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Installing and Maintaining #Azure Kubernetes Cluster #AKS #ContainerInsights #AzureDevOps

Start Creating Azure Kubernetes Cluster for your Containers.

Managed Azure Kubernetes Service (AKS) makes deploying and managing containerized applications easy. It offers serverless Kubernetes, an integrated continuous integration and continuous delivery (CI/CD) experience, and enterprise-grade security and governance. As a hosted Kubernetes service, Azure handles critical tasks like health monitoring and maintenance for you. The Kubernetes masters are managed by Azure. You only manage and maintain the agent nodes. As a managed Kubernetes service, AKS is free – you only pay for the agent nodes within your clusters, not for the masters. In the following steps you can see the different ways for creating Azure Kubernetes Cluster via the Azure Portal, or via Azure Cloud Shell, or via Azure Resource Template. When the Microsoft Azure Kubernetes Cluster is running, then I will explain the different ways for deploying container workloads on AKS. When your workload is running on Azure Kubernetes Services, you also have to monitor your Container workloads with Azure Monitor Container Insights to keep in Controle. Let’s start with installing Azure Kubernetes Services (AKS)

Installing Azure Kubernetes Cluster via the Portal.

To begin you need of course a Microsoft Azure Subscription and you can start for free here

Basics information of the Azure Kubernetes Cluster

To Create the Azure Kubernetes Cluster, you have to follow these steps and type the right information in the Portal:

  1. Basics
  2. Scale
  3. Authentication
  4. Networking
  5. Monitoring
  6. Tags
  7. Review + Create

At the basics screen you select the right Azure Subscription and the Resource Group. You can create a New Resource Group or one you already made.
At Cluster details, you give your Cluster a name and select the Kubernetes version.

Here you select the Kubernetes Node size for your Container workload and the number of nodes.
You can start a Cluster already with One node, but choose to start with the right size for your workloads.
When you click on Change size, you can choose your nodes to do the job. 😉

Select the right Size node

Then we go to step 2 and that is Scale.

2. Scale options in Azure Kubernetes Cluster

Here you have two options :

  1. Virtual Nodes
  2. VM Scale sets (Preview)

To quickly deploy workloads in an Azure Kubernetes Service (AKS) cluster, you can use virtual nodes. With virtual nodes, you have fast provisioning of pods, and only pay per second for their execution time. In a scaling scenario, you don’t need to wait for the Kubernetes cluster autoscaler to deploy VM compute nodes to run the additional pods. Virtual nodes are only supported with Linux pods and nodes. More information here about Virtual Nodes

To create an AKS cluster that can use multiple node pools, first enable two feature flags on your subscription. Multi-node pool clusters use a virtual machine scale set (VMSS) to manage the deployment and configuration of the Kubernetes nodes. With this Preview feature you can run Linux Containers and Windows Containers on the same Cluster. More information here about VM Scale sets (Preview)

3, Authentication

The service principal is needed to dynamically create and manage other Azure resources such as an Azure load balancer or container registry (ACR). To interact with Azure APIs, an AKS cluster requires an Azure Active Directory (AD) service principal. More information about the Service Principal can be found here

Azure Kubernetes Service (AKS) can be configured to use Azure Active Directory (Azure AD) for user authentication. In this configuration, you can sign in to an AKS cluster by using your Azure AD authentication token.
Cluster administrators can configure Kubernetes role-based access control (RBAC) based on a user’s identity or directory group membership. More information about RBAC for AKS

4. Networking

Configuring the virtual Networks for your Azure Kubernetes Cluster is important for the right IP range but later on also for the Network Security Groups (NSG).

Here you see an example of the Kubernetes NSG which is connected to the Internet by Default after installation, you can deep dive into security but be careful which settings you do here because Microsoft resources must have access to service the Azure Kubernetes Cluster.

NSG created after installation is finished

NSG Rule set Inbound and outbound

In a container-based microservices approach to application development, application components must work together to process their tasks. Kubernetes provides various resources that enable this application communication. You can connect to and expose applications internally or externally. To build highly available applications, you can load balance your applications. More complex applications may require configuration of ingress traffic for SSL/TLS termination or routing of multiple components. For security reasons, you may also need to restrict the flow of network traffic into or between pods and nodes.

Best practices for network connectivity and security in Azure Kubernetes Service (AKS):

Here is more information about networking and Security for AKS

5. Monitoring

Keep Azure Monitoring Enabled and Connect to your Log Analytics workspace or create a new workspace for Container monitoring of your Azure Kubernetes Cluster.

Azure Monitor for containers is a feature designed to monitor the performance of container workloads deployed to either Azure Container Instances or managed Kubernetes clusters hosted on Azure Kubernetes Service (AKS). Monitoring your containers is critical, especially when you’re running a production cluster, at scale, with multiple applications.

