monitoring-analytics
R statistical computing and graphic tool for monitoring metrics from data scientists
2.5K
Limited public demo instance: https://monitoringartist.shinyapps.io/monitoring-analytics/
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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, ...

Overview of Monitoring Artist Dockerized monitoring ecosystem:
Please donate to author, so he can continue to publish other awesome projects for free:
docker run \
-d \
--name=shiny \
-p 3838:3838 \
monitoringartist/monitoring-analytics:latest
Deep monitoring knowledge and science skills are required, so only commercial support is available.
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:
Content type
Image
Digest
Size
517.7 MB
Last updated
about 8 years ago
Requires Docker Desktop 4.37.1 or later.