Infrastructure Support Engineer to CloudOps Engineer

Most organisations have a mix of legacy and advanced systems. In this course, you'll explore how you can avoid the trap of legacy system architecture with DevOps and CloudOps. You'll learn the strategies, best practices, and benefits of modernizing or migrating legacy system architectures to multi-cloud architecture. Next, you'll look at different types of failover that can be adopted to manage system failures and the organizational structure that can be adopted to support mature multi-cloud deployments. You'll move on to learn about the elements of support systems and tools that enable robust technical and operational support systems. Finally, you'll examine how to use Git for issue tracking and Freshdesk for implementing a support ticketing system.

Support engineers commonly leverage cloud services like AWS, Azure, and GCP to manage resource configuration. In this course, you'll explore cloud infrastructure components, common cloud service models, and the technologies behind cloud computing. You'll learn about the logical architecture of AWS and critical services provided by Azure and Google Cloud Platform. Next, you'll examine a comparison of services provided by IBM, VMware, and Kamatera cloud. You'll then move on to look at tools that can be used to manage multi-cloud environments and the concepts of automation and configuration management in CloudOps. Next, you'll learn about the support levels provided by cloud service providers used to enable the shared responsibility support model. Finally, you'll learn to work with new EC2 runtime commands to manage remote EC2 instances, as well as common Azure and Google Cloud Platform commands to manage Azure and Google Cloud Platform resource configuration.

There are key DevOps practices that the support engineer may adopt to enable end-to-end DevOps management, including best practices, patterns, and workflows. In this course, you'll explore the role of metrics, monitoring, and alerting in managing the state of infrastructures and systems. You'll learn about the best practices for monitoring systems and infrastructures, as well as deployment patterns for building reusable applications and services. Next, you'll examine the benefits of automating configuration management and the continuous integration deployment workflow for DevOps workflows management. Finally, you'll learn to configure GitLab to implement continuous integration.

There are a range of DevOps tools available to implement end-to-end DevOps processes and principles. In this course, you'll explore the products and tools that can be used to manage code versioning, builds, configuration management, integration, and monitoring. You'll learn about containerization, actions that can be performed during downtime, and considerations for creating downtime. Next, you'll look at how to work with prominent DevOps tools like Git, Gradle, Jenkins, Kubernetes, Chef, New Relic, and Raygun, including how they can be used to enable and implement end-to-end DevOps processes and principles in the software development lifecycle.

Learn how to adopt the automation first mindset and transition from a task-oriented support engineer to a more design and automation-oriented mindset. In this course, you'll explore the concept of an automation mindset and the benefits that enterprises can realize by adopting an automation first mindset. You'll examine the pros and cons of automation and the principles for adopting an automation first approach. Next, you'll learn the process for instilling an automation mindset, the role of design thinking in deriving and enabling an effective automation mindset and digital transformation, and how to apply the automation mindset to automate projects. You'll explore the operational KPIs for tracking and implementing continuous improvement, IT tasks that can be automated, and the evolution of software in Automated Production Systems, and the process of deriving Automated Production Systems. Finally, you'll learn about the "Who will do it" and "How can we get this done most efficiently" approaches and the best practices for building productive DevOps CI/CD automation pipelines.

There is a range of automation technologies available to the support engineer. In this course, you'll explore the features of key scripting languages, common DevOps automation tools, platform automation tool features, and the key areas that system management tools should address. You'll learn about the features of Foreman and how to use Bash, Python, Ruby, and Shell scripts to automate interactions with infrastructures that are hosted on AWS and Azure. Finally, you'll see how to work with Puppet and Chef, create Ansible Playbooks and Salt formulas to automate creating resources and installing web servers, install Foreman, and work with Foreman UI features.

Script automation involves the use of automation software to leverage the current scripts within your framework. In this course, you'll explore the benefits of Infrastructure as a Code and Configuration as a Code, the role of Python in configuring AWS resources, and the automation capabilities of Azure. You'll learn about the differences between PowerShell runbooks and PowerShell Workflow runbooks, how to install Python for AWS to manage AWS S3 buckets, and write Python code to retrieve AWS EC2 information and manage AWS S3 buckets. Next, you'll examine how to set up automated deployments in AWS and create an Azure Automation account. Finally, you'll learn how to create and publish PowerShell runbooks, create Python runbooks to start Azure VMs, execute scripts using Bash Interpreter in Chef, and use Chef Recipes to run scripts and handle configuration changes in AWS.

