go-cloud VS kubernetes

Even when going multi-cloud you can employ different strategies. Vault is definitely one of them, but you can also use federation to exchange one cloud's credentials for another's, giving you the ability to centralize secrets in one of them. You can use a layer of abstraction like GoCloud [0]. You can also build for each cloud separately and decide either not to centralize secrets at all, or build some trivial bespoke tooling to synchronize some of them. I'm not endorsing any of the options, just pointing out that Vault isn't the only one.

https://github.com/google/go-cloud

> If Google started adding Google Cloud specific primitives natively to Go would you call that forward thinking as well?

Go actually ships with a quite forward thinking SQL interface. It's an abstract interface over a DB, and you just import the "driver" that powers it. The driver conforms to a standard interface, so all of them behave roughly the same.

I think this is what everyone wants from Deno/etc - why can't there also be a KV interface that's universal, or a Queue interface that's universal?

People attempted this w/ go [1], where it attempts to use the same nice experience of the SQL logic, but it never seemed to gain traction.

https://gocloud.dev

github.com/google/go-cloud/pubsub package provides a set of interfaces and tools to work with publish/subscribe messaging. This package allows easy communication between independent components by decoupling the sender and the receiver.

I'm guessing this is a reasonable example of what they're using it for? server.go

https://github.com/google/go-cloud probably out of context, but not IaC but agnostic backend development with Go across multiple clouds

The plan is to have a process for generating AWS CDK targeting Lambda (pluggable providers, but start with AWS CDK, because it's what I use), and to use the Google Cloud Development Kit (also called CDK, but not the same) https://github.com/google/go-cloud to abstract the services.

In this post, I will talk about an exciting project maintained by the team that develops the Go language: the Go Cloud Development Kit, also known as the Go CDK.

The API of gocloud.dev, is stable. We are at ariga.io, already use gocloud.dev for internal service, and even in the public for easy adopt multi-clouds provider: https://github.com/ariga/atlas/commit/ef0b0eae65a61375482497ceb9ed9790a469b56e

On Go, which has a community focused on the cloud, there is even GoCloud, a library with a single, common, and high-level API that allows an application to support any of those clouds and even on-premise alternatives for those services. All can be configurable at deploy time by the infrastructure team.

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

Securing access to your Kubernetes clusters is crucial as inadequate security measures can lead to unauthorized access and potential data breaches. However, navigating the complexities of Kubernetes access security, especially when setting up strong authentication, authorization, and network policies, can be challenging.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.