AWS CodePipeline is one of the major cloud platforms used by technical support engineers to model and configure the different software release process stages. In this course, you'll examine the concept of continuous integration and its implementation in AWS CodePipeline. You'll explore the key terms and processes associated with pipeline stages, recognize how to set up continuous deployment and delivery, and outline methods for processing pipeline executions. Next, you'll learn to integrate CodePipeline with AWS CodeCommit and GitHub, configure Amazon CloudWatch Events to trigger pipelines, and set up CodePipeline to deploy various deployment groups. You'll then create pipelines to retrieve source applications from Amazon S3 Bucket and deploy them to Amazon EC2 instances. Finally, you'll configure pipelines to deploy customized product templates and use the AWS CloudFormation console service to create infrastructures.

Azure Pipelines is one of the major cloud platforms used by technical support engineers to create and manage pipelines. In this course, you'll explore the core features of Azure Pipelines and how to use them for continuous integration and delivery. You'll examine the environments that can be used to create deployment targets, the types of jobs that can be configured, the artifacts that can be utilized, and the Azure Pipelines ecosystem's components. Additionally, you'll recognize the use of pull request validation triggers, identify how to debug and resolve Azure Pipeline execution issues, and learn to create VMs for continuous deployment implementation. Finally, you'll learn how to use Azure Pipelines to clone, export, and import pipelines as well as to build GitHub repositories and images containing Dockerfiles.

Google Cloud Platform (GCP) is one of the primary services used by technical support engineers to build DevOps pipelines and implement CI/CD. In this course, you'll explore GCP's products and services and outline Google's approach to implementing continuous integration. You'll identify the benefits of using this approach in conjunction with a reference pipeline to facilitate continuous delivery. You'll also examine the different approaches to implementing CI/CD pipelines on GCP-hosted products. Next, you'll explore Google's recommendations for designing an automated deployment process, learn to configure a Cloud Build trigger, and use the Cloud Build GitHub app to build code automatically. Finally, you'll set up a CI/CD pipelines for processing data with GCP-managed products and configure continuous delivery pipelines using Google Kubernetes Engine.

Creating pipelines in AWS involves knowing how to use a series of CodePipeline action types for integration with products and services. In this course, you'll recognize the various action type integrations that facilitate AWS CodePipeline configuration. You'll identify the pipeline and stage structure requirements in AWS and list the test action integrations that can be included in pipelines and the deploy action integrations that can be used to deploy diversified applications. You'll then recognize the AWS services that can be used to configure approvals and invoke action integrations in CodePipeline and the prominent AWS service integrations not based on CodePipeline action types. Additionally, you'll create IAM users and configure IAM managed policies. You'll create two-stage pipelines and configure AWS CodeDeploy. You'll configure CodePipeline and CodeBuild to build pipelines and stages of pipelines. Finally, you'll use Step Functions invoke actions and create Lambda functions to be added as actions.

Azure Pipelines comprises various components to enable better code delivery. In this course, you'll outline the various Azure Pipelines stages and define the role of approvals and gates. You'll describe deployment conditions and triggers and the concept of queuing policies to control deployment. You'll identify the types of resources that can be used by Azure Pipelines and recognize the role of agents. You'll also outline how to translate Jenkins and Travis pipeline configurations to Azure pipelines. Next, you'll create pipelines to build a GitHub repository, configure and execute Azure Pipelines jobs, organize deployment jobs in release pipelines into stages, and configure resource triggers from different branches. You'll then change the default branch for a pipeline and create new resource groups and virtual machine scale sets. Finally, you'll install extensions to organizations and create a custom extension for Azure DevOps to place in the marketplace.

To work with DevOps pipelines, you need to recognize how pipelines are managed using different cloud platforms. In Google Cloud Platform (GCP), working with pipelines is more advanced than some of the other providers. In this course, you'll identify the benefits of GCP DevOps pipelines and the prominent GCP components and services that build them. You'll list the essential elements used to implement IoT to analytics and the benefits of using Cloud Deployment Manager to create and manage cloud resources. Next, you'll learn to create repositories to host sample app source codes, development environments from a feature branch, and GKE clusters. You'll set up a canary deployment environment and use a GCP pipeline to implement triggered deployment. You'll create Cloud Build config files to build and push Docker images to Container Registry, and configure CI/CD pipelines using GKE, Cloud Source Repositories, and Cloud Build. Finally, you'll write Dataflow pipelines and run Dataflow locally and on the cloud.

