Event-Driven Architecture
Apache Kafka, event streaming, and asynchronous integration architecture for complex enterprise systems.
Event-driven architecture solves real problems — decoupling, scalability, real-time responsiveness — but it introduces new ones: schema drift, consumer lag, operational complexity, and eventual consistency challenges that are hard to reason about at scale.
NILUS brings senior-level Kafka and streaming architecture expertise to organisations designing or scaling event-driven platforms. We focus on getting the architectural decisions right early — topology, schema strategy, governance — because those are hardest to fix later.
What We Deliver
Kafka Topology Design
Topic naming conventions, partition strategy, consumer group design, retention policies, and producer/consumer separation patterns for complex enterprise landscapes.
Schema Governance
Schema Registry setup, Avro/Protobuf schema design, compatibility strategy, schema versioning, and breaking change management processes.
Integration Modernisation
Moving from point-to-point or ESB-based integration to event-driven patterns — strangler fig approach, dual-write elimination, and outbox pattern implementation.
Platform Architecture
Multi-cluster topologies, geo-replication, MirrorMaker 2 configuration, Kafka Connect source/sink design, and Kafka Streams processing architecture.
Security & Compliance
ACL design, SASL/TLS configuration, audit logging, data retention governance, and GDPR-compliant event deletion strategies for regulated environments.
Observability Design
Consumer lag monitoring, dead letter queue patterns, distributed tracing integration, alerting strategy, and operational runbooks for streaming platforms.
Architecture Challenges We Address
- Kafka adopted tactically but without a coherent topology or naming strategy
- Schema evolution breaking downstream consumers in production
- Dual-write patterns creating data inconsistency between databases and topics
- Consumer groups designed without considering rebalancing impact at scale
- No dead letter queue or poison pill handling — silent failures in production
- Integration teams treating Kafka like a message queue rather than an event log
- Regulatory requirements for data residency or retention conflicting with streaming defaults
Technologies
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