Data Flow in Distributed Systems #

Data flow design determines throughput, latency, and failure behavior of modern platforms.

Communication patterns #

Synchronous request/response #

• low implementation complexity
• strong coupling between caller and callee
• sensitive to latency spikes and downstream failures

Asynchronous event-driven flow #

• better decoupling and scalability
• naturally supports retries and buffering
• requires careful idempotency and observability

Common architecture components #

• API gateway / ingress
• message broker (Kafka, RabbitMQ, SQS/PubSub)
• stream processing (Flink, Spark Streaming)
• storage tiers (cache, operational DB, warehouse/lake)

Reliability patterns #

• idempotent consumers
• dead-letter queues
• retry with backoff and jitter
• outbox pattern for transactional consistency

Performance considerations #

• partition strategy and key design
• backpressure management
• schema evolution and compatibility rules
• data retention and compaction policies

Operational checklist #

• traceability across producer and consumer services
• end-to-end latency SLI defined
• replay strategy documented
• failure injection tested in non-production