To work as a DevOps technical support engineer, you need a basic understanding of the growing number of version and source control tools. In this course, you'll explore the history of source control systems, examine how project management without source control differs from PM with source control, and recognize the control mechanisms used by Dev and Ops teams when working with control systems. You'll then identify the different types of source control systems and when to use them, and distinguish between cloud and cloudless version control systems. Next, you'll recognize the best practices for implementing version control in DevOps, and how to improve and measure the effectiveness of source control systems. Finally, you'll list the features of a source code repository and the primary source code repository providers.

While aspiring DevOps engineers may have a background in support, they often lack exposure to the standard tools used for specific development support tasks, such as source control. In this course, you'll identify the essential features and purposes of prominent source control tools and the technical support-related situations in which to avail of them. Specifically, you'll learn to install, set up, and work with Git, GitLab, Beanstalk, Apache Subversion, AWS CodeCommit, Azure DevOps Server, Concurrent Versions System, and IBM Rational Team Concert. You'll use these tools to share and manage code and repositories, collaborate and track work, and ship applications.

Release Management is much bigger than just source control and CI/CD. As an aspiring DevOps technical support engineer, it's critical to recognize the importance and function of release management and its application in the cloud. In this course, you'll explore the release management cycle and define key release management terms, metrics, and KPIs. You'll distinguish release management implementation with and without cloud and recognize best practices for designing release management processes. Next, you'll outline the DevOps lifecycle used to define release management pipelines. You'll then compare the differences between deployment and release and recognize the impact of the release management process on CI/CD. Finally, you'll explore application release automation and identify application release automation solutions for configuring an automated release process.

As part of your role as a DevOps technical support engineer, you'll need to work with popular applications to carry out release management in the cloud. In this course, you'll explore the solutions and services provided by AWS, Azure, and Google Cloud Platform to enable stable and scalable releases. You'll recognize the differences between releases on-premise and in the cloud and examine the deployment and release management workflows adopted in hybrid environments and the hybrid solution features that help deliver consistent release experiences. Next, you'll create automated continuous integration and release management workflows using AWS, plan releases using AWS CodeStar, and automate deployments to release enterprise applications. You'll configure Azure DevOps Server to automate the deployment of applications in the cloud and use the Deployment Manager to manage deployments and release cycles. Finally, you'll configure IncrediBuild Hybrid Cloud to enable automatic releases.

Before deployment, a project, application, fix, new utility, or function must be bundled into a deployable artifact. This practice is called "packaging" and is an essential part of the "DevOps toolchain." In this course, you'll explore the concept of software packaging and recognize its core characteristics and benefits. You'll identify the components of an application package, and describe the standards, patterns, processes, tools, and best practices involved in application packaging. You'll then outline the tasks involved in each packaging stage of the DevOps lifecycle. Next, you'll examine the architecture of continuous packaging and the container-based application package. You'll identify the benefits of delivering container-based and cloud-native application bundles and outline the different application package distribution approaches. Finally, you'll learn how to use standard tools for packaging open-source applications.

There are several cloud-based application packaging tools available. Knowing how to choose and use the best tool for a task will streamline the DevOps packaging life cycle. In this course, you'll examine the features and benefits of AWS Systems Manager Distributor, the packaging structure used by AWS CodeArtifact, and the application package architecture, features, and benefits afforded by Azure Batch. You'll outline the strategy for packaging applications in hybrid and multi-cloud environments and identify the architecture and key components to packaging applications for Kubernetes. Next, you'll create packages using AWS Systems Manager Distributor and create repositories with AWS CodeArtifact to store, publish, and share software packages. Finally, you'll manage application packages in an Azure Batch account, deploy container images of packaged applications to Cloud Run, and build packages with Maven artifacts using Google Cloud Storage.