Azure Monitor for containers gives you performance visibility by collecting memory and processor metrics from controllers, nodes, and containers that are available in Kubernetes through the Metrics API. Container logs are also collected. After you enable monitoring from Kubernetes clusters, metrics and logs are automatically collected for you through a containerized version of the Log Analytics agent for Linux. Metrics are written to the metrics store and log data is written to the logs store associated with your Log Analytics workspace.

6. Tags

When you build more Azure Kubernetes Clusters for different departments or teams you can TAG your Clusters for organizing your billing and security for example. Here you find more information about tagging.

After this you click on the last step Review and Create
The Azure portal will do a validation of your Azure Kubernetes Cluster settings, and when it’s validated you hit Create. But when you want more Automation, you can download the JSON ARM template first and use that.

Installing Azure Kubernetes Cluster via Cloud Shell

Azure Cloud Shell AKS CLI

Azure hosts Azure Cloud Shell, an interactive shell environment that you can use through your browser. Cloud Shell lets you use either bash or PowerShell to work with Azure services. You can use the Cloud Shell pre-installed commands to run the code in this article without having to install anything on your local environment.

Here you see an Example of AKS CLI with Auto Scaler with max count of nodes 😉

Installing Azure Kubernetes Cluster via Template

Create Azure Kubernetes Cluster via Template in the Portal

Here you find an Example at GitHub for a Template deployment

Now you have your Microsoft Azure Kubernetes Cluster (AKS) running in the Cloud, you want to deploy your Container workloads on the Cluster. In the following steps you see different deployments.

Deploy Container workload with Azure DevOps Project

Deployment Center

First you select your repository where your source code is of your workload.

Set the information right and click Next.

Simple example Click Next

Create a Container Registry.

Building Pipeline with Azure DevOps.

Here you see the Building in Microsoft Azure DevOps.

Build, test, and deploy in any language, to any cloud—or on-premises. Run in parallel on Linux, macOS, and Windows, and deploy containers to individual hosts or Kubernetes.

Here you find all the information about Microsoft Azure DevOps for your workloads, code and Deployments.

Deploying Container workload completed with Azure DevOps.

 

Deploy Container Workloads via Visual Studio Code

When you download and install Visual Studio Code on your computer, you can install the Azure Kubernetes extension for VSCode.

Install Kubernetes extension for VSCode

VSCode with Kubernetes Extension

Here you see Microsoft Visual Studio Code connected with my Azure subscription where my Azure Kubernetes Cluster is running. With the standard Helm Repository packages for deployment to your AKS Cluster. Here you see a WordPress yaml file which I deployed to the Kubernetes Cluster on Azure.

Just Select your Package and Install on Azure Kubernetes.

From here you can into the Container and read the logs.

I’m using Visual Studio Code a lot for Azure Kubernetes but also for Docker Containers and images.
Making Azure ARM JSON templates and this great for Infrastructure as Code.

 

Azure Monitoring with Container Insights

In One Dashboard you can see the Status of all your Clusters

 

Azure Monitor Container Insights Live View

Because we installed Azure Monitor for Containers on the Microsoft Azure Kubernetes Cluster, we can live see what is happening inside the Kubernetes Cluster with the containers. This is a great feature when you have a issue with a Container for troubleshooting fast and see what is happening.

Conclusion

Microsoft Azure Kubernetes Cluster is fast and easy to manage. You can upgrade your Cluster without downtime of your Container workload. With Azure Monitor for Containers you can see what’s happening inside the container and you can set alerts when something went wrong. This keeps you in Controle of the solution. With Deployment center alias Azure DevOps Projects you can deploy your workload via Azure DevOps Pipeline and work on versioning, testplans, Azure DevOps repo and work together with a Team on the following releases. Working with Azure Kubernetes Multi node pools with Linux and Windows on the same Cluster is possible. Try it yourself and start with a Proof of Concept for your Business.

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#Azure IoT Pipeline with Microsoft #AzureDevOps Project #IoT #Code #Apps #SmartCities

Azure IoT Edge – Hub with Azure DevOps Pipeline

Configure continuous integration (CI) and continuous delivery (CD) for your IoT Edge application with DevOps Projects. DevOps Projects simplifies the initial configuration of a build and release pipeline in Azure Pipelines.

In the following steps you can see how easy it is to build your Continuous integration and continuous deployment to Azure IoT Edge with DevOps Project :

Select Simple IoT

Click on Next.

From here you set your Azure DevOps organization to your Azure IoT Hub. Click on additional settings

In additional settings you can set :

  • Azure Resource Group
  • Location ( region)
  • Container Registry
  • Container Registry name
  • Container registry SKU
  • Container Location
  • IoT Hub of Edge Devices
  • IoT Hub Location

Select Container Registry Plan

Azure Container Registry allows you to store images for all types of container deployments including DC/OS, Docker Swarm, Kubernetes, and Azure services such as App Service, Batch, Service Fabric, and others. Your DevOps team can manage the configuration of apps isolated from the configuration of the hosting environment.
More information about Azure Container Registry and pricing

Azure DevOps Project will do the rest of the deployment.

