Back in May, I participated in IBM Think Digital 2020 with my colleague Filipe Miranda to present a Master Class titled “Modernizing Your Infrastructure with Kubernetes and ICPA on IBM Z.”
In this 93-minute video, I kick things off by giving a quick tour of IBM Z and then dive into current options available for Kubernetes on Linux on IBM Z and LinuxONE, including Kubic from SUSE, Canonical distribution of Kubernetes, Red Hat OpenShift, and other deployments supported by IBM partners and community. I wrap up by demonstrating how you’d run a simple nginx deployment from a Dockerfile on OpenShift running on IBM Z.
#containers #ibm linuxone #ibm z #kubernetes #systems
Last year, we provided a list of Kubernetes tools that proved so popular we have decided to curate another list of some useful additions for working with the platform—among which are many tools that we personally use here at Caylent. Check out the original tools list here in case you missed it.
According to a recent survey done by Stackrox, the dominance Kubernetes enjoys in the market continues to be reinforced, with 86% of respondents using it for container orchestration.
And as you can see below, more and more companies are jumping into containerization for their apps. If you’re among them, here are some tools to aid you going forward as Kubernetes continues its rapid growth.
#blog #tools #amazon elastic kubernetes service #application security #aws kms #botkube #caylent #cli #container monitoring #container orchestration tools #container security #containers #continuous delivery #continuous deployment #continuous integration #contour #developers #development #developments #draft #eksctl #firewall #gcp #github #harbor #helm #helm charts #helm-2to3 #helm-aws-secret-plugin #helm-docs #helm-operator-get-started #helm-secrets #iam #json #k-rail #k3s #k3sup #k8s #keel.sh #keycloak #kiali #kiam #klum #knative #krew #ksniff #kube #kube-prod-runtime #kube-ps1 #kube-scan #kube-state-metrics #kube2iam #kubeapps #kubebuilder #kubeconfig #kubectl #kubectl-aws-secrets #kubefwd #kubernetes #kubernetes command line tool #kubernetes configuration #kubernetes deployment #kubernetes in development #kubernetes in production #kubernetes ingress #kubernetes interfaces #kubernetes monitoring #kubernetes networking #kubernetes observability #kubernetes plugins #kubernetes secrets #kubernetes security #kubernetes security best practices #kubernetes security vendors #kubernetes service discovery #kubernetic #kubesec #kubeterminal #kubeval #kudo #kuma #microsoft azure key vault #mozilla sops #octant #octarine #open source #palo alto kubernetes security #permission-manager #pgp #rafay #rakess #rancher #rook #secrets operations #serverless function #service mesh #shell-operator #snyk #snyk container #sonobuoy #strongdm #tcpdump #tenkai #testing #tigera #tilt #vert.x #wireshark #yaml
Kubernetes is a highly popular container orchestration platform. Multi cloud is a strategy that leverages cloud resources from multiple vendors. Multi cloud strategies have become popular because they help prevent vendor lock-in and enable you to leverage a wide variety of cloud resources. However, multi cloud ecosystems are notoriously difficult to configure and maintain.
This article explains how you can leverage Kubernetes to reduce multi cloud complexities and improve stability, scalability, and velocity.
Maintaining standardized application deployments becomes more challenging as your number of applications and the technologies they are based on increase. As environments, operating systems, and dependencies differ, management and operations require more effort and extensive documentation.
In the past, teams tried to get around these difficulties by creating isolated projects in the data center. Each project, including its configurations and requirements were managed independently. This required accurately predicting performance and the number of users before deployment and taking down applications to update operating systems or applications. There were many chances for error.
Kubernetes can provide an alternative to the old method, enabling teams to deploy applications independent of the environment in containers. This eliminates the need to create resource partitions and enables teams to operate infrastructure as a unified whole.
In particular, Kubernetes makes it easier to deploy a multi cloud strategy since it enables you to abstract away service differences. With Kubernetes deployments you can work from a consistent platform and optimize services and applications according to your business needs.
The Compelling Attributes of Multi Cloud Kubernetes
Multi cloud Kubernetes can provide multiple benefits beyond a single cloud deployment. Below are some of the most notable advantages.
In addition to the built-in scalability, fault tolerance, and auto-healing features of Kubernetes, multi cloud deployments can provide service redundancy. For example, you can mirror applications or split microservices across vendors. This reduces the risk of a vendor-related outage and enables you to create failovers.
#kubernetes #multicloud-strategy #kubernetes-cluster #kubernetes-top-story #kubernetes-cluster-install #kubernetes-explained #kubernetes-infrastructure #cloud
Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.
Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.
“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”
We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.
We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.
Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use
ast module, and wide adoption of the language itself.
Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:
As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:
The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.
A token might consist of either a single character, like
(, or literals (like integers, strings, e.g.,
Bob, etc.), or reserved keywords of that language (e.g,
def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.
Python provides the
tokenize module in its standard library to let you play around with tokens:
code = b"color = input('Enter your favourite color: ')"
for token in tokenize.tokenize(io.BytesIO(code).readline):
TokenInfo(type=62 (ENCODING), string='utf-8')
TokenInfo(type=1 (NAME), string='color')
TokenInfo(type=54 (OP), string='=')
TokenInfo(type=1 (NAME), string='input')
TokenInfo(type=54 (OP), string='(')
TokenInfo(type=3 (STRING), string="'Enter your favourite color: '")
TokenInfo(type=54 (OP), string=')')
TokenInfo(type=4 (NEWLINE), string='')
TokenInfo(type=0 (ENDMARKER), string='')
(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)
#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer
The gaming industry has taken a boom in the last few years. If we talk about numbers, according to NewZoo, the worth of the video gaming industry was $159.3 Billion in 2020. Video games are not just something for fun now, players and users expect much more from video game developers. Creating such products that just do not satisfy the player’s needs and exceed their expectations is what video game development company are thriving for.
Though kickstarting a new game-making studio is not an easy task. This business requires a team with a huge passion to create games and earn money from these video games. The idea of the approach is to create such unique games that will reach millions of people in the world and gain popularity. This growth demands more professionals in this field.
This just can not be obtained by finding someone with a good CV, the whole process includes a deep dig down to grab the right talent. Read on to learn more about Mobile game developers and the process of hiring video game developers.
Read Complete Blog Here - https://theninehertz.com/blog/how-to-hire-video-game-developers-video-game-development
#Video Game Development
#Video Game developers
#Video game development studio
#Video game development services
Infrastructure as Code has been the hottest trend in cloud-native application development in recent years. By transforming infrastructure management into simple coded runtimes and routines, Infrastructure as Code or IaC allows developers to be more involved in the deployment part of their CI/CD pipelines. Even the most complex cloud infrastructure can be created with several lines of code.
IaC also means that server management, resource provisioning, and even long-term maintenance of complex cloud infrastructures are entirely simplified. Tools like Terraform certainly make maintaining a production environment that is both capable and efficient easy, even when there is no dedicated infrastructure team to handle the associated tasks.
A new trend that we’re seeing right now is further simplification of IaC, mainly known as Infrastructure as Software or IaS. Now that cloud services and the providers behind them are easier to access and control using tools and software, it is not impossible for the entire cloud infrastructure to be provisioned and managed as software libraries.
How does Infrastructure as Code differ from Infrastructure as Software? Which approach is better? We are going to answer these questions, and several others about these two trends, in this article.
The two approaches have some stark differences, but we are going to take a closer look at each of them first before we start differentiating the two. Infrastructure as Code is obviously the older approach of the two, and it has been very popular among developers. Using tools designed for managing infrastructure through lines of code, you can either manage the configurations of your cloud infrastructure or manage the provisioning of cloud resources; or both.
Terraform, a popular tool used by millions of developers, applies the second approach. The tool is not just handy for managing multiple configurations and making sure that key infrastructure variables are coded properly; it is also capable of provisioning resources and automating server deployment as needed. Terraform is very extensive in this respect.
Upon close inspection, Infrastructure as Software performs similar—if not the same—tasks using similar tools. You can deploy new server instances or configure the entire architecture using a few lines of codes. You can also automate provisioning and management, and you can still integrate IaS with your existing CI/CD pipelines.
Services that are available today support both approaches in most cases. The tools that fall into these two categories basically use the same API calls and available cloud resources to perform their runtimes, but they take different approaches when it comes to management. That actually brings us to our next point.
Now that we know how the two approaches are relatively similar, it is time to get the obvious out of the way. Infrastructure as Code and Infrastructure as Software has one huge difference, and that difference lies in the programming languages used by the tools. The easiest way to understand this difference is by comparing Terraform with Pulumi, which is a popular IaS tool.
Terraform requires you to use its native programming language. The HCL language is used for low-level programming. While the language is also used by other tools, the way it is used by Terraform is not always as straightforward as it seems. Terraform also supports JSON syntax but parsing and generating can quickly become bottlenecks as you try to organize massive cloud infrastructure environments.
Since the programming language being used carries its own best practices and things like package management, you can implement the same set of elements into your IaS routine. No need to worry about having difficulties pushing infrastructure modules or doing plenty of adjustments in order for the configuration to be deployed at all.
#blog #code #continuous delivery #continuous integration #ci/cd pipeline #infrastructure as code #infrastructure as software #pulumi #terraform