In this course, you'll explore the competitive advantage of cloud computing, the features of various types of virtualization implemented in the cloud, and the hypervisors that are used by popular public cloud providers. You'll examine how to choose the right cloud service model and the SaaS Enablement Framework components that can be used to build, manage, and deliver SaaS solutions. Next, you'll learn how to select the right cloud deployment model, create AWS HVM Linux AMIs, and manage custom deployment configuration on AWS. Finally, you'll look at how to deploy applications to Azure VMs, configure resource deployments with GCP Deployment Manager, and create Network File Systems on public clouds.

In this course, you'll explore IT support levels that can be structured to provide appropriate technical support and the skills required by traditional Support Engineers to play the role of a Cloud Support Engineer. Next, you'll look at the phases of the Cloud Adoption Framework that you need to follow to manage support services for Cloud. You'll learn about the Cloud Decision Support Framework, which you can adopt to define application migration processes, as well as the concept of Cloud Affinity Assessment. You'll move on to explore how to evaluate drivers and inhibitors of cloud adoption and the metrics that you can use to score potential solution candidate. You'll examine multi-cloud use cases that need to be considered to define support strategy and support levels and the Technical Support Services Guidelines for managing support for GCP resources. Finally, you'll learn how to create a support case, select the severity for AWS resources, and create new support requests from the Azure Portal.

Identifying the job roles, workflows, and processes that can support and manintain DevOps performance is essential for any enterprise. In this course, you'll explore core support roles and responsibilities. You'll also define the best DevOps team pattern to debug key components, identify an effective value stream for observing delivery across multiple systems for debugging, and establish a troubleshooting workflow to ensure continuous delivery and deployment. Next, you'll examine the processes of logical, systematic, and effective DevOps troubleshooting. You'll identify troubleshooting tasks to resolve common DevOps deployment challenges, and the concept, working mechanism, and benefits of reverse debugging. You'll move on to outline approaches to debugging cloud deployment solutions, define techniques for resolving DevOps performance issues, and distinguish the pros and cons of incremental and full deployment processes. Finally, you'll identify troubleshooting approaches for application distribution in multi-cloud and the role of the error monitoring and logging integrated approach. You'll also explore orchestrated service deployment, maintenance, and debugging processes in IaaS clouds.

Knowing how to maintain a healthy DevOps environment in all scenarios is a fundamental skill of the Support Engineer. In this course, you'll explore how to establish a performance test strategy for microservice-oriented applications, the different types of routers for connecting internal networks with external networks, and the features and issues associated with edge routers. Next, you'll identify how to troubleshoot slow connection and timeout issues when accessing AWS EC2 instances. You'll examine how to use CloudWatch metrics, tune the performance of web servers that host applications deployed using DevOps pipelines, benchmark network throughput among Amazon EC2 Linux instances, and configure firewall rules. You'll move on to learn to use the AWS Trusted Advisor and install, configure, and use JMeter to conduct performance, functional, and load testing. Finally, you'll see how to use iPerf and JPerf and configure GoReplay to record live traffic and load test for potential issues impacting performance.

An automated IT delivery cycle can have a significant impact on IT management and monitoring. In this course, you'll recognize the most suitable tasks and processes for automation, examining the role of process, robotic process, and service automation in building value streams for end-to-end Enterprise IT delivery. Next, you'll identify the benefits of implementing process-aware information systems and how to enable flexible and robust business process automation for agile enterprises. You'll indicate the key considerations when setting up multiplatform automation systems, and the relationships, dependencies, and automation policies for deriving reference automation clusters. You'll move on to recognize the role of a DevOps workflow in fulfilling the digital economy's demands and the prominent tools used to facilitate automation environments. Finally, you'll learn how to set up an end-to-end automation environment for a DevOps-driven delivery mechanism.

