devopsy.kinsta.cloud

Tag: deployment

  • Improvement Cycles and AI

    Improvement Cycles and AI

    It’s that time of year when pundits start to crank out their predictions for the next twelve months! I’ve been asked to contribute to a private group (and occasionally, Techstrong Group asks me to contribute to their annual list), so I won’t thrill you with my predictions just yet; but I will discuss the cycles and trends that previous predictions have shown.

    Back when I was testing physical servers, we thought of throughput bottlenecks in terms of where the problem was. The circle would go disk->memory-> CPU, and in the case of things like NAS, insert network in there somewhere, too. When one bottleneck was resolved, another would crop up. And users will always seem to be pushed to the next bottleneck.

    We see similar cycles in DevOps. First, Agile made development faster, and that caused us issues throughout the rest of the process. Then, DevOps helped with the build and a little with the testing and deployment steps, but in the early days it was more standardization than speeding up. And since then, we have been in that cycle. We’re seeing GitOps speed up and automate deployment and upgrades—based upon the automation that came before—we’re seeing test and security tools feeding directly into the IDE to speed resolution, we’re seeing container automation cause “cloud-first development” (I hate that name; Kubernetes has cloud-like interfaces, but is not precisely cloud) to be fully automated, and thus faster.

    And we’re looking at that cycle continuing. No doubt you will hear a lot of predictions that start with “AI …” For the most part, AI is a tool and/or technique. Listen for what it is improving because it is doing very little that’s actually new. (Delete list of things that sounded alarmingly like those predictions I said I wouldn’t make.)

    All in all, we are still living in a very cool technological time. We can have a tool generate code snippets of the dull work for us so we can focus on the important bits. Some shy away from this use of “AI,” but honestly, we already use libraries to do the grunt work—I haven’t written to a screen buffer in forever—because we don’t have to. Same story—use the tools that make you productive. Some of the more obscure bits of Linux command lines are far better dealt with by asking an AI to give me examples than searching through tons of docs across several platforms. Never was much of a fan of man output for complex commands, this makes it easier. The nice bit is that bash and UNIX utilities don’t change much over time, so answers can be a bit dated and perfectly accurate. That’s not true for much of IT.

    The environment has consistently gotten more complex, while the building blocks have gotten easier to use. This allows things to keep moving and changing while it offers us better ways to serve the organization’s needs. This does change at some point—infinite change is indeed a thing, but any technology does have its limits—but I honestly can’t see the point at which we are content with our IT tools and stop looking for more. Oh, most organizations go through those cycles—organizationally, once an infrastructure and process works correctly, it is likely to hang around until better is found—but the industry will keep changing.

    And you’ll keep learning and changing. That’s one prediction I will make for next year. Teams and IT staff will grow and learn new things, because at its heart, that is what we do. We learn new things and keep the lights on. In good years, we offer sweet new tools and functionality to the company or our customers. But either way, we keep things online. Year after year. You all rock, so that’s an easy prediction.

  • OpsMX Adds Deployment Firewall to Ensure Compliance

    OpsMX Adds Deployment Firewall to Ensure Compliance

    OpsMx today added a deployment firewall to its Deploy Shield portfolio for securing continuous integration/continuous delivery (CI/CD) platforms.

    The deployment firewall automatically applies rules to ensure DevOps teams comply with mandates such as the payment card industry data security standard (PCI DSS) or the Health Insurance Portability and Accountability Act (HIPAA). It then evaluates a release against a checklist of policies and blocks any deployments into a production environment unless a specific exception has been defined. In those instances, DevOps teams can also set an alert to address that issue at a later date.

    In addition, DevOps teams can extend those rules defined by OpsMX to address specific compliance requirements.

    The checklist includes making sure vulnerability scams have been run, tracking when the manifest was last updated, confirming that operational controls are being followed, checking whether images have been modified and evaluating the infrastructure being employed and performing an analysis of the performance impact.

    A simulation feature also allows developers to check their release for compliance before it is time to deploy.

    OpsMx CEO Gopal Dommety said the goal is to streamline the effort that is required to meet multiple compliance mandates as the pace at which applications are being developed and deployed continues to increase.