An aspiring DevOps engineer needs to be familiar with the DevOps deployment landscape, not only in on-premises data centers but also in cloud-based architectures. In this course, you'll outline various deployment architectures and differentiate between traditional and DevOps-powered deployment approaches. You'll list the primary components and tools used to create custom content managed by the deployer. You'll examine the vital questions to ask when defining the most appropriate deployment strategy and describe some deployment troubleshooting techniques. You'll then list prominent cloud deployment models for deploying assets and applications and the technologies that facilitate deployments using IaaS, PaaS, and SaaS. Next, you'll recognize the components, usage, and benefits of AWS CodeDeploy, Azure Resource Manager, Azure DevOps Services, Azure DevOps Server, and GCP Cloud Deployment Manager.

DevOps engineers need to be familiar with the prominent cloud deployment tools and their combined use for optimum application deployment. In this course, you'll explore the hybrid deployment capabilities of AWS, Azure, and GCP. You'll outline how to deploy web applications using IDEs, code to EC2 virtual machines, application revisions from GitHub repositories to EC2 instances using AWS CodeDeploy, and updated source bundles to an Elastic Beanstalk environment. You'll also recognize how to implement continuous monitoring of deployment targets and set up staging environments in Azure App Service. Next, you'll describe how to implement continuous deployment from GitHub, deploy microservices in Azure App Service as a single unit, and create virtual machine instances. Finally, you'll identify how to set up deployment environments using Google Cloud Deployment Manager and use AWS Systems Manager to manage Amazon EC2 instances.

An aspiring operations DevOps engineer needs to understand where their deployments run and how "run" platforms differ from server-executed platforms. In this course, you'll recall the deployment models and software components used to manage server-based applications and middleware. You'll list the compute/run services used to provide runtime environments and manage applications. You'll explore the popular cloud provider services to set up compute environments and outline a workflow to select the appropriate compute/run service. You'll also recognize the benefits of using a hybrid cloud to manage compute/run environments for application deployment. Next, you'll distinguish compute and run platforms that are configured in on-premises, cloud, and hybrid environments, the compute/run services provided by AWS, Azure, and GCP, and the benefits of using edge cloud architecture to manage compute/run environments and workloads. Finally, you'll set up a compute/run environment using Amazon Lightsail and use the gcloud compute command-line to set up and manage Compute Engine.

A fundamental duty of a technical support engineer is to implement effective DevOps continuous monitoring. In this course, you'll identify the key components and patterns used to implement DevOps monitoring systems, the KPIs for analyzing the state of in-use IT systems, the best practices for defining effective outcomes with monitoring and alerting mechanisms, and how to monitor diversified applications. You'll also name the key components of multi-tier applications, the metrics to monitor for tracking an applications' overall performance, and prominent tools for adopting a flow monitoring strategy. Additionally, you'll identify information system security risks and outline techniques for defense in depth implementation, data-center monitoring, and security monitoring and comprehensive reporting. Finally, you'll learn to use commands to monitor server CPU, memory, disk usage, network, and load statistics.

Before carrying out DevOps monitoring, technical support engineers need to choose the most appropriate cloud-based solution. In this course, you'll recognize the QoS parameters to monitor at each cloud platform layer and the prominent monitoring architectures used to monitor components across cloud layers. You'll then name some leading cloud monitoring platforms and critical tool selection criteria, before examining Azure, GCP, and AWS monitoring services. Next, you'll configure the AWS CloudWatch Logs Agent on EC2 Linux instances and use AWS Systems Manager Quick Setup to configure Systems Manager's capabilities on these instances. You'll configure Cloud Trail to manage event logs and use Azure Monitor to monitor and collect metrics and activity logs. Lastly, you'll monitor AWS EC2 instances, Compute Engine virtual machine instances, and web servers using Google Cloud Monitoring.

Final Exam: DevOps Support Practitioner will test your knowledge and application of the topics presented throughout the DevOps Support Practitioner track of the Skillsoft Aspire Infrastructure Support Engineer to CloudOps Engineer Journey.