity
- Target systems/use cases
-
Spawn Data Architect Agent Use the
data-architectagent to design the pipeline. The agent will:- Recommend appropriate architecture (lake/lakehouse/warehouse/mesh)
- Select pipeline pattern (batch/stream/hybrid)
- Propose technology stack
- Design data flow stages
-
Present Architecture Display the design including:
- Architecture diagram (ASCII)
- Technology recommendations with rationale
- Pipeline stage breakdown
- Data quality strategy
- Cost and risk considerations
Architecture Patterns
The command may recommend:
Data Platform Types
- Data Warehouse: For structured BI/reporting
- Data Lake: For ML and diverse data types
- Data Lakehouse: For unified BI and ML
- Data Mesh: For decentralized domain ownership
Pipeline Patterns
- Batch ETL/ELT: Scheduled bulk processing
- Stream Processing: Real-time event handling
- Lambda: Batch + speed layers
- Kappa: Stream-only with replay
Processing Frameworks
- Spark: Large-scale batch and streaming
- Flink: Low-latency streaming
- dbt: SQL-based transformations
- Kafka: Event streaming backbone
Output
The command produces a comprehensive design document with:
- Requirements summary
- Recommended architecture with rationale
- Architecture diagram
- Technology stack recommendations
- Pipeline design (stages, transformations)
- Data quality strategy
- Migration path (if applicable)
- Cost considerations
- Risk analysis
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