Delve into advanced Kubernetes features like monitoring, alerting, logging, and auto-scaling. Gain insights into creating resilient, self-adaptive clusters and applications with minimal manual intervention.

The world of DevOps has exploded with the help of technologies like Kubernetes and Docker.  
In this course, you will learn about more advanced Kubernetes features such as monitoring, alerting, logging, and scaling - crucial concepts for any DevOps professional. You will learn how to monitor clusters, how to send alerts when potential issues arise, and how to query the whole system’s metrics and logs. 
By the end of this course, you will have the required knowledge to make your clusters and applications truly dynamic and resilient, so that they require minimal manual involvement and are self-adaptive.

Advanced Kubernetes Techniques: Monitoring, Logging, Auto-Scaling

# `Prometheus` as a dashboard

It doesn't take more than a few minutes with `Prometheus` to discover that it is not designed to serve as a dashboard. Sure, you can create graphs in `Prometheus` but they are not permanent, nor do they offer much in terms of presenting data. `Prometheus`'s graphs are designed to be used as a way to visualize ad-hoc queries. And that's what we need most of the time. When we receive a notification from an alert that there is a problem, we usually start our search for the culprit by executing the query of the alert and, from there on, we go deeper into data depending on the results. That is if the alert does not reveal the problem immediately, in which case there is no need to receive notifications since those types of apparent issues can usually be fixed automatically.

But, as I already mentioned, `Prometheus` does not have dashboarding features, so we'll have to look for a different tool.

# `Grafana` as a dashboard

These days, the choice of a dashboard is easy. [Grafana](https://grafana.com/) is the undisputed ruler in the area. Other solutions are too old to bother with, or they do not support `Prometheus`. That is not to say that `Grafana` is the best tool on the market. But the price is right (it's free), and it works with many different data sources. We could, for example, argue that [Kibana](https://www.elastic.co/products/kibana) is just as good as `Grafana`, or even better. But, it is limited to data from **ElasticSearch**. While `Grafana` can also use data from **ElasticSearch**, it supports many others. Some might say that [DataDog](https://www.datadoghq.com) is a better choice. Still, it suffers from the same problem as **Kibana**. It is tied to a specific source of metrics. There is no flexibility and no option to combine data from other data sources. More importantly, neither of the two supports `Prometheus`.

I'll save you from further comparison with other tools. You can try them yourself. For now, you'll need to trust me that `Grafana` is good, if not the best choice.


In this lesson, we will see and compare the different tools we can use for dashboards.

Before Getting Started

Autoscaling Deployments and StatefulSets

Auto-Scaling Nodes Of A Kubernetes Cluster

Collecting and Querying Metrics and Sending Alerts

Debugging Issues Discovered Through Metrics and Alerts

Extending HorizontalPodAutoscaler With Custom Metrics

Visualizing Metrics And Alerts

Collecting And Querying Logs

Conclusion

Which Tools Should We Use for Dashboards?

`Prometheus` as a dashboard #

Which Tools Should We Use for Dashboards?

Prometheus as a dashboard #

`Prometheus` as a dashboard #