To exploit IT automation fully, support engineers must know how to identify and use the correct processes and tools. In this course, you'll leverage design thinking in enterprise automation strategy, examining the role of scripting languages and protocols, the considerations when selecting automation tools and scripts, and the targets to configure for automated notifications and alerts. Next, you'll learn how to write batch files to automate repetitive tasks, set up automation environments for command-line tasks, and write Bash scripts to automate Git workflows. Finally, you'll practice how to automate builds with Jenkins on Ubuntu and share artifacts in the Artifactory repository. You'll learn how to automate AWS EC2 instance provisioning, configure mail servers to generate automated emails, and use cloud-based email services for automation.

DevOps pipelines are an efficient means of ensuring the continuous integration and delivery of applications, maintaining clean code, and managing and simplifying production deployment. In this course, you'll explore the key phases of DevOps pipelines, the steps and patterns involved in creating them, and the differences between DevOps pipelines and traditional approaches. Next, you'll explore the principles of continuous integration and the benefits of containerization in DevOps pipelines. You'll identify the patterns of source code management used to simplify production deployment, the code integration strategies used to manage code repositories for diversified staging deployment, and the different types of tests that can be automated when using DevOps pipelines. Finally, you'll examine how to implement continuous monitoring and observability in DevOps pipelines, create pipelines using Azure DevOps tools, and use branching strategies to facilitate collaborative development and operations.

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

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.

The principles and practices of DevOps help enterprises innovate with greater efficiency and agility. In this course, you'll explore these practices, identify the problems they eliminate, and recognize their application in DevOps environments and solutions. You'll also list the DevOps methods and toolchains to improve product development and release and outline how to achieve DevOps transformation to leverage continuous integration and continuous delivery. You'll list the DevOps and Cloud principles that help achieve continuous operations in public, private, and hybrid clouds and the steps involved in building cloud-ready application architectures. You'll identify the IT operational principles for moving to an automated self-service CloudOps model, define the concept of DevOps as a Service, outline the steps involved in modernizing applications, and recognize some DevOps documentation challenges.

To successfully implement DevOps, you need to devise strategies that combine DevOps principles and practices. In this course, you'll explore how to implement robust DevOps practices throughout the deployment process. You'll examine the different types of release management processes, outline automation approaches, and identify methods for solutioning complex applications. You'll recognize how to incorporate DevOps practices with traditional ones, the role of Agile and Lean principles in the DevOps lifecycle, prominent tools for automating DevOps software development, and how to set up Agile delivery practices using Atlassian tools. You'll then set up open source tools to enable continuous application delivery and use Cloud tools to establish continuous integration and deployment and monitor the implementation success of DevOps practices.

Hybrid cloud solutions offer many benefits to growing enterprises. As a CloudOps engineer, you will likely face the challenge of successfully deploying a hybrid cloud solution. In this course, you'll explore the core components of a hybrid cloud solution, the business challenges driving their adoption, and how to develop roadmaps to achieve them. You'll identify the main pillars of hybrid cloud applications and the differences between hybrid, on-premises, and single cloud deployment solutions. You'll also examine considerations when deploying middleware using hybrid cloud solutions. Next, you'll outline a hybrid cloud migration strategy and the hybrid integration reference architecture. You'll name the cloud vendors providing end-to-end or elements of hybrid cloud solutions. Lastly, you'll compare the capabilities of the hybrid cloud solutions offered by AWS, Azure, Intel, and IBM.

When managing a hybrid cloud environment, applying DevOps principles can aid with deployment complexity, legacy app modernization, and Agile and CI/CD process implementation. In this course, you'll learn how to enable DevOps in a hybrid cloud environment, recognizing the associated challenges, the role of containerization and DevOps deployment strategies, and the implementation process of CI/CD. You'll then examine the challenges, best practices, and tools for monitoring hybrid clouds. You'll also recognize the workload combination used to build hybrid architectures, the tools used to design conceptual architecture and set up CI/CD mechanisms, and the benefits of hybrid CI/CD. You'll set up and configure hybrid cloud connectivity between AWS and on-premises environments, CI/CD processes for hybrid environments using Jenkins with Docker, and monitoring tools to monitor solutions deployed in hybrid environments.