    Today there are simply too many data silos for DevOps teams to manage, with each one presenting yet another opportunity to run afoul of a compliance mandate, he added. The overall goal is to reduce the cognitive load being placed on developers as more responsibility for compliance is shifted left, noted Dommety.

    The deployment firewall currently supports DevOps platforms such as Jenkins, Argo and Spinnaker, with support for GitHub Actions and GitLab to follow.

    OpsMX claims its overall customer base has increased by more than 60% in the first half of this year, with customers including Google, Cisco and Western Union. In addition to Deploy Shield, the company provides a Delivery Bill of Materials (DeliveryBOM) tool to capture a consolidated record of every step in the application software delivery and deployment process. Security checks, approvals, policy enforcement and audits are tracked via one tool. In addition, the company provides tools for managing the deployments of software via either the Argo or Spinnaker CD platforms.

    It’s now only a matter of time before various regulations that apply to the building and deploying applications become more stringent. Tolerance for sloppy application development processes is dropping as legislative bodies increasingly determine that software development practices are a big cybersecurity problem that threatens national security and the global economy.

    Organizations that fail to comply with those regulations will be subject to even heftier fines than the ones that already exist. In many cases, that may slow down the rate at which applications are deployed unless DevOps teams find ways to manage compliance-as-code more effectively as part of a larger DevSecOps workflow.

    When it comes to DevSecOps, progress tends to be slow in most organizations, but as more tools become available to automate various tasks, it’s now only a matter of time before software supply chains are more tightly secured. Hopefully, the pace at which those advances are being made will occur faster than cybercriminals can exploit the vulnerabilities that today are too numerous to manually track.

  • Raise Those (Feature) Flags

    Raise Those (Feature) Flags

    I’ve written about feature flags before, but I think it’s time for a solid bit of advice: “If you are not yet using feature flags for DevOps, it is past time to reconsider.”

    I say this because feature flags have been a slow-growth item, with a few spikes here and there in usage, but mostly on the development side. While many of you are using feature flags to do dynamic toggling of features–definitely an advanced use case, but still very development heavy–few organizations are using them for deployment options–blue/green without separate code, environment changes for different targets (deploy this to the cloud provider, but do this for local installation type stuff), etc.

    Feature flags are starting to be supported by DevOps tooling for this precise use case, and it is glorious. While the ability to say, “run this code,” and if the new code fails, change something like an environment variable to say, “stop running that bit,” is huge. We have enough time with this process that the industry is well aware of the need to clean up as soon as the correct code options are known, and that is the biggest weakness; it makes spaghetti code that must be cleaned up. But the benefits are well worth a bit of cleanup at the end of a project, even if far too many of you don’t bother.

    Now we can use them in deployment, and in full life cycle toolsets, we have them available for both–so a set of source code can be gated behind a feature flag and deployment can use the same feature flag. This enables the ability to say, “If our FF environment variable is ‘TestDeployment,’ connect to ‘Test Database’ … then use the same variable to say, “If our FF environment variable is ‘TestDeployment,’ spin up the container with test data instead of real data.” This simple example is something that could be (and has been) done manually, but doing it this way makes it part of the process. Automated testing can then follow without the need for human intervention, and from “commit” to “test results,” IT can focus on other things instead of spinning up the right containers and making sure the right DB is accessible.

    After all, we are in a time of massive automation, which should not worry us. There are globally too many IT positions and too few people to fill them; automation should be exercised as much as possible to address that simple fact. And there is a good chance that you are already using tools in the DevOps toolchain that support feature flags–a growing number do support them. If not, it is easy to research and find a good tool to do so and while it doesn’t offer as much functionality, a stand-alone feature flag product will still offer more options than not having any.

    And keep rocking it. The applications you wrote years ago are still powering the enterprise, even if you are at a different organization today. We leave a legacy of applications behind us that is a testament to quality. Keep leaving quality, and use all of the tools you reasonably can.

  • The Differences Between DevOps and GitOps

    The Differences Between DevOps and GitOps

    The transition to a DevOps model has been welcomed by many organizations that are looking to implement more efficient development processes. However, there are multiple models to choose from, with GitOps a popular choice for many. So what is the difference, and which one is better?

    In this article, we will discuss what each model entails and their key differences and establish if one is better than the other for your business.

