Data visualization is a way to make sense of the vast amount of information generated in the digital world. By converting raw data into a more understandable format, such as charts, graphs, and maps, it enables humans to see patterns, trends, and insights more quickly and easily. This helps in better decision making, strategic planning, and problem-solving. Visualization and understanding data are critical in platform-as-a-service (PaaS) offerings like Heroku.

Virtual machines give you a flexible and convenient environment where people can access different operating systems, networks, and storage while still using the same computer. This prevents them from purchasing extra machines, switching to other devices, and maintaining them. This helps companies to save costs and increase task efficiency. Although using VMs for everyday tasks may be enjoyable, ensuring consistent performance and performing maintenance can be daunting.

In this article, we will be covering how to monitor Kubernetes using Graphite, and we’ll do the visualization with Grafana. The focus will be on monitoring and plotting essential metrics for monitoring Kubernetes clusters. We will download, implement and monitor custom dashboards for Kubernetes that can be downloaded from the Grafana dashboard resources. These dashboards have variables to allow drilling down into the data at a granular level.

Prometheus is becoming a popular tool for monitoring Python applications despite the fact that it was originally designed for single-process multi-threaded applications, rather than multi-process. Prometheus was developed in the Soundcloud environment and was inspired by Google’s Borgmon. In its original environment, Borgmon relies on straightforward methods of service discovery - where Borg can easily find all jobs running on a cluster.

Before we jump into the specifics of Grafana and Datadog, let's look at the main comparison points. Grafana is a great dashboard that allows you to plug in essentially any data source in the world. Grafana is most commonly paired with Prometheus, Graphite, and Elasticsearch to provide a full APM, time-series, and logs monitoring stack.

Synthetic testing, also referred to as continuous monitoring or synthetic monitoring, is a technique for identifying performance problems with critical user journeys and application endpoints before they impair the user experience. Businesses may use synthetic testing to assess the uptime of their services, application response times, and the efficiency of consumer transactions on a proactive basis.

A Key Management Service (KMS) is used to create and manage cryptographic keys and control their usage across various platforms and applications. If you are an AWS user, you must have heard of or used its managed Key Management Service called AWS KMS. This service allows users to manage keys across AWS services and hosted applications in a secure way.

This is the first post of a 2 part series where we will set up production-grade Kubernetes logging for applications deployed in the cluster and the cluster itself. We will be using Elasticsearch as the logging backend for this. The Elasticsearch setup will be extremely scalable and fault-tolerant. ‍

In this tutorial, we will learn about configuring Filebeat to run as a DaemonSet in our Kubernetes cluster in order to ship logs to the Elasticsearch backend. We are using Filebeat instead of FluentD or FluentBit because it is an extremely lightweight utility and has a first-class support for Kubernetes. It is best for production-level setups. This blog post is the second in a two-part series. The first post runs through the deployment architecture for the nodes and deploying Kibana and ES-HQ.