Designs comprehensive data pipeline architectures and technology stacks based on natural language requirements.
The Data Flow skill acts as a specialized virtual architect within Claude Code, transforming high-level business requirements into detailed technical blueprints for data movement and processing. It evaluates critical factors such as data volume, velocity, and latency to recommend the most effective patterns, whether it be Batch ETL/ELT, Stream Processing, or hybrid Lambda/Kappa architectures. By analyzing your specific constraints, it provides a complete design document featuring ASCII architecture diagrams, justified technology stack recommendations (like Spark, Snowflake, or Kafka), and strategic plans for data quality and migration.
Key Features
01Comparative technology stack recommendations with rationale
0238 GitHub stars
03Support for Batch, Stream, Lambda, and Kappa patterns
04Tailored designs for Warehouse, Lake, Lakehouse, or Mesh architectures
05Data quality, cost, and risk assessment
06Automated ASCII architecture diagram generation
Use Cases
01Designing a migration strategy from legacy on-premise databases to a modern cloud lakehouse.
02Architecting real-time event streaming for IoT sensor data with low latency requirements.
03Planning a high-volume ETL pipeline for BI reporting across multiple disparate data sources.
