azure devops pipeline

Azure DevOps Pipeline Complete Guide 2022

The past decade has witnessed the most technological advancement in every industry, especially in terms of the digital world. As the world now uses some type of software in day-to-day life, it is essential to reduce the overall software development lifecycle and reduce human efforts in doing so. And with that goal, Azure DevOps came into existence. Developed by Microsoft, Azure DevOps is an overall package that offers tons of useful services to enhance the overall development lifecycle of software or an application. Legions of organizations have already implemented Azure DevOps in their culture to reduce the development time without compromising the code quality. 

Azure DevOps comes with several features including Azure Boards, Test Plans, Repos, Artifacts, and Pipeline. This article will explain all about the Azure DevOps pipeline including its benefits, components, phases, and configuration. 

azure devops pipeline 2022

What is the Azure DevOps Pipeline?

Many experts believe that automation is the future and this is exactly what Azure DevOps Pipeline is all about. It is a set of automated processes that developers can use to create, and deploy codes to computation platforms. The idea is that every time there is human intervention in a process, the probability of mistakes or issues will always exist. Being a continuous delivery tool based on automation processes, its primary goal is to eradicate or minimize human intervention in projects that will automatically reduce the possibility of errors. Not only that, but the tasks achieved through the Azure DevOps pipeline are accurate, seamless, and faster as well. 

Applications based on Python, Node.js, Java, XCode, Go, JavaScript, and .Net support the Azure DevOps pipeline. However, it requires source control to work with such apps. The base of this pipeline is a continuous integration and continuous delivery which builds, tests, and deploys the code steadily.

Continuous Integration (CI):

Continuous integration is all about finding and fixing bugs as soon as possible. CI helps in finding underlying or overlooked issues and bugs in the code in the initial stages of the development, making bug-fixing faster as they can be fixed easily and quickly in the early stages.  Furthermore, developers will always know about the errors, and can even give a glance to code through version-controlled repositories. With continuous testing and compilation of code, the entire integration process becomes faster, enhancing the overall productivity of the team. 

Continuous Delivery (CD):

Continuous delivery is all about boosting the delivery process and making sure that every policy is followed, each test is performed effectively, and code is deployed in the right environment. Moreover, it integrates the code with infrastructure and assists the developers in fixing bugs, making essential changes in the code, and adding new features as well. With all these actions, CD ensures that bug fixes are delivered faster, and the overall risk related to release is reduced to a maximum extent so that software delivery can become faster and more feasible. 

Benefits of Azure DevOps Pipeline

When implemented and utilized appropriately, the Azure DevOps pipeline provides several benefits to the organization, and those are explained hereafter. 

  • Support for Multiple Languages: As there are multiple programming languages and application types, having a single culture that supports all the major types is essential. Azure DevOps pipeline can be used in several application types along with all the leading programming languages like Java, Ruby, C, PHP, and many others. Support for not just Windows, but macOS, and Linux is also present as well. 
  • Progressive Deployment: Development and testing is the phase where the scope of improvement is the most. Azure DevOps pipeline allows the developer to set multiple stages in these phases to control the project’s quality prior to moving to the next phase. The idea is to make sure that the code is optimized to the fullest and is error-free entirely. Moreover, it assists in identifying hidden bugs as well as several issues to eradicate them from the root. 
  • Rapid Upgrades: One of the best things about the Azure DevOps pipeline is not just its features, but the frequency of upgrades. Managed by Microsoft, Azure DevOps is updated rapidly to ensure that it remains updated with the latest features. 
  • Pricing: Azure DevOps pipeline is a widely used service that has helped innumerable organizations. Another great thing about this is its pricing. For a public project, the Azure DevOps pipeline is entirely free of cost. However, developers working on a private project need to pay its subscription fee post-1800 minutes every month. 

Components of Azure DevOps Pipeline

There are certain components that make up an Azure DevOps pipeline and comprehending them is essential to ensure its smooth implementation and attain effective outcome. With that in mind, here are the different components of the Azure DevOps pipeline. 

