Building a System Monitoring Dashboard with Python, Prometheus, and Grafana

August 16, 2025 👩🏽‍🔬 Letisia Pangata'a

Intermediate

In this post, I’ll walk through the development of a full-stack system monitoring and alerting solution. The goal was to create a dashboard that tracks infrastructure health, performance metrics, and security events in real time, using open-source tools and Python scripting.

Project Goals

  • Real-time monitoring of CPU, memory, disk, and network usage
  • Custom alerting workflows for performance and security anomalies
  • Historical data analysis and visualisation
  • Security event correlation

Technology Stack

  • Python: For custom metrics collection and alerting scripts
  • Prometheus: For metrics scraping and time-series storage
  • InfluxDB: For historical data storage
  • Grafana: For dashboards and alerting UI

Development Process

1. Metrics Exporter Script

I started by writing a Python script using psutil to collect system metrics. The script formats the metrics in Prometheus exposition format and pushes them to the Prometheus Pushgateway every 15 seconds.

Key challenges:

  • Ensuring the metrics were formatted correctly for Prometheus
  • Handling errors when the Pushgateway was unavailable

2. Prometheus and Pushgateway Setup

I configured Prometheus to scrape metrics from the Pushgateway. This required editing the prometheus.yml file to add a scrape job for the exporter.

3. Visualisation with Grafana

After setting up Prometheus, I installed Grafana and connected it to Prometheus as a data source. I then imported a pre-built dashboard JSON to visualise the metrics in real time.

4. Alerting and Security Event Correlation

Additional Python scripts were developed for alerting (e.g., sending notifications when thresholds are crossed) and for correlating security events, such as detecting high CPU usage combined with repeated login failures.

Lessons Learned

  • The Prometheus Pushgateway expects metrics in a very specific format; even small mistakes can cause 400 errors.
  • Automating the setup with clear documentation makes it much easier to reproduce the stack on a new machine.
  • Combining multiple open-source tools provides powerful observability with minimal cost.

Conclusion

This project demonstrates how to build a robust monitoring and alerting stack using Python and popular open-source tools. The result is a flexible, extensible dashboard that provides real-time and historical insights into system health and security.

Check out the GitHub repository for the full code.

Disclaimer

This project was developed using a combination of publicly available learning resources, reference books, open source projects, and artificial intelligence tools. All efforts have been made to attribute and comply with relevant licenses. Contributions and insights from the broader open source and educational communities are gratefully acknowledged. This software is provided as-is, without warranty of any kind, express or implied. The author assumes no responsibility for any loss, damage, or disruption caused by the use of this code. It is intended for educational and experimental purposes only and may not be suitable for production environments.

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