Containers are being used by more and more organizations to automate build pipelines in their CICD processes. In this article, Mahendran Purushothaman shows how to automate the creation of Docker containers and a Kubernetes cluster in Azure.
In the past decade, the software development processes and application and infrastructure architectures and technologies have gone through many innovations and changes. Organizations that started with on-premises data centers have moved to hardware virtualization, private and public clouds, containers, and now serverless applications. With this transition, many organizations are moving away from the monolithic architectures to microservices and serverless models. This article focuses on running container workloads in Microsoft Azure.
After hardware virtualization became commonplace, the question was raised about how much faster and more efficient application development and deployment could be made. The container was an obvious solution. A container is a virtualization on top of the operating system layer. Containers do not need to boot up another operating system to run an application. All you need is your application code and its dependent libraries packaged into a single image. The important advantage of containers is that you do not need to boot up another operating system with all the software packages needed for your application on top of the host machine.
Figure 1 shows the virtualization of containers:
#containerization #devops #homepage #automate
Earlier this month Docker announced our partnership with Microsoft to shorten the developer commute between the desktop and running containers in the cloud. We are excited to announce the first release of the new Docker Azure Container Instances (ACI) experience today and wanted to give you an overview of how you can get started using it.
The new Docker and Microsoft ACI experience allows developers to easily move between working locally and in the Cloud with ACI; using the same Docker CLI experience used today! We have done this by expanding the existing
docker context command to now support ACI as a new backend. We worked with Microsoft to target ACI as we felt its performance and ‘zero cost when nothing is running’ made it a great place to jump into running containers in the cloud.
ACI is a Microsoft serverless container solution for running a single Docker container or a service composed of a group of multiple containers defined with a Docker Compose file. Developers can run their containers in the cloud without needing to set up any infrastructure and take advantage of features such as mounting Azure Storage and GitHub repositories as volumes. For production cases, you can leverage Docker commands inside of an automated CI/CD flow.
Thanks to this new ACI context, you can now easily run a single container in Microsoft ACI using the
docker run command but also multi-container applications using the
docker compose up command.
This new experience is now available as part of Docker Desktop Edge 2.3.2 . To get started, simply download the latest Edge release or update if you are already on Desktop Edge.
Once you have the latest version, you will need to get started by logging into an Azure account. If you don’t have one you can sign up for one with $200 of credit for 30 days to try out the experience here. Once you have an account you can get started in the Docker CLI by logging into Azure:
This will load up the Azure authentication page allowing you to login using your credentials and Multi-Factor Authentication (MFA). Once you have authenticated you will see a
login succeeded in the CLI, you are now ready to create your first ACI context. To do this you will need to use the
docker context create aci command. You can either pass in an Azure subscription and resource group to the command or use the interactive CLI to choose them, or even create a resource group. For this example I will deploy to my default Resource Group.
My context is then created and I can check this using
docker context ls
Before I use this context, I am now going to test my application locally to check everything is working as expected. I am just going to use a very simple web server with a static HTML web page on.
I start by building my image and then running it locally:
Getting ready to run my container on ACI, I now push my image to Dockerhub using
docker push bengotch/simplewhale and then change my context using
docker context use myacicontext. From that moment, all the subsequent commands we will execute will be run against this ACI context.
#products #azure #docker-desktop #microsoft #microsoft azure #partnership
K-means is one of the simplest unsupervised machine learning algorithms that solve the well-known data clustering problem. Clustering is one of the most common data analysis tasks used to get an intuition about data structure. It is defined as finding the subgroups in the data such that each data points in different clusters are very different. We are trying to find the homogeneous subgroups within the data. Each group’s data points are similarly based on similarity metrics like a Euclidean-based distance or correlation-based distance.
The algorithm can do clustering analysis based on features or samples. We try to find the subcategory of sampling based on attributes or try to find the subcategory of parts based on samples. The practical applications of such a procedure are many: the best use of clustering in amazon and Netflix recommended system, given a medical image of a group of cells, a clustering algorithm could aid in identifying the centers of the cells; looking at the GPS data of a user’s mobile device, their more frequently visited locations within a certain radius can be revealed; for any set of unlabeled observations, clustering helps establish the existence of some structure of data that might indicate that the data is separable.
K-means the clustering algorithm whose primary goal is to group similar elements or data points into a cluster.
K in k-means represents the number of clusters.
A cluster refers to a collection of data points aggregated together because of certain similarities.
K-means clustering is an iterative algorithm that starts with k random numbers used as mean values to define clusters. Data points belong to the group represented by the mean value to which they are closest. This mean value co-ordinates called the centroid.
Iteratively, the mean value of each cluster’s data points is computed, and the new mean values are used to restart the process till the mean stops changing. The disadvantage of k-means is that it a local search procedure and could miss global patterns.
The k initial centroids can be randomly selected. Another approach of determining k is to compute the entire dataset’s mean and add _k _random co-ordinates to it to make k initial points. Another method is to determine the principal component of the data and divide it into _k _equal partitions. The mean of each section can be used as initial centroids.
#ad #microsoft #microsoft-azure #azure #azure-functions #azure-security
During the recent Ignite virtual conference, Microsoft announced several updates for their Azure multi-cloud and edge hybrid offerings. These updates span from security innovations to new edge capabilities.
From its inception onward, Microsoft Azure has been hybrid by design, providing customers with services that allow ground to cloud and cloud to ground shifts of workloads. Moreover, Microsoft keeps expanding its cloud platform hybrid capabilities to allow customers to run their apps anywhere across on-premises, multi-cloud, and the edge. At Ignite, the public cloud vendor announced several innovations for Azure Arc, Stack, VMWare and Sphere.
At Ignite last year, Microsoft launched Azure Arc, a service allowing enterprises to bring Azure services and management to any infrastructure, including AWS and Google Cloud. This service was an addition to Microsoft’s Azure Hybrid portfolio, which also includes Azure Stack and Edge. Later in 2020, the service received an update with support for Kubernetes. Now Azure Arc has more capabilities with the new Azure Arc enabled data services in preview. Furthermore, the Azure Arc enabled servers are now generally available.
#amazon #microsoft azure #cloud #iaas #kubernetes #iot #edge #google #azure #edge computing #microsoft #hybrid cloud #deployment #aws #containers #devops #architecture & design #development #news
It’s one of the leaders in the cloud computing space, but what is Azure cloud and what is it used for? This ACG Fundamentals episode will give you a high-level overview of Microsoft Azure cloud, so you can understand this cloud computing platform’s strengths and weaknesses, use cases, market share and competition, and how the Azure services all work together.
Azure Infrastructure (1:07)
Azure Competitors (3:43)
Azure Strengths and Weaknesses (4:18)
Azure Use Cases (6:12)
What’s Next? (7:39)
#microsoft azure #azure #what is microsoft azure?
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