  1. Artifact: An Azure DevOps pipeline artifact is a collection of packages published by build pipelines that allow the team to assort the dependencies for the development of the application. In most cases, these artifacts are available to desired tasks. 
  2. Agent: Whenever a developer runs a build or deployment, their system commences single or multiple jobs. An agent will run one job at a single time as it is equipped with agent software and is entirely a computing infrastructure. To make its management easier, these agents can be grouped into agent pools, eradicating the need of managing each agent separately. 
  3. Library: A library is like storage or safe for your variable groups and secure files. Azure DevOps pipeline variable groups have secrets and store values that you want to include in the YAML pipeline or several other pipelines. On the other hand, a secure file is a method of storing files that can be shared to multiple pipelines. Storing variable groups and secure files will allow you to gain access to them whenever you want.
  4. CD: Continuous delivery is an integral component of the Azure DevOps pipeline where code is created, tested, and deployed in a test and production stage. Running tests on a single code but in multiple test and production stages helps in reducing errors and enhancing the overall code quality.  
  5. CI: Sometimes, code testing can be complicated for the team. Continuous integration helps the team in simplifying not just testing but building the code as well. By performing automated tests on the code, CI aims to eradicate bugs and issues by detecting them in the early stages of software development. It all depends on the developer whether they want to run CI whenever a code is pushed or set a schedule or both. CI systems create artifacts mentioned above which are further used in CD release pipelines for automated deployments. 
  6. Approval: In general, approval is a nod for agreement or permission to perform a specific task. In the Azure DevOps pipeline, approval is a set of validations necessary prior to running a deployment. Unless all the checks are completed or approvals are achieved, the pipelines will stop functioning to prevent the deployment environment from starting. 
  7. Deployment and Deployment Group: Deployment is performing tasks for a stage including deploying build artifacts, performing automated tests, and other actions for a stage. For Azure DevOps pipeline YAML, this deployment is also called a deployment job which is an assortment of steps that run in a sequence in an environment. Apart from that, a deployment group is a collection of deployment target machines where agents are installed, making it highly similar to an agent pool. 
  8. Release: A release is a versioned set of artifacts that includes a snapshot of the crucial details necessary to perform different tasks and actions in the release pipeline. In a classic pipeline, these actions can include policies, stages, and deployment options. 
  9. Trigger: Whenever a pipeline is told to run at a specific time, it is triggered. In order words, a trigger is set by a developer that activates the pipeline at the desired time. Configuration of a pipeline at schedule time or post-completion of a build is possible and these actions are considered an Azure DevOps pipeline trigger. 
  10. Script: A script helps in running a code as a step in a pipeline through the command line. However, the difference between a task and script is that the latter is entirely custom code and exclusive for your pipeline.  
  11. Run: If you want to implement any function in a pipeline, you need to use the run command. By gathering the logs linked with all the currently performing steps, it evaluates the pipeline and transfers the run to several agents afterward. 
  12. Stage: A logical boundary in the pipeline is a stage and it is majorly used to highlight the separation of concerns like production, build, and quality assurance. Every stage will have at least one job, but it can have multiple jobs as well. Whenever there are multiple stages defined, they always run consecutively by default. 

Phases of Azure DevOps Pipeline

The Azure DevOps pipeline is not the same for every project or team, but it varies with the requirements, technology stack, budget, and the DevOps engineer’s experience. With that in mind, there cannot be an accurate framework of a DevOps pipeline, but a common framework that can be altered to meet the requirements. Below explained are the common phases of an Azure DevOps pipeline.

