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Monitoring Analytics

Limited public demo instance: https://monitoringartist.shinyapps.io/monitoring-analytics/

If you like or use this project, please provide feedback to author - Star it ★.

Yes, monitoring is not a rocket science usually. However your monitoring system keeps a lot of time series data. You can you use science / math / statistics and turn your data into knowledge, which can be used to improve your monitoring systems and settings. Don't estimate any static thresholds for your metrics. Set them based on your real values. If you don't know, what is normal value, then try to detect anomalies in your series. Remember, your only limitation is your data science imagination: histograms, linear/polynomial/... trends, prediction, anomaly detection, correlation, 3d visualization, heat map, ...

Monitoring Analytics

Overview of Monitoring Artist Dockerized monitoring ecosystem:

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Dockerized Monitoring Analytics

docker run \
  -d \
  --name=shiny \
  -p 3838:3838 \
  monitoringartist/monitoring-analytics:latest

Support

Deep monitoring knowledge and science skills are required, so only commercial support is available.

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Author

Devops Monitoring Expert, who loves monitoring systems and cutting/bleeding edge technologies: Docker, Kubernetes, ECS, AWS, Google GCP, Terraform, Lambda, Zabbix, Grafana, Elasticsearch, Kibana, Prometheus, Sysdig, ...

Summary:

Professional devops / monitoring / consulting services:

Monitoring Artist

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R statistical computing and graphic tool for Zabbix monitoring metrics from data scientists

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