Of course Infrastructure as Code (IaC) is possible by ARM JSON Template.

Save the template for later.

here you got your ARM Templates.

Later you will see when you complete the deployment, that your JSON ARM template is in Azure DevOps Repo.
You can connect your Azure DevOps Repo via the portal but also via Visual Studio and Visual Studio Code.

The resources coming into myiotpipeline-rg

MyIOTPipeline-IoTHub is created.

MyIOTPipelineACR Container Registry is created.

MyIOTPipeline with Azure DevOps is created 🙂

Your Continuous integration and continuous deployment to Azure IoT Edge is deployed and active. Now you have your Azure Pipeline in place to continuously update your IoT Device App. From here you can go to Azure DevOps Project Homepage.

Via Agent phase you can see all the jobs of the deployment.

Azure DevOps Pipeline Release

here we have Azure DevOps Repos

Azure DevOps Services includes free unlimited private Git repos, so Azure Repos is easy to try out. Git is the most commonly used version control system today and is quickly becoming the standard for version control. Git is a distributed version control system, meaning that your local copy of code is a complete version control repository. These fully functional local repositories make it easy to work offline or remotely. You commit your work locally, and then sync your copy of the repository with the copy on the server.
Git in Azure Repos is standard Git. You can use the clients and tools of your choice, such as Git for Windows, Mac, partners’ Git services, and tools such as Visual Studio and Visual Studio Code.

All the Azure Resources for the IoT Edge Pipeline with Azure DevOps.

When you have your Azure DevOps Pipeline with IoT Edge devices running, you can monitor your pipeline with Analytics inside Azure DevOps.

Click Next.

Click on Install Analytics.

Select the right Azure DevOps Organization for your IoT Edge Pipeline.

Done !

 

Analytics is now active, you can make automated test plans in Azure DevOps and see the results via Analytics.

Azure DevOps Overview Dashboard.

There are a lot of predefined Analytics Views for you shared.

An Analytics view provides a simplified way to specify the filter criteria for a Power BI report based on the Analytics Service data store. The Analytics Service provides the reporting platform for Azure DevOps.
More information about Analytics in Azure DevOps here

Easy to start with Powerbi and Azure DevOps Connector.

Planned manual testing
Plan, execute, and track scripted tests with actionable defects and end-to-end traceability. Assess quality throughout the development lifecycle by testing your desktop or web applications.

More information about making your testplan for your IoT Edge Devices Azure DevOps Pipeline

Conclusion :

When you connect Microsoft Azure IoT Edge – HUB with your Internet of Things Devices and combine it with Microsoft Azure DevOps Team to develop your Azure IoT Pipeline, then you are in fully control of Continuous integration and continuous deployment to Azure IoT Edge. From here you can make your innovations and Intelligent Cloud & Edge with Artificial Intelligence and Machine Learning to your Devices. You will see that this combination will be Awesome for HealthCare, Smart Cities, Smart Buildings, Infrastructure, and the Tech Industry.

In this Microsoft article, you learn how to use the built-in Azure IoT Edge tasks for Azure Pipelines to create two pipelines for your IoT Edge solution. The first takes your code and builds the solution, pushing your module images to your container registry and creating a deployment manifest. The second deploys your modules to targeted IoT Edge devices.

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Join Containers in the Cloud Community on LinkedIn

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#Microsoft #AzureDevOps – Azure Pipelines, #Azure Boards + GitHub with @AbelSquidHead #LoECDA

Azure DevOps for CI/CD

Azure DevOps Services is a cloud service for collaborating on code development. It provides an integrated set of features that you access through your web browser or IDE client. The features are included, as follows:

  • Git repositories for source control of your code
  • Build and release services to support continuous integration and delivery of your apps
  • Agile tools to support planning and tracking your work, code defects, and issues using Kanban and Scrum methods
  • Many tools to test your apps, including manual/exploratory testing, load testing, and continuous testing
  • Highly customizable dashboards for sharing progress and trends
  • Built-in wiki for sharing information with your team

The Azure DevOps ecosystem also provides support for adding extensions and integrating with other popular services, such as: Campfire, Slack, Trello, UserVoice, and more, and developing your own custom extensions.

