DevOps Monitoring and Alerting Dashboard
A Prometheus and Grafana observability stack with custom application metrics, alert rules and incident history.
Project overview
Applications expose metrics, Prometheus scrapes and stores them, Grafana visualises service health, and alert rules fire when error rates or latency cross defined thresholds. Alerts reach a chat channel with enough context to act on.
The project teaches the difference between collecting metrics and being able to answer a question during an incident, which is a distinction most student projects never touch.
Problem statement
Teams learn a service is down from a user report. Without metrics there is no way to tell whether a slowdown is the database, the network or a single bad deployment, so debugging becomes guesswork.
Proposed solution
Instrument the application with request rate, error rate and latency metrics, scrape them into a time-series database, build dashboards that answer specific operational questions, and define alert rules with thresholds justified by observed baselines rather than arbitrary numbers.
Key features
- Application instrumentation exposing a metrics endpoint
- Infrastructure metrics through node and container exporters
- Service dashboards covering traffic, errors, latency and saturation
- Alert rules with severity levels and sensible thresholds
- Notification routing to email or a chat channel
- Log aggregation correlated with metric spikes
- Uptime and availability reporting over a chosen period
- Incident timeline reconstruction from stored metrics
Technology stack
Metrics
Visualisation
Logs
Platform
System architecture
The flow below is what goes into your architecture diagram and the corresponding chapter of the report.
- Applications expose metrics on a dedicated endpoint
- Prometheus scrapes targets on a fixed interval and stores time series
- Exporters supply host and container level metrics
- Grafana queries Prometheus for dashboards defined as code
- Alertmanager evaluates rules, deduplicates and routes notifications
- Loki collects logs so a metric spike can be correlated with log lines
Expected output
- Live dashboards for a running application
- Alert firing and resolving during a simulated incident
- Availability report for the monitored period
- Runbook describing the response to each alert
Suitable branches, difficulty and duration
Documentation, review, demo and viva support
Documentation support
Abstract, problem statement, literature survey guidance, system architecture, ER and UML diagrams, module descriptions, test cases and screenshots — prepared in your department format.
Project review support
Delivery is split across your review calendar so review one has a design and plan, review two has working modules, and the final review has the integrated, tested system.
Demo support
A rehearsed demo path, seeded sample data, a reset script, and a recorded walkthrough kept as a backup in case something fails in the review room.
Viva preparation
A written question bank for this domain, a code walkthrough session, and practice at explaining each module in your own words — because the examiner will ask you.
Customisation options
Colleges reject duplicate topics, so most projects are adjusted before we start. These are the usual directions for this one.
- Add distributed tracing with OpenTelemetry and Jaeger
- Add synthetic uptime checks from an external location
- Add capacity forecasting from historical metric trends
- Add an on-call rotation schedule with escalation policies
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Enquire about the DevOps Monitoring and Alerting Dashboard project
Send your branch, college and first review date. You will get confirmation of scope, what the customisation would look like, and a delivery plan mapped to your reviews.