Distributed Systems
Microservices, event-driven architectures, and real-time data pipelines. Built for resilience under pressure and designed to perform when it matters.
What We Build
Microservices Architecture
- Service decomposition and domain boundaries
- API gateway patterns (REST, GraphQL, gRPC)
- Service discovery and registration
- Inter-service communication strategies
Event-Driven Systems
- Kafka, RabbitMQ, AWS SQS/SNS
- Event sourcing and CQRS patterns
- Asynchronous processing pipelines
- Exactly-once delivery guarantees
Real-Time Data Processing
- Stream processing (Kafka Streams, Flink)
- WebSocket and Server-Sent Events
- Real-time analytics and aggregations
- Live dashboards and monitoring
Service Mesh
- Istio, Linkerd, Consul Connect
- Traffic management and routing
- Mutual TLS and service-to-service auth
- Circuit breakers and retry logic
Data Consistency
- Distributed transactions (Saga pattern)
- Eventual consistency strategies
- Conflict resolution and reconciliation
- Two-phase commit protocols
Observability
- Distributed tracing (Jaeger, Zipkin)
- Centralized logging (ELK, Loki)
- Metrics aggregation (Prometheus)
- Service dependency mapping
Design Principles
Fault Tolerance
Systems fail. Network partitions happen. We design for failure as the default state. Circuit breakers, bulkheads, and graceful degradation ensure your system stays operational even when components fail.
Horizontal Scalability
Add capacity by adding instances, not by upgrading hardware. Stateless services, load balancing, and distributed state management enable linear scaling.
Eventual Consistency
Not every operation needs immediate consistency. We help you identify where eventual consistency is acceptable, trading strict consistency for availability and partition tolerance (CAP theorem).
Decoupled Services
Services that don't know about each other can't create cascading failures. Event-driven architectures and message queues create natural isolation boundaries.
Idempotency
Operations should be safe to retry. When network failures occur, idempotent APIs ensure duplicate messages don't corrupt data.
Backpressure
When downstream services slow down, upstream services must adapt. Backpressure mechanisms prevent cascade failures and resource exhaustion.
Common Patterns We Implement
Saga Pattern
Distributed transactions across services using compensating transactions for rollback.
CQRS
Command Query Responsibility Segregation - separate read and write models for scalability.
Event Sourcing
Store state changes as a sequence of events, enabling time travel and audit trails.
Circuit Breaker
Prevent cascading failures by failing fast when downstream services are unavailable.
Bulkhead
Isolate resources to prevent a single failing component from exhausting all resources.
Strangler Fig
Incrementally migrate monoliths to microservices by gradually replacing functionality.
Building a Distributed System?
We'll help you navigate the complexity and build a resilient, scalable architecture.
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