  • Planning: Planning is the first part of the Azure DevOps phases which includes incremental planning for the next sprints, releases, or iterations in the project. The two most widely used Agile methodologies focusing on planning are XP and Scrum and picking the one can be done based on factors like company structure, project needs, and individual choice. Both XP and Scrum can provide information on backlogs, stand-ups, and metrics which can help in enhancing overall team transparency and visibility. Furthermore, the project and product managers will build a roadmap for development that will help the entire team in the upcoming tasks to achieve. 
  • Development: Development is the part where the coding begins. In this part, the developers install their desired programming language’s integrated development environment, code editors, and any other tool necessary for coding on the machine. To make understanding the code easier for the entire team, developers follow a specific coding style and meet the set standards to maintain utmost uniformity in the code. Once the developers are done writing the code, a pull request is made to the code repository. Afterward, the code is reviewed by the team members. 
  • Building: If the code with potential errors reaches the Azure DevOps pipeline, it could lead to a significant problem that could not just ruin it but may also lead to huge losses in the future. Due to this reason, several automated tests are performed on the code post-submission to the shared repository. In the building stage, the developers will try to find and eradicate as many issues from the code as possible. In most cases, once the pull request is sent, it will commence an automated process that will compile the code to a build.
    Languages including Python and PHP do not require compilation whereas C and Java need to be compiled before doing so. Though all experienced developers are aware of this fact, it is always essential to keep that in mind. Coming to the test, the build will fail if there is an issue with the code. In case that happens, the developer will be alerted and they will make the necessary fixes to the code and repeat the process until they achieve the desired results.
  • Testing: Once the build stage is complete and is successful, it is time for the actual testing to begin. Even though the developers will perform several automated tests in the build stage, still running full-fledged testing to the code is crucial. In this stage, the developers will not just run automated tests, but manual tests as well. Several checks including performance, security, and load testing are done before the code is sent for production. Furthermore, a user acceptance test is also performed to understand user behavior and decide whether any additional changes are required or not. If everything goes well, the code is pushed for the next stage.  
  • Deployment: After the testing is completed, the build will reach the deployment stage where it will be pushed for production. In case the code passes the earlier tests smoothly without any major changes, the automated deployment method will be used. However, if the aforementioned does not happen and the build goes through major changes, it will be deployed to an environment similar to the production to monitor its behavior.
    There are several ways to do so, but the commonest one is the blue-green strategy where two similar production environments are used. One environment will host the new version of the build whereas the other one will host the unchanged version of the build. Doing this will help them in monitoring the performance of the build, and if anything goes out of hand, they can simply switch to the working environment to fix the issue. 
  • Monitoring: Monitoring the deployed build is the final stage of the Azure DevOps pipeline. Here, the operations team will monitor the system, application, and infrastructure to ensure the smooth working of the program. Collecting Azure DevOps pipeline logs, monitoring systems, analytics, and feedback from users is also part of this phase. This collected data will help the team in finding and fixing any issues in the code. Apart from that, it will also assist them in eradicating all the issues in the production environment, directly improving their efficiency.

Building an Azure DevOps Pipeline

Building an Azure DevOps pipeline is an intriguing process that will aid in enhancing the team’s productivity and efficiency. This guide is all about building a sample application through Azure YAML pipelines using a YAML pipeline editor. Before you begin with the process, make sure that you have a GitHub account to create a repository, authority to run pipelines on Microsoft-hosted agents, and an Azure DevOps organization. 

