Microservices Architecture

Designs and implements microservices where the split is justified by real boundaries in the domain and the operational cost of running them is planned for so the architecture pays back the complexity it adds.

Everything included under this practice line.

01

Domain-driven decomposition: event storming context mapping and service boundary definition tied to the business

02

Inter-service communication: synchronous APIs asynchronous events and the choice between them per interaction

03

Data ownership: one service owns each data set with read models and eventual consistency where cross-service reads are needed

04

Service mesh and gateway: Istio Linkerd or equivalent for traffic shaping retries and mTLS between services

05

Distributed tracing and correlation: OpenTelemetry across the call graph so incidents can actually be diagnosed

06

Deployment topology on Kubernetes with progressive delivery canary releases and automated rollback

07

Operational tooling: platform templates service catalog and a paved path so new services do not each reinvent the setup

The stack we reach for.

KubernetesIstiogRPCKafkaOpenTelemetryArgo CDBackstagePostgreSQLRedisHelm

What the business gets, measured.

  • Independent release cycles per service so one team is not blocked waiting on another
  • Fault isolation so a single service failure does not take down the whole product
  • Right-sized scaling cost because hot paths scale independently of the rest
  • Faster onboarding of new services through a paved-path template rather than a bespoke setup each time
  • Faster incident diagnosis from tracing that follows the request across services

The specialists behind this practice line.

A distributed systems architect leads the decomposition and boundary decisions alongside domain engineers who own each service. A platform engineer builds the paved path and service catalog and a site reliability specialist wires in tracing alerting and progressive delivery.

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