Aspiring cloud engineers need to move beyond working with single platform deployments, such as on-prem and cloud, to more complicated scenarios, such as hybrid and multi-cloud deployments. In this course, you'll explore the benefits of multi-cloud and hybrid cloud, common scenarios involving hybrid and multi-cloud deployment, and the multi-cloud DevOps framework for managing multi-cloud distributed environments. You'll examine best practices for achieving multi-platform deployment and the driving hybrid and multi-cloud design principles to set up multi-cloud environments. You'll also outline hybrid and multi-cloud deployment and architectural patterns, network topologies used to set up hybrid and multi-cloud environments, and approaches to adopting multi-cloud management. Lastly, you'll identify a multi-cloud operating model and outline techniques to transition to cloud and multi-cloud environments and troubleshoot application distribution issues in multi-cloud environments.

Aspiring cloud engineers need to know how to select and use hybrid and multi-cloud deployment management services. In this course, you'll explore the AWS services for implementing hybrid and multi-cloud solutions and other multi-cloud environment application deployment tools. You'll configure AWS Outpost to run AWS services on-premises, Azure Arc to build hybrid deployment environments, and AWS, Azure, and GCP to connect with other public cloud platforms. You'll use AWS and Azure to set up dual or multi-cloud deployment environments. You'll examine how to back up multi-cloud environments and overcome disaster recovery challenges. Next, you'll configure Aviatrix networking software for an AWS multi-cloud network and LiveData for multi-cloud environment backup. Lastly, you'll use Terraform with VPNs to create secure, private, site-to-site connections between Google Cloud Platform and AWS.

An aspiring cloud engineer needs to learn how to build upon DevOps architecture design fundamentals to formulate robust and sound container clustering strategies. In this course, you'll recall the concept of clustering and outline the different cluster architectures for failover and load balancing support. You'll outline what comprises a clustering topology, physical and virtual clusters, and enterprise application cluster management. You'll then name clustered environment resource managers, application containerization types, and containerized cluster implementation solutions. Next, you'll examine data center cluster virtualization and investigate hybrid cloud cluster architectures for multi and hybrid cloud solution delivery. You'll explore clustering in Docker, Kubernetes, and Docker Swarm. Lastly, you'll set up single control-plane Kubernetes clusters, Docker Swarm clusters for high availability, and high-performance compute node clusters in Amazon EC2.

Container orchestration is an essential DevOps practice for managing containers in diversified environments. It provides portability and scalability by orchestrating containerized application management in multi-cloud environments. In this course, you'll start by examining cloud application-level orchestration, the orchestration layer, and the container orchestration tools for virtual machine and container allocation. You'll then outline the architectures used in multi-cloud and multi-cluster environments and cloud orchestration reference systems used for TOSCA-complaint container cluster federation. Next, you'll explore orchestration in managing multi-cloud environments and container orchestration in implementing CI/CD pipelines. You'll implement a pool of Docker hosts into a single virtual server and set up container clusters using Amazon Elastic Container Service and Fargate. Lastly, you'll deploy Azure Kubernetes Service clusters, use Kubernetes Cluster Autoscaler, and create multi-cloud Docker clusters with Docker Swarm.

An obvious goal of any cloud engineer is to ensure as little loss of service and downtime as possible. In this course, you'll explore the concept of high availability and outline the most common IT considerations, necessary components, architectures, and recommended strategies for implementing high availability. You'll examine the different types of outages addressed using high-availability solutions and the implementation approaches for primary and secondary distribution servers in high-availability environments. You'll then classify the benefits and pitfalls of implementing high-availability deployments and compare traditional and virtualized high-availability and failover solutions. Furthermore, you'll investigate the system availability metrics, key cloud architecture patterns, and critical cloud service management guidelines for designing and architecting high-availability solutions. Lastly, you'll differentiate between traditional and cloud high-availability solutions and define the process of migrating from traditional to cloud high-availability deployments.

How high availability (HA) is implemented, managed, and deployed often comes down to how applications use HA in their given environments - traditional or cloud. In this course, you'll learn how to implement HA in the cloud. You'll start by examining three-layer classification and the role of availability frameworks in implementing high-availability cloud solutions. You'll then explore the design process for developing a highly available cloud, the concept of elasticity and scalability, and the HA services provided by various cloud providers. Next, you'll configure a template that defines EC2 instances and use it to create an EC2 Auto Scaling group. You'll use AWS CodePipeline to deploy applications to multiple high-availability environment regions and Azure to create highly available virtual machines. Lastly, you'll configure an instance for high availability in GCP.

