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Case study 07Distributed SystemsFull-Stack

Commerce Platform Modernization

Independently deployable services and Kafka event pipelines sustaining 99.95% availability.

Role
Senior engineer · services and event pipelines
Core stack
KafkaMicroservicesVersioned APIsSchema validationAutoscaling
99.95%
Availability sustained for core services
−18%
Cloud infrastructure cost via autoscaling and query tuning
6
Engineers aligned on release checks and reviews

Overview

Modernized a client commerce platform into independently deployable services, with event-driven order and inventory processing built to recover cleanly from bad data.

The platform had to evolve without breaking existing clients or data, while order and inventory processing needed to be reliable, observable, and recoverable when malformed events appeared.

Product walkthrough

The flow, screen by screen

Pipeline health
01 / 02

Pipeline health. Throughput, consumer lag, and error rates for order and inventory topics, alongside the availability of the core services.

ArchitectureCorePlatformAsync / control
eventseventsinvalidfix & replayStorefront & clientsVersioned APIsbackward-compatibleOrder serviceown databaseInventory serviceown databaseKafkaschema-validated eventsDead-letter queueisolated failuresEvent replayrecovery tooling
Clients use versioned APIs; order and inventory services exchange schema-validated events through Kafka, with dead-letter handling and replay.

Approach

  1. 01Split the platform into independently deployable services with clear domain boundaries and versioned APIs.
  2. 02Used backward-compatible database migrations so services and schemas could change without downtime.
  3. 03Developed Kafka workflows for order and inventory processing with schema validation, dead-letter queues, and event-replay tooling.
  4. 04Improved autoscaling and database queries to reduce cloud infrastructure costs.
  5. 05Standardized release checks and technical reviews across a six-person engineering team.

Key decisions

Versioned APIs at the boundary

Consumers can migrate at their own pace while services evolve independently.

Dead-letter queues with replay

Bad events are isolated instead of blocking the stream, then replayed once fixed, with no data loss.

Backward-compatible migrations

Expand-then-contract schema changes let old and new code run side by side during deploys.

Outcomes

  • More consistent deployments and incident response across the team.
  • Recoverable event processing through schema validation and replay.
  • Kafka
  • Microservices
  • Versioned APIs
  • Schema validation
  • Autoscaling
  • SQL
  • CI/CD

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