    What is DevOps? – An Overview

    The DevOps model involves several tools and practices designed to make software development much more efficient. This increase in efficiency is achieved by implementing more automation, adopting continuous delivery practices and improving the communication channels between the developers and operations. 

    Ultimately, DevOps is designed to assist organizations in deploying new products and systems faster. By establishing more collaboration between development and operational teams, changes can be made quickly to best suit the business’s overall goals. 

    There is no single tool that is required for DevOps. In fact, your team can use any tool that effectively improves communication and automates key tasks. 

    How to Integrate a DevOps Strategy

    Adopting and integrating a DevOps strategy into your organization’s overall workflow is not a difficult process, and it can be achieved by following these steps: 

    1. Create a continuous integration/continuous delivery (CI/CD) pipeline.
    2. Configure this new pipeline to enable the automated deployment of changes to your development/production environment.
    3. Set up an alert and monitoring system for your development environment. 
    4. Create collaboration channels so operational teams can inform the developers of new opportunities to improve the business’s workflow. 

    An Overview of GitOps

    GitOps is a modern concept that was initially introduced by the Kubernetes management system Weaveworks. This model uses Git, an open source, distributed version control system, to track any changes to a system and who has made them. 

    The GitOps model uses a Git repository as the single source of truth (SSoT), improving collaboration between developers and operational team members. This, in turn, allows organizations to automate the deployment process. 

    By leveraging CI/CD pipelines like DevOps, changes are automatically applied to the codebase without any need for manual input. Thanks to Git, these changes can also be easily reversed if needed, reverting to a previous working version. 

    How to Integrate a GitOps Strategy

    GitOps can be integrated into a business’s workflow quite simply by following four key steps: 

    1. Set up a Git repository for your organization’s codebase. When integrating a GitOps strategy, an organization does not need to use GitHub exclusively, as any code repository that uses Git commands can do the job. 
    2. Connect this repository to a CI/CD tool.
    3. Configure your chosen tool to deploy automatic changes to your development environment. Popular tools include Jenkins and Circle CI. 
    4. Set up monitoring and alerts for your development environment. 

    It should be noted that the CI part of GitOps still needs to go through your DevOps tool, whether it’s GitHub Actions or AWS CodeBuild. 

    The Differences Between DevOps and GitOps

    The core difference between the two is that GitOps allows teams within an organization to manage and deploy changes to infrastructure using Git as the single source of truth. DevOps, on the other hand, focuses on effectively connecting development and operational teams using a tool of the organization’s choosing. 

    Either model can offer a number of benefits in terms of development and deployment, but there are notable differences between the two that should be considered. It should also be noted that DevOps and GitOps can work in unison as long as continuous integration (CI) is delivered using your chosen DevOps tool.

    DevOps – Key Elements

    • The DevOps model is considered a working culture.
    • DevOps can use a variety of CI/CD pipelines and is not restricted to any one tool.
    • This model increases automation and the regularity of deployment, reducing any human input. 
    • Cloud configuration can be used for coding, supply chain management and other key requirements.
    • The pipeline separates development and operations processes while improving collaboration between the two.

    GitOps – Key Elements

    • GitOps is a development and deployment technique that relies on the Git version control system.
    • Git is used as the single source of truth for all changes to the codebase.
    • GitOps can use Kubernetes and infrastructure-as-code (IaC) practices and various other CI/CD pipelines. 
    • The GitOps model is designed to increase development times and reduce reliance on lengthy scripts.
    • It also enables a smoother transition between a project’s development and operation stages.

    DevOps Vs. GitOps – Which Model Is Better?

    Both GitOps and DevOps are becoming increasingly popular when it comes to adopting new approaches to improve software development processes and making deployment quicker and more efficient. As outlined above, the difference between the two models largely comes down to the tools that are used, but the result is roughly the same in terms of the overall benefits. 

    Deciding which approach is the right one for your organization or determining which one is “better” will always come down to the needs and requirements of your organization. In fact, the ideal scenario would be to integrate GitOps so it works alongside your DevOps approach. 

