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Cloud & DevOps · Capstone
Microservices Observability Stack
Traces, metrics and logs across services that call each other
4 weeks build
Overview
Four small services that call each other, instrumented end to end. A request carries one trace ID through every hop, metrics feed alert rules, logs correlate to traces, and a fault injector lets you break a service on demand and watch the dashboards find it.
What makes it stand up
- 01Distributed tracing across every service hop
- 02Fault injection, so your demo can show a real incident
- 03Alert rules that fire on the golden signals
- 04Logs correlated to traces by ID, not by guesswork
- 05One docker compose up brings the whole stack live
Module breakdown
- 01
Services
Four instrumented Go services with a shared API.
- 02
Tracing
OpenTelemetry context propagation into Jaeger.
- 03
Metrics
Prometheus scraping, RED metrics and alert rules.
- 04
Dashboards
Grafana boards for latency, errors and saturation.
- 05
Chaos
Injected latency and failures to prove it all works.
After this, you will be able to
- Explain the difference between a metric, a log and a trace
- Find an injected fault live, using only the dashboards
- Describe context propagation across a service boundary
Technology stack
- Go
- Docker
- OpenTelemetry
- Prometheus
- Grafana
- Jaeger
- Level
- Capstone
- Domain
- Cloud & DevOps
- Build time
- 4 weeks
- Difficulty
- Rating
- 4.8 / 5 · 69
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support