← Back to blocks

Messaging & Streaming Platform

Messaging & Streaming Platform

Summary

Asynchronous messaging and streaming infrastructure for decoupled, reactive systems — from simple queues/topics to log-based streams and processing, with comprehensive security and observability.

What it is

A comprehensive platform capability that provides messaging and streaming infrastructure to enable event-driven architectures. It spans simple messaging (queues/topics) and streaming (partitioned logs) with retention and replay, integrated with enterprise-grade security and observability.

Modes

  • Simple messaging: queues & topics for work dispatch, decoupling, and back-pressure (e.g., RabbitMQ, SQS)
  • Streaming: append-only, partitioned logs for high-throughput events, replay, and stateful processing (e.g., Kafka, Pulsar)
  • Hybrid: combining messaging reliability with streaming scale and retention

Core Responsibilities

  • Durable delivery, ordering where required, and intelligent routing
  • Consumer groups, auto-scaling, circuit breakers, and dead letter queues
  • Schema governance, compatibility, and event catalog management
  • End-to-end observability with distributed tracing and metrics
  • Security: encryption, authentication, authorization, and audit trails
  • Compliance: data retention policies, GDPR/privacy, and regulatory requirements

Security & Compliance Capabilities

  • End-to-end encryption (TLS 1.3, mTLS) and data-at-rest encryption
  • Fine-grained access control (RBAC, ABAC) with service identities
  • Audit logging and compliance reporting for regulated industries
  • Data sovereignty and residency controls
  • Vulnerability scanning and security patching automation
  • Zero-trust networking with service mesh integration

Observability & Monitoring

  • Distributed tracing with OpenTelemetry integration
  • Real-time metrics: throughput, latency, consumer lag, error rates
  • Automated alerting on SLA breaches and system anomalies
  • Log aggregation and correlation across event streams
  • Performance profiling and bottleneck detection
  • Business metrics: event volume, processing success rates, data quality

Architecture Patterns

  • Pub/sub topics with competing consumers and load balancing
  • Transactional outbox and saga orchestration patterns
  • Event sourcing + CQRS with snapshotting and replay
  • Streaming ETL with windowing and stateful processing
  • Event-driven microservices with circuit breakers
  • Change Data Capture (CDC) with exactly-once processing

Performance & Reliability Patterns

  • Partitioning strategies for horizontal scaling
  • Backpressure handling and flow control mechanisms
  • Idempotent processing and duplicate detection
  • Graceful degradation and chaos engineering readiness
  • Multi-region replication and disaster recovery
  • Auto-healing and self-balancing consumer groups

Tech Examples

  • Apache Kafka (with Kafka Streams, ksqlDB, Schema Registry)
  • Redpanda (high-performance Kafka-compatible)
  • RabbitMQ (with clustering and federation)
  • AWS MSK, EventBridge, SQS/SNS
  • Azure Event Hubs, Service Bus
  • Google Pub/Sub, Cloud Tasks

KPIs/SLIs/SLOs

  • End-to-end latency (P50, P95, P99) and throughput
  • Message delivery success rate and duplicate rates
  • Consumer lag/backlog and processing time
  • System availability and MTTR for incidents
  • Security: unauthorized access attempts and compliance violations
  • Cost efficiency: resource utilization and scaling events