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· 27 min read
Lukas Lösche

In Actionable Cloud Infrastructure Metrics, we explored how to create metrics, export them into a time series database, and visualize them with Grafana. Today, we'll take a look at how to build a web app using Streamlit, a framework that turns data into web apps.

Sheep looking inside a black box

If you are not familiar with Python, don't worry—we're going to keep it simple! In Prerequisites, we'll go over installing Python and the coding techniques utilized in this project.

· 7 min read
Anja Freihube

Software engineers working with AWS have every cloud service imaginable at their fingertips, and developer velocity could hardly be higher. But, even the most shiny of coins has two sides.

While developers can freely spin up compute instances and databases in addition to less tangible things like Lambda functions or virtual identities—at some point, someone will ask, "What is all of this?"

And as that person hacks away in the CLI trying to get an overview of resources spanning multiple AWS accounts, they will inevitably get frustrated.

While Amazon has been a pioneer in cloud computing and offers the largest array of services, there are some things that just aren't so ideal. Namely, API consistency.

In this post, I describe a few of the challenges and quirks with the AWS API and why we're building Resoto. (Spoiler alert: It is so that you don't have to!)

· 10 min read
Matthias Veit

Today's world of cloud computing is complex. There are many cloud providers, each with their own set of services. Getting insights out of your infrastructure requires specialized understanding of the data from each service.

Cloud Service Diversity

Properties in different services may have different names but the same meaning, or vice versa. To interpret properties, we need to ensure that values have a defined unit of measurement and one base unit. You can see the challenge if you imagine the many ways you can specify the size of a volume, the number of CPU cores, or even timestamps.

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· 12 min read
Matthias Veit

Kubernetes has dramatically improved the way we manage our workloads. It has become the de-facto standard for deploying and managing containerized applications, and is available in all major cloud providers.

A typical setup consists of distinct Kubernetes clusters for each application stage (e.g., dev, test, prod) or a cluster per tenant, and Kubernetes clusters shared between different users and teams often utilize namespaces and roles to control access. Deploying a single application to a Kubernetes cluster usually consists of tens to hundreds of resources (e.g., deployments, services, ConfigMaps, secrets, ingresses, etc.).

Even a relatively simple setup quickly becomes tedious to manage as the resource count grows. It is difficult for a human to keep track of resources, especially with user access limited to certain clusters in select namespaces.

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