  • Creating the first Pipeline: Before you create your first Azure DevOps pipeline, you need to specify the programming language that you are going to use. Depending on the language, you need to attain the sample code from GitHub. Once you have attained the sample code, you need to sign in to your Azure DevOps organization and head towards your project.
    • Afterward, you need to select the New Pipeline option from the Pipelines section. Now select GitHub as the location of the source code. Keep in mind that you may have to sign in to your GitHub account to move forward to the next step. Upon successful login to your GitHub account, you will see a list of available repositories. Here you need to select your repository.
    • In case you have not installed the Azure Pipelines application, the system may redirect you to GitHub to install the same. All you have to do is click on Approve & Install to begin the installation. Once the installation is complete, Azure Pipelines will analyze the repository and recommend the desired pipeline template based on the programming language that you have preferred.
    • Once you have the Azure DevOps pipeline template, you can review the YAML to understand its functionalities, and click on Save and Run whenever you are done to move to the next step. Now you will have to commit a new Azure Pipelines YAML file to your repository. Akin to the previous step, all you have to do is click on the Save and Run button.
    • Now, you need to select the build job to see your pipeline functioning and the pipeline is ready to be used. You can customize the newly created Azure DevOps pipeline from your repository. Just head towards the Pipeline page and click on the Edit button to make any desired changes to the pipeline. 
  • Clone Azure DevOps Pipeline: Sometimes you just need to copy an already created pipeline and customize it to create a new one. Pipelines created from both YAML and classic editor can be copied but the process is a bit different. When compared with the classic editor, YAML pipelines are easier to copy. Keep in mind that cloning a pipeline is only possible when it is done within the same project. For different projects, the pipelines can be exported. 
    • YAML Pipeline: The process of cloning Azure DevOps YAML pipelines can only be done from the source pipeline. Start by heading towards the Pipeline Details of the pipeline which you want to clone and click on the Edit button. Now all you have to do is copy the pipeline YAML from the editor and paste the same to your new pipeline’s YAML editor. After cloning the pipeline, you can begin customizing it as per the project’s demands. 
    • Classic Editor Pipeline: Before you begin the process, you need to make sure that you have either Edit Build Pipeline or Edit Release Pipeline permissions. If you already have those, you can head towards the pipeline details page of the pipeline which you want to copy and click on the Clone button. The cloned pipeline will come with the name-cloned where you just need to click on Save & Queue or Save button and the pipeline will be cloned. 

Azure DevOps is all about making software development, operations, and deployment faster and more effective. Azure DevOps pipeline helps in streamlining the implementation of these actions and promotes continuous delivery practices into the organization. With the rising use of artificial intelligence, machine learning, and automation, it can be said that the DevOps pipeline will become an integral strategic asset for an organization. With a team of skilled and highly experienced professionals, ThinkSys Inc can help you in implementing Azure DevOps into your organization’s culture.

From DevOps Management to Expert DevOps Consulting, ThinkSys Inc is the all you need for any DevOps-related service. 

Frequently Asked Questions

Azure DevOps pipeline allows the user to specify conditions under which every step, stage, or job runs. This pipeline comes with a default configuration where every stage or job will run if it is not dependent on any other stage or job. The same applies if every stage or job that it depends on has already been completed. Furthermore, a step will run in case there is no failure and the step preceding it is already finished. Azure DevOps pipeline allows customization of this behavior as per the user’s requirements. 

An Azure DevOps build pipeline helps in building and testing code projects effectively. On the other hand, the Azure DevOps release pipeline’s primary task is to help the team in continuous delivery of the software to the customers with the least risk and quickly. In other words, build pipelines are for building the software whereas release pipelines are for software delivery.

Serverless Computing Options With AWS, Azure, Google Cloud

Serverless Computing Options With AWS, Azure, Google Cloud

Serverless computing involves on-demand function execution for application deployment. Serverless provides agility in application development to stay ahead of a world of rapid changes and innovations. O’Reilly conducted a survey with 1500 IT professionals in 2019 and 40% confirmed being associated with organizations working on serverless options. Owing to benefits linked to cost-savings, on-demand execution, and non-stop availability, it is expected that the adoption of serverless computing will only go up further in the coming years.

Serverless Computing Options With AWS, Azure, Google Cloud

The options are growing too. Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are three great options for serverless computing services, and features offered by these three market giants vary from one another.

Serverless Computing on AWS  (Click Here to Tweet)

AWS offers numerous options in serverless computing, application integration, and data storage. AWS Lambda and AWS Fargate are the two popular options.

  • AWS Lambda: AWS Lambda provides the ability to virtually run code for the applications or services with no administration. It also provides the mechanism to write code in any preferred language and utilize serverless and container tools. AWS SAM and Docker CLI are some of the popular tools under AWS Lambda. Continuous scaling, cost optimization, and improved performance are some of the benefits offered by AWS Lambda.
  • AWS Fargate: It is a serverless compute engine that promotes security through application isolation by design and easily deploys and manages applications. AWS Fargate works with Amazon’s Container and Kubernetes services. The customer needs to pay only for the resources involved in running the containers thereby helping control costs in a volatile setup.