When multi-cloud or hybrid deployment scenarios are at play in distributed environments, technical support engineers need to switch from a data center to a CloudOps mindset. In this course, you'll investigate several deployment strategies for setting up multi-cloud or hybrid infrastructures. You'll examine multi-cloud architecture patterns, use cases, and business continuity reference architecture, as well as how to approach secure multi-cloud peering. You'll also identify the services provided by multi-cloud management systems for simplifying next-gen firewall insertion and operations and the CI/CD processes for managing multi-cloud. You'll recognize the challenges of federated multi-cloud PaaS and hybrid and multi-cloud environment monitoring, design consideration for setting up multi-cloud disaster recovery processes, and the differences between cloud orchestration and automation. Finally, you'll create and configure a virtual network, gateway subnet, VPN gateway, and virtual private cloud Elastic IP VPN device in Azure and AWS.

A CloudOps engineer should be able to deploy into various multi-cloud environments confidently. In this course, you'll recognize the key benefits of multi-cloud deployments and recall the CAMP and TOSCA principles for flexible multi-cloud deployment. You'll examine the features of high-performance cloud routing, cloud network-as-code, and multi-cloud and multi-region transit routing. You'll also identify multi-cloud deployment, management, and cost control tools, the features of Cloudify and Kubernetes for managing multi-cloud deployment challenges, and the conceptual architecture and benefits of CI/CD pipelines for multi-cloud deployment. Next, you'll outline how to create multi-cloud applications and segregate services. Finally, you'll deploy the Node.js web server and application using Cloudify, setup and configure Spinnaker for multi-cloud deployments, configure Jenkins for multi-cloud pipeline management, and create and configure a simulated hybrid environment for deployment.

Splunk is a horizontal application with many possible use cases. In this course, you'll explore the core Splunk products, features, and pricing options that can help achieve CloudOps observability, discover problems, collect and index data, gain business-critical insights, and implement a visualization service for cloud operational intelligence. Additionally, you'll examine the benefits of using Splunk Cloud, the data ingestion concepts used by this product, and its approach to identifying, investigating, and resolving critical issues. You'll also recognize the infrastructure monitoring capabilities provided by Splunk App for Infrastructure. Moving on, you'll learn to use Splunk to explore, categorize, and analyze data and generate Sparklines. You'll navigate Splunk Cloud's primary interface. You'll ingest, organize, and mine data in Splunk to glean operational intelligence. And lastly, you'll integrate VictorOps with Splunk.

The components of Splunk provide CloudOps practitioners with reliable methods to give their data meaning and structure in efficient ways. In this course, you'll examine various Splunk components used to create reports, including datasets, data models, and inheritance. You'll also explore the primary components of Splunk's Search Processing Language, some best practices for designing data models with Splunk, and the different types of lookup configurations you can create in Splunk. You'll then use the Data Model Editor to design a data model and create charts, dashboards, and reports for visualizing ingested data. You'll use commands in Splunk to transform search results into data structures. You'll create pivot reports, lookup files, alerts, and search macros. Lastly, you'll learn how to run Splunk reports automatically.

You may know how to implement continuous integration (CI) and continuous delivery (CD) in a single cloud environment, but what about more advanced environments? In this course, you'll explore the challenges of applying CI/CD techniques to hybrid and multi-cloud environments and the benefits and use cases for doing so. You'll examine the 12-factor multi-cloud CI/CD process, the critical components of integrated solution design, the prominent hybrid and multi-cloud architecture patterns, and tools to manage CI/CD processes. You'll then outline how to set up multi-cloud environments geared for CI/CD implementation and how to transition from traditional multi-cloud to secure CI/CD deployment. Next, you'll investigate enterprise CI/CD patterns for diversified hybrid and multi-cloud environments and how to build pipelines for multi-cloud application deployment. Lastly, you'll identify the different hybrid environments that can simplify multi-cloud CI/CD processes.