    GitOps is not a replacement for DevOps, as the CI process still relies on DevOps tools. However, GitOps can improve the effectiveness of the DevOps team thanks to continuous delivery processes. Using Kubernetes-native mechanisms and commands and sticking to standard source code principles, GitOps does not require developers to change how they work, guaranteeing a seamless implementation process.

    Wrapping Up

    DevOps is a working culture that improves collaboration between development and operations teams and utilizes automation tools, resulting in rapid and more efficient software development. GitOps, on the other hand, uses Git as the single source of truth, making it easy to track changes and further increase development times thanks to continuous delivery processes. 

    Although each model can be used exclusively, they can also work alongside each other to deliver rapid software development and high levels of collaboration. Therefore, it is unfair to claim that one model is better than the other, as they can both play their part in an overall DevOps strategy. 

  • 4 Ways to Uplevel Your Software Engineering Team

    4 Ways to Uplevel Your Software Engineering Team

    In my 20 years leading software development teams, I’ve witnessed the industry’s continuous growth alongside technology’s evolution. The only thing we know for certain is that the industry will continue to change and winning businesses will be those that build with change in mind, backed by effective, fast-moving software teams.

    This is the year we must focus on fine-tuning our DevOps processes and uplevel our teams, but where do we start?

    Below are four ways you can begin to transform your teams into engineering powerhouses, no matter how big or small or where they are in the world.

    1. Embrace New Delivery Models

    Remember when deployments were a big deal; a highly-anticipated event that often meant late nights? These days, many teams have managed to make deployments a non-event and are deploying any time of day. Those that haven’t adapted risk being left behind. The rate of deployments will only increase, so how teams handle this progression will be a deciding factor for success in an already competitive field.

    In 2022, teams should continuously evaluate their procedures as they push the boundaries of their tech stack. Navigating the complexity of the digital era—from massive datasets and machine learning to networks of third-party services—will force both developers and company leaders to embrace flexible tooling and processes to ultimately refine the risk/innovation tradeoff.

    2. Prioritize People and Culture

    While 2021’s Great Resignation saw many software professionals jumping ship in search of a fresh work environment, in 2022 we expect to see a Great Reshuffle. Realizing their new or current gig still may not be quite the right fit, engineers and developers will start looking for roles that are more in line with their long-term career and personal goals.

    Don’t fear this reshuffle. Instead, recognize it as an opportunity to build a strong and stable team. Be honest about what, exactly, the job entails and the hard and soft skills you’re looking for. Honesty throughout the recruitment process leads to a better match between employer and employee and also contributes to resilient company culture.

    3. Deploy Low-Code Solutions Strategically

    Pressure to get the highest productivity out of software development teams has led to the adoption of no-code and low-code solutions. But that means these tools are being used predominantly by people who already know how to code. The folks with big ideas who don’t actually want to be programmers remain unserved and untapped.

    Expect this disparity to soon lead to a split in the category of no-code and low-code solutions—those built specifically for optimizing developer teams, and those that can serve everyday creators. To really get the most out of your team, keep an eye on the low-code space and ensure the solution you use is the one that’s actually best suited for your goals.

    4. Build With Change Validation in Mind

    The growing adoption of cloud storage, service-oriented architectures, third-party API-based services and open source code (to name a few) makes modern software development monumentally complex.

    A way to manage this complexity is by pulling our focus away from the application itself and onto myriad sources of change that affect the application—a concept we can think of as change validation.

    Ultimately, change validation builds upon tried-and-true software best practices such as continuous integration, continuous delivery and release orchestration. Cloud-native architectures can be effective and scalable, but you need to have the right tools and systems in place to validate changes to your code at every step.

    In the past, the general consensus was that you shouldn’t test in production; you should just release and wait for the bug reports to tell you what’s wrong. But in the world of extensive software supply chains and cloud-native architectures, testing in production is the only way to truly know whether changes to your code are working as intended before releasing software to customers.

    In the new year, software developers will be of critical importance as organizations continue to expand on digital services and projects. Embracing new methods of software delivery, prioritizing people and culture and keeping an eye on how to best leverage automation tools like no-code/low-code and change validation will all help to transform your team for the better in 2022. This forward-looking approach and a willingness to reassess and pivot guarantees that your software engineering team will remain agile amidst a rapidly changing tech landscape.