Data Flow Diagram

Visualize data movement, transformations, and storage across systems to identify bottlenecks and design efficient pipelines.

Context

You are designing how data flows between systems. Document data sources, transformations, sinks, and timing. Read existing architecture documents and data pipelines.

Domain Context

Based on enterprise data architecture patterns and streaming frameworks:

• Batch Processing: High-latency, high-throughput; good for analytics and reports
• Stream Processing: Low-latency, event-driven; good for realtime features and monitoring
• Lambda Architecture: Batch + streaming for both completeness and speed
• Transformation Stages: Extract, transform (business logic), load; validate at each stage
• Data at Rest vs In Motion: Storage systems vs message queues and streams

Instructions

1. Identify Data Sources: List all sources (APIs, databases, event streams, user uploads). For each, note volume, frequency, data format, and reliability.

2. Map Transformations: What business logic applies? Normalize, enrich, aggregate, filter? Where does the transformation happen (source, pipeline, destination)? What's the latency requirement?

3. Define Sinks and Destinations: Where does processed data land? Data warehouse for analytics? Cache for serving? Message queue for downstream consumers? API for external systems?

4. Choose Processing Model: Batch (daily jobs) or streaming (realtime)? Hybrid (Lambda)? Consider latency, cost, operational complexity, and consistency needs.

5. Diagram the Flow: Show sources, transformation stages, queues, storage, consumers. Mark synchronous vs asynchronous flows. Identify potential failure points and bottlenecks.

Anti-Patterns

• Spaghetti Pipelines: Too many ad-hoc integrations between systems. Result: impossible to understand data lineage, hard to modify. Guard: Create canonical pipeline architecture; consolidate sources.
• No Staging Environment: Transform data directly to production analytics. Result: bugs corrupt historical data. Guard: Stage transformations; validate data quality before final load.
• Ignoring Failure Recovery: Assume pipelines always succeed. Result: gaps in data, silent failures. Guard: Implement idempotent transformations, track completion, replay on failure.
• Tight Coupling Between Stages: Output of one transformation directly feeds next without buffering. Result: failure cascades. Guard: Use message queues; decouple producers and consumers.

Further Reading

• Designing Data-Intensive Applications by Martin Kleppmann — data pipeline patterns
• The Art of Statistics by David Spiegelhalter — data quality and validation
• Fundamentals of Data Engineering by Joe Reis and Matt Housley — modern data pipelines