Message Brokers & Event-Driven Architecture
I design and implement high-throughput, fault-tolerant messaging systems that enable asynchronous communication between your services. Using Kafka, RabbitMQ, or cloud-native solutions like AWS SQS/SNS, GCP PubSub, I build architectures that handle event streaming, task queues, and real-time data pipelines. My solutions ensure message durability, exactly-once processing, and horizontal scalability to support your most demanding workloads.
Development Services
Kafka Cluster Setup & Optimization
Deploy production-grade Kafka clusters with proper partitioning, replication, and monitoring. Configure ISR settings, log retention policies, and Zookeeper tuning for high availability.
RabbitMQ Architecture & Scaling
Design HA queues with mirrored nodes, implement dead-letter exchanges, and optimize for low-latency message delivery in complex routing scenarios.
Event Sourcing & CQRS Implementation
Build event-driven systems where state changes are captured as immutable events, enabling temporal queries and audit trails. Separate read/write models for scalability.
Real-Time Data Pipelines
Process high-volume streams with Kafka Streams, Flink, or Spark Streaming for transformations, aggregations, and analytics.
Message Schema Design & Validation
Define protobuf/Avro schemas for type-safe messaging, including schema evolution rules to maintain backward/forward compatibility.
Cloud-Native Messaging
Implement serverless messaging with auto-scaling, FIFO queues, and serverless consumers for cost-efficient event processing.
Benefits
Decoupled & Scalable Architecture
Services communicate asynchronously, eliminating tight coupling and allowing independent scaling of producers/consumers.
Fault Tolerance & Durability
Messages persist through failures with disk-backed storage and replicated queues, ensuring no data loss during outages.
Real-Time Processing Capabilities
Enable instant reactions to events with sub-second latency for time-sensitive workflows.
Ordered & Exactly-Once Processing
Guarantee message ordering within partitions and implement idempotent consumers to prevent duplicate processing.
Development Process
Requirements & Protocol Selection
Analyze throughput needs, latency tolerances, and delivery guarantees to choose between Kafka, RabbitMQ, or cloud queues.
Cluster/Queue Configuration
Set up brokers, partitions, replication factors, and retention policies aligned with your data volume and durability requirements.
Producer/Consumer Implementation
Develop efficient publishers like batching, compression and reliable consumers like acknowledgment strategies, error handling.
Monitoring & Optimization
Track lag, throughput, and error rates with Prometheus/Grafana. Fine-tune buffer sizes, timeouts, and concurrency for peak performance.