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

When transitioning to a CloudOps Engineer role, you're required to recognize where your responsibilities lie and how to embrace proper CloudOps practices. In this course, you'll distinguish the roles of DevOps and CloudOps engineers and explore a CloudOps engineer's role in designing, deploying, and managing end-to-end IT processes and in project management. You'll examine the fundamental factors in setting up a robust CloudOps practice, the primary cloud infrastructure components and technologies, and how to implement DevOps principles to ensure continuous operations in public and private clouds. You'll also investigate the most prominent cloud architectures, automation techniques for composing agile and automated end-to-end IT processes, and software re-architecting and re-engineering approaches. Lastly, you'll identify the significant CloudOps security and compliance challenges you need to mitigate when transitioning from a traditional IT to a CloudOps environment.

To develop into a dependable CloudOps engineer, you need to be conscious of the many hats you must wear and the many situations you need to plan for. In this course, you'll explore the various skills needed by CloudOps engineers, including those required for cloud management, CloudOps component architecting, and to establish collaboration and communication techniques. Next, you'll learn how to identify the need for continuous application optimization in the cloud and outline why and how to adopt cloud reference architecture. You'll also identify optimal CloudOps transformation and modernization methods, how to design a technology upgrade path, and the selection criteria when choosing cloud platforms and tools. Lastly, you'll investigate how to embody a strategic advisor's role and implement appropriate multi-cloud security assessment and defense techniques when designing and architecting CloudOps solutions.

Transitioning to the cloud requires a design-thinking process. In this course, you'll explore the tasks and considerations involved in CloudOps solution design. You'll examine how to shift from a static to a dynamic infrastructure, utilize knowledge exploration and exploitation, and adopt a CloudOps managed strategy and architecture service. You'll investigate the role of prototyping tools, using them to create visual drafts of CloudOps solutions. You'll use Lucidchart with AWS and Azure to present architectural diagrams, visualizations to create CI/CD task boards, and Visual Paradigm to create multi-cloud visual architectures and perform a Five Forces Model analysis. Next, you'll investigate the patterns used to implement reliable and recoverable CloudOps solutions, including health endpoint monitoring patterns. You'll outline the steps involved in managing a hosted cloud service. Lastly, you'll identify the routing requests used in multi-cloud environments.

Continuous automation in CloudOps provides many benefits, but its impact doesn't come without disadvantages. In this course, you'll explore the advantages and disadvantages of several areas of automation, including continuous product delivery, infrastructure provisioning automation, Zero Code multi-cloud automation, and edge networking automation. Next, you'll investigate how to establish an automation governance model and the role of automation and multi-cloud orchestration in moving cloud workloads and managing infrastructure environments. You'll identify prominent multi-cloud management tools and typical use cases of disposable and repeatable infrastructure. Moving on, you'll configure Terraform for automated workflows, use Jenkins and Terraform on Amazon EKS to configure continuous integration, and use Ansible and Terraform to set up environments. Lastly, you'll deploy applications to AWS and configure Cloudify to integrate with an automation toolchain and use a single CI/CD plugin.

Continuous operations and improvements in the cloud is key for successful CloudOps and requires the appropriate components, tools, and best practices to be implemented. Cloud virtualization platforms offer the capability to provide highly redundant, failure-proof systems through the use of hardware and software components. In this course, you'll explore the concept and objective of redundancy and the hierarchical network design used to attempt to eliminate single points of network failure in the network. Part of the discussion includes the key redundancy architectures and factors to consider in providing redundancy in the network design. You'll also examine the features of various cloud computing architectures and differentiate among redundancy management approaches in diversified deployment environments. Next, you'll investigate the need for redundancy in data centers and outline the concept of site redundancy and the role of geo-redundancy in resolving problems of unused computing resources. You'll then identify the need for data replication to achieve redundancy, the features of asynchronous and synchronous replication, along with the conceptual aggregated work path for cloud services to implement redundancy and enable end-to-end availability.

An additional level of redundancy for cloud operations can be achieved by leveraging the resources and architectural components of multiple clouds. In this course you'll explore the concept of the platform as a service (PaaS) framework and the tools available to design diversified architectures. You'll discover open-source and SaaS-based tools used to manage redundant multi-cloud environments, the implementation of geo-redundancy and replication in AWS, Azure, and GCP as well as the key cloud redundancy design principles for building resilient application infrastructures. Also discussed are the key principles and best practices for building multi-cloud deployment platforms and disaster recovery programs. The course also explores recommended design principles and components for cloud data management, architecting successful disaster recovery plans using multi-cloud technologies, and demonstrating how to work with Visio, Arpio, and Hashicorp to design redundant multi-region cloud architectures. Finally, you'll learn how to replicate an AWS environment to an alternate AWS regions and set up redundant environments on AWS and Azure.