Start your CI/CD Pipelines Today with Azure DevOps

More information about Microsoft Azure DevOps :

Microsoft Azure DevOps Docs

Azure DevOps Community Group on LinkedIn

Azure DevOps PODCAST

and stay up-to-date on Azure DevOps via Twitter :

The #LoECDA Team

@AzureDevOps

@DonovanBrown

@AbelSquidHead

@jldeen

@damovisa

@StevenMurawski


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Installation of #AzureDevOps Server 2019 RC1 for your Team Work #DevOps #Winserv

What is Azure DevOps Server?

Collaborative software development tools for the entire team

Previously known as Team Foundation Server (TFS), Azure DevOps Server is a set of collaborative software development tools, hosted on-premises. Azure DevOps Server integrates with your existing IDE or editor, enabling your cross-functional team to work effectively on projects of all sizes.

In the following Step-by-Step Guide we will install Microsoft Azure DevOps Server 2019 RC1

 

Start the Wizard to Configure the Azure DevOps Server

Choose your Deployment Type

Choose your Scenario

Select your language

Here you can choose for your SQL Backend

Click on edit for your Site settings of Azure DevOps

Click on Next to complete

Your Microsoft Azure DevOps Windows Server 2019 RC1 is running for your Team.

Azure DevOps Community Project 😉

Here you can do your settings, like in Azure DevOps.

Azure DevOps Server Administration Console

The installation of Microsoft Azure DevOps Windows Server 2019 RC is straight forward and Great for On-premises when you can’t use Internet.

Here you find more information on Microsoft Docs to get Started Today for your Business

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via @MSAzureCAT Enterprise #Cloud Control Plane Planning #AzureDevOps #Pipelines

End-to-end Pipelines for Automating Microsoft Azure Deployments

 

Overview :

Imagine a fully automated, end-to-end pipeline for your cloud deployments—one that encompasses and automates everything:

• Source code repos.
• The build and release iterations.
• Agile processes supported by continuous integration and continuous deployment (CI/CD)
• Security and governance.
• Business unit chargebacks.
• Support and maintenance.

Azure services and infrastructure-as-code (IaC) make control plane automation very achievable. Many enterprise IT groups dream of creating or unifying their disparate automation processes and supporting a common, enterprise-wide datacenter control plane in the cloud that is integrated with their existing or new DevOps workflows. Their development environments may use Jenkins, Azure DevOps Services (formerly Visual Studio Team Services), Visual Studio Team Foundation Server (TFS), Atlassian, or other services. The challenge is to automate beyond the CI/CD pipeline to the management and policy layers. From a planning and architecture standpoint, it can seem like an overwhelming program of interdependent systems and processes. This guide outlines a planning process that you can use for automated support of your cloud deployments and DevOps workflows beyond the CI/CD pipeline. The Azure platform provides services you can use, or you can choose to work with third-party or open source options. The process is based on real-world examples that we have deployed with enterprise customers on Azure.

This whitepaper was authored by Tim Ehlen. It was edited by Nanette Ray. It was reviewed by AzureCAT.

Download the Awesome eBook here on the AzureCAT Team Blog

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Using #Azure Pipelines for your Open Source Project #AzureDevOps

Azure Pipelines for your Open Source Projects

Damian speaks to Edward Thomson about how to get started with Azure Pipelines – right from GitHub. The deep integration and GitHub Marketplace app for Azure Pipelines makes it incredibly easy to build your projects no matter what language you’re using. You can even use the builds as part of your PR checks!

https://github.com/marketplace/azure-pipelines

Edward shows us the incredible (free!) offers for open and closed source projects, and walks through creating and running a new Azure Pipelines build from scratch in only a few minutes.

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NEW via #MSFTConnect 2017 Microsoft #Azure Databrick

Today at Microsoft Connect(); we introduced Azure Databricks, an exciting new service in preview that brings together the best of the Apache Spark analytics platform and Azure cloud. As a close partnership between Databricks and Microsoft, Azure Databricks brings unique benefits not present in other cloud platforms. This blog post introduces the technology and new capabilities available for data scientists, data engineers, and business decision-makers using the power of Databricks on Azure.

Azure Databricks Preview

Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.

Read more on Microsoft Docs what Microsoft Azure Databrick is

Quickstart: Get started with Azure Databricks using the Azure portal :

This quickstart shows how to create an Azure Databricks workspace and an Apache Spark cluster within that workspace. Finally, you learn how to run a Spark job on the Databricks cluster.

Creating Clusters

In Databricks, you can create two different types of resources:
Standard Clusters: Databricks’ standard clusters have lot of configuration options to customize and fine tune your Spark jobs. You can learn more about standard clusters below.
Serverless Pools (BETA): With serverless pools, Databricks’ auto-manages all the resources and you just need to provide the range of instances required for the pool. Serverless pools support only Python and SQL. Serverless pools also auto-configures the resources with right Spark configuration. Visit Serverless Pools to know more about them.

Read more on the Microsoft Azure Blog here:  A Technical Overview of Azure Databricks after Microsoft Connect() 2017.