snyk VS kubernetes

Snyk CLI was introduced to the World Wide Web and security enthusiasts on October 2, 2015, as v0.0.0-pre-alpha release. In the past eight years, we released Snyk CLI nearly two thousand times — and more than eleven hundred of those releases happened in the last three years. That’s one release every thirty-two hours, signifying our customers’ growing needs as well as the pace at which we operate to meet those needs at an enterprise scale. With increasing demand, the complexity, reach, and impact of our fast-paced code changes increased, too.

There are a number of ways that you can install the Snyk CLI on your machine, ranging from using the available stand-alone executables to using package managers such as Homebrew for macOS and Scoop for Windows.

There's tons of tools to solve each of these problems Snyk for vulnerability scanning, tons of license checker plugins (like we use license-webpack-plugin which generates the license text for everything we distribute and fails a build if a license doesn't have one of our allowlisted licenses.

oh, such companies already exist: For example Snyk

Snyk provides security score and vulnerability count badges, which you can link to the relevant pages, as in these examples:

If you app is dockerized I would recommend adding something like Snyk to make sure your image is safe.

Note: I remind you that we support multiple strategy for vulnerabilities like Sonatype or Snyk.

My name is Adam, and I am a software engineer working at Snyk for the past 2.5 years. Over the past year, I have been leading a few projects that spanned multiple teams. My colleague is a tech lead at Snyk, and he’s been coaching people on how to lead projects effectively for a few years now.

So I'm curious, how are businesses building iOS apps securely? Could a tool like Snyk replace a manual audit, or is it a good idea to have an initial manual audit of our desired environment?

Ideally, software can quickly go from development to production. Continuous deployment and delivery are some processes that make this possible. Continuous deployment means establishing an automated pipeline from development to production while continuous delivery means maintaining the main branch in a deployable state so that a deployment can be requested at any time. Predecos uses these tools. When a commit goes into master, the code is pushed directly to the public environment. Deployment also occurs when a push is made to a development branch enabling local/e2e testing before push to master. In this manner the master branch can be kept clean and ready for deployment most of the time. Problems that surface resulting from changes are visible before reaching master. Additional automated tools are used. Docker images are built for each microservice on commit to a development or master branch, a static code analysis is performed by SonarCloud revealing quality and security problems, Snyk provides vulnerability analysis and CodeClimate provides feedback on code quality while Coveralls provides test coverage. Finally, a CircleCI build is done. Each of these components use badges which give a heads-up display of the health of the system being developed. Incorporating each of these tools into the development process will keep the code on a trajectory of stability. For example, eliminating code smells, security vulnerabilities, and broken tests before merging a pull-request (PR) into master. Using Husky on development machines to ensure that code is well linted and locally tested before it is allowed to be pushed to source-control management (SCM). Applying additional processes such as writing tests around bugs meaning reintroduction of a given bug would cause a test to fail. The automated tools would then require that test to be fixed before push to SCM meaning fewer bugs will be reintroduced. Proper development processes and automation have a strong synergy.

In the field of software development, efficiency and agility are always sought after. In the era of cloud-native apps, traditional deployment techniques—which are frequently laborious and prone to errors—are starting to become obstacles. This is when Kubernetes and GitOps come in handy.

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.