Supporting critical CloudOps tasks requires skills and knowledge specific to managing and troubleshooting cloud operations and multi-cloud environments. This course takes a look at essential DevOps elements, the required support architecture, and the automation mindset needed to support critical CloudOps operations. You will explore why creating knowledge bases and establishing key troubleshooting approaches is an essential part of dealing with the challenges and issues of managing multi-cloud environments and storage. Using these tools, you'll be able to identify the root cause of multi-cloud issues and how to troubleshoot the identified issues. In this course, you'll also discover the differences between problem management and incident management, the role of monitoring and alerting systems in resolving operational issues, and the key capabilities of multi-cloud discovery. Finally, the course provides insight into the critical application and business KPIs that can be analyzed to prioritize support tasks.

When designing solutions, CloudOps practitioners need to mitigate typical performance issues. In this course, you'll explore some common performance problems and the systemic tuning approach to improving performance. You'll examine what comprises a performance engineering approach before outlining a practical performance tuning roadmap. Next, you'll identify post-deployment performance diagnostic techniques for large-scale software systems, essential steps when optimizing application performance, and functional and non-functional components and layers to consider when planning performance management. Moving on, you'll outline the steps involved in configuring performance testing and identify critical cloud computing KPIs and metrics. You'll investigate use cases that help identify gaps in hybrid and multi-cloud deployment architectures. You'll examine performance management challenges and recommended solution architecture for cloud-hosted services. Lastly, you'll outline how to measure private and hybrid cloud performance.

Managing the performance of cloud-hosted services doesn't come without challenges. Luckily, there are tools to help you monitor, measure, and improve performance. You'll start this course by exploring common performance management challenges and solutions. You'll then examine parameters to track IT infrastructure and application performance, differentiate between performance and scalability, and identify key metrics to monitor virtualized environments. Next, you'll look in-depth at the purpose of various tools and services, such as the purpose of the five pillars of the AWS framework, how to improve infrastructure resources on Google Cloud Platform, and the role of IBM Cloud Application Performance Management in increasing cloud efficient. You'll also work with CloudWatch Agent, AWS Compute services, Amazon QuickSight, and Application Insight to increase performance efficiency and visibility and troubleshoot issues. Finally, you'll outline how to ensure applications are more scalable, resilient, and manageable in Azure.

Managing the performance of cloud deployments extends to managing multi, hybrid, and multi/hybrid distributed cloud infrastructures. In this course, you'll explore the challenges, goals, and strategies for performance optimization in these environments. You'll start by identifying desired characteristics for successful and robust hybrid or multi-cloud designs. Next, you'll outline how to create a multi-cloud performance optimization strategy and monitor hybrid and multi-cloud distributed infrastructures. You'll then identify common multi-cloud performance challenges and associated solutions. Furthermore, you'll investigate prominent use cases of multi-cloud networking, recommended solutions to resolve multi-cloud network configuration issues, and the different non-functional tests available to determine a multi-cloud performance benchmark. You'll examine significant business and service continuity performance tuning tasks. Lastly, you'll outline how to tune multi-cloud integrator components to resolve connectivity issues between two participating clouds.

CloudOps architects need to explain how their often complex, multi-cloud deployment solutions work to a wide variety of audiences - not an easy feat, but one you'll learn to overcome in this course. You'll start by defining the concept of interpretability and explainability in CloudOps. You'll then outline how to build explainability into a CloudOps workflow, investigating the core explainability principles and benefits along the way. You'll examine the explainability decision tree used to derive a value stream from an existing CloudOps implementation, the algorithms used to explain CloudOps practices, and the governance strategy for deploying explainable cloud applications in multi-cloud environments. You'll examine the challenges and opportunities of explainable AI in CloudOps and identify the key applied intelligence features to derive AIOps solutions. You'll end this course by creating a basic explainability workflow using New Relic.

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