Serverless computing options by AWS also cover application integration and data storage with ample choices available in these two areas. Amazon API Gateway, Amazon SQS, Amazon EventBridge, Amazon SNS, etc. are a few options offering scalable and hassle-free application integration. Amazon S3 is the data store that uses serverless computing to enable scalable data storage while maintaining essential data properties, such as availability, integrity, and confidentiality at all times. Amazon Aurora Serverless, Amazon DynamoDB, and Amazon RDS Proxy are the other data store options available.

Serverless Computing on Azure (Click Here to Tweet)

Microsoft’s growing focus on the cloud has meant that Azure presents itself as a viable option in most cloud-linked spaces and serverless is no different. Azure Serverless is now a popular choice with multiple options offered to the end-users in the areas of compute, workflows and integration, database, monitoring, storage, and others.

Customers can select from a wide range of serverless execution environments without bothering about the infrastructure management activities irrespective of application or service type. Azure Kubernetes Services (AKS) is a powerful serverless computing option to create serverless Kubernetes-based applications. Open-source functions runtime provide the option to execute serverless functions written in any language.

Azure serverless API management provides the mechanism to manage and monitor the APIs to enable smooth workflows and robust integration. Azure Event grid enables serverless messaging and simplification of event-based scenarios. Azure also provides an option to build serverless applications using relational and non-relational databases. Azure SQL Database serverless and Azure Cosmos DB are two options present. Azure Blob Storage gives the options to create static applications and supports the scalable storage of unstructured or semi-structured data. A durable functions feature is also included under Azure serverless computing features, enabling the development and execution of stateful serverless functions.

Serverless computing services and options by Azure have many striking similarities with AWS and a few differences. Azure offers broader language support than AWS Lambda. C#, F#, and JavaScript are fully supported by Azure with many other languages supported in experimental or preview modes.

Google Cloud Serverless Computing Options (Click Here to Tweet)

Google Cloud serverless computing options eliminate the need for all infrastructure management by enabling source code/container development or deployment. Google App Engine is the primary serverless computing platform offered by Google. However, it supports JavaScript as the only language unlike a wide range of options available in Azure of AWS serverless computing options. Third-party wrappers can be used with the Google Cloud platform to execute other languages, such as Python.

Google Cloud serverless platforms provide the mechanism to build, develop, and deploy scalable APIs. REST APIs can also be developed and managed for web and mobile applications. Complex event requirements of these applications can be handled by Google Cloud through automated event orchestration. Current and future data processing requirements are considered to manage the autoscaling and authorization aspects of the apps. Scalable and pay-per-use functions as a service (FaaS) options are provided by Google Cloud serverless platforms to run the codes without any need for server and infrastructure management. Apart from the Google App Engine, Cloud Run and Cloud Functions are the other options provided by Google Cloud.

Summary

Serverless has gone mainstream in recent years due to the flexibility and benefits that it offers. It’s a fair bet that serverless will further expand and it will be closely synced with the container ecosystem. The event-driven approach in serverless will continue to connect everything present on the cloud. As that happens, expect more application development to become serverless-focused. The choice of serverless platform will ultimately be driven by the specific needs and available skill sets. That said, these are all great choices.

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Related Blogs:

  1. Exploring Serverless Technologies.

  2. Serverless Computing Options.

  3. All Cloud Computing Approaches Supporting Enterprises IT Today.

  4. Serverless App Testing.

azure and AWS

AWS and Azure: The Business Enablers for 2020

A few weeks ago Amazon Web Services announced its new industrial IoT support platform – AWS IoT Sitewise. Not long before that, Microsoft Azure launched a Healthcare Emergency Response Solution. Even in tough times, these two cloud services have stayed committed to growth.

Now, product development organizations consist of remote and distributed teams of skilled and cost-effective talent from across the world. Of course, the pandemic has driven the sudden dominance of the cloud. Expanded “Work from home” measures are accelerating cloud adoption.
azure and AWS
In that scenario, both AWS and Azure have much to offer. While AWS is committed to aiding businesses of all kinds with flexibility and scalability, Azure has been allowing businesses to develop and deploy their services uninterrupted.

Some businesses are still struggling with stepping up to cloud adoption. The lack of technical experience has put them in a peculiar situation. They are skeptical about accepting the cloud as the new normal, but they cannot deny its necessity.

For such companies, let’s take a look at how both AWS and Azure can accelerate business impact now.

Based on these benefits, organizations can know AWS and Azure better. They may thus be able to make a choice based on what works best for them.

  • Native Tools and services: Over, time AWS and Azure, both have evolved from being mere cloud computing and storage facilitators. AWS is capable of providing complex services that take care of database management, software development assistance, networking, mobility, analytics, etc. On the other hand, Azure offers an array of specific applications for business needs. These applications have been aiding various industries like healthcare, financial service, and even governments. The native tools and services provided by AWS and Azure have been aiding businesses with cloud computing, security, administrative compliance, and access to a new age technology stack, among other benefits. Businesses of all sizes can be relieved of the burden of picking and investing in good infrastructure and third-party tools from multiple vendors. Moreover, native tools and services help with better monitoring and tracking at different stages of management and administration. These are savings in both capital and operational expenditure for the businesses.
  • Storage Capacity: Let’s start with AWS. In the storage front, AWS can handle a business’ current needs and allow future growth as well. The storage capacity is practically unlimited. AWS saves businesses from crippling data storage limits. The distributed cloud storage also makes the data less vulnerable to malware or cybersecurity issues. More on that later. These storage services offered by AWS are very well handled by the AWS Storage Gateway. For the same context, Azure offers a platform called the Azure Storage platform. This cloud storage solution has been built to handle new-age software needs like scalability, virtual machine handling, communication data, etc. Azure storage is meant to offer qualities like – durable storage, high availability, data security, easy accessibility, and more
  • Security: Security is front and center for both services. Azure, for instance with its multiple compliance certifications, is deemed safe for even high-risk industries. That is the reason, many governments and healthcare organizations adopt it for cloud services. With features like network security, key logs, multi-factor authentication among others, Azure ensures that both the business services and the end-users are safe. Security with AWS is about safe information exchange, trusted infrastructures, etc. AWS has multiple data centers spread out globally. The business and end-user data and processes are all safe and secure. AWS offers security features like Identity Access Management, Cloud Trail, S3 security, etc. All of these come together to relieve businesses of all their security concerns.
  • Agility: The core idea behind business agility is faster development and provisioning for future changes of direction. With practices like DevOps, agile principles are wedded to automation and cloud services. Obviously, it makes sense for two of the most popular cloud services to provide agile features from the get-go. AWS, for instance, offers all its resources at one stop. This makes development faster and more agile. With its storage capabilities, security assurance, and reliable infrastructure, AWS can encourage even conventionally non-agile organizations to attain agility at scale. Azure too has been allowing businesses to evolve from their legacy processes and bring business agility with a move to the cloud. Applications can be built, managed, and deployed in minimal time. Both AWS and Azure support DevOps along with technologies like Microservices, Containers, Serverless, etc. This means that even for more futuristic products and services, provisions for agile development are already in place.
  • Affordability: With all these services, both AWS and Azure can help businesses achieve acceleration. In addition, even for start-ups, they can prove to be very cost-effective. AWS offers infrastructure on demand. This means that the business can use whatever resources they need for however long they need them. Azure is capable of scaling its services and resources as per the business size and needs. The pay-as-you-go price model allows small scale businesses to employ only the required services and resources. In fact, both AWS and Azure, encourage businesses to optimize resource utilization. They offer the preferred Operating System, technology stack, network management, etc. at an affordable price and 100% brand reliability from Amazon and Microsoft.

Conclusion:

Amazon Web Services and Microsoft Azure have been giants in the cloud-computing game. Both have vast experience and committed customer bases. There is also the trust that comes with the Microsoft and Amazon brands. Both cloud solutions are committed to helping businesses of all scales and sizes to evolve at a steady pace. The cloud adoption choice has never been easier for organizations to make. The hard part is separating the vendors. That often comes down to the specific features you want to use and the commercial deal available to you at that time. One thing is sure. AWS or Azure are both proven solutions.

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