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Tag: devops

  • The Rise of NetDevOps and CI/CD Pipeline Solutions

    The Rise of NetDevOps and CI/CD Pipeline Solutions

    Over time, as organizations, networks and technologies have changed, the way enterprises think about managing networks has shifted as well. Enterprises are continuing to evolve their networking strategies, specifically with regard to how they expand their efforts to adopt infrastructure-as-code (IaC) practices. Looking ahead, the industry will continue to see the proliferation of solutions that help teams adopt a modern network operating model that delivers an agile, data-driven change management process. In most organizations, the software development team members and the networking team members have operated independently, with separate tools and processes. Developers would leverage pipelines to test and deploy code and then open a ticket for network changes such as VPNs or firewall changes. The network team would implement the changes, and end users could then use the application. The handoff between dev and network could be immediate or, more likely, it could take a few days–delaying the ability of the end users to access the new functionality. NetDevOps is focused on addressing this situation, which has become one of the market’s more critical infrastructure questions: How do you build an organization that can function smoothly across different skill sets, different technologies and different processes? Those same enterprises will be looking to leverage continuous integration/continuous delivery (CI/CD) practices from their software development best practices to help them overcome some of the challenges involved with implementing automation and orchestration into their networks.

    CI/CD Accelerates the Automation Process

    Let’s start with CI/CD. Adoption of network automation and orchestration will accelerate as network operators change the way they think about network updates and configuration management. According to Cisco’s recent white paper on how “Automation is driving network engineer skills transformation,” the reasons we’re seeing an increase in automation adoption is due to organizations relying on continuous connectivity, a greater need for the network to become more dynamic and the increased rate of changes both in the service supply side (applications) as well as in the service demand side (application consumers).

    However, some enterprises trying to build their own automation infrastructure struggle with resourcing, skill sets and maintenance effort struggles. To solve this, many of them are looking to incorporate the traditional DevOps practice of building and updating code with traditional continuous integration/continuous delivery (CI/CD) pipelines into their network automation and orchestration initiatives.

    Establishing a CI/CD pipeline is a workflow best practice for DevOps teams to follow. Today, organizations rely on CI/CD pipelines to help minimize bottlenecks in the software development and delivery process, while continuous integration optimizes building, integrating and testing code within a development environment.

    By integrating both CI/CD with network automation, teams will gain greater velocity and improve delivery. And by integrating network automation with an application CI/CD pipeline, they can reduce the time to perform network changes and enable secure access between the application and an in-house sales database.

    Why NetDevOps?

    NetDevOps brings the culture, technical methods, strategies and best practices of DevOps to networking.

    When DevOps principles are applied to network operations, organizations minimize operator interactions and optimize programmable network processes to configure, deploy and manage the environment, increasing efficiency and ensuring network availability, quality and reliability.

    NetDevOps takes those principles and applies them in a networking environment, offering tools to empower teams to collaborate and build an organization that can function across different skill sets and different processes — enabling everyone to participate in network orchestration.

    And it’s a movement that’s taking hold. According to Cisco’s Hybrid Cloud Trends report, 55% of those surveyed have created a cross-functional team with technical and business representation and 50% have a centralized CloudOps and NetOps function to help ensure their organization’s hybrid cloud strategy meets business objectives.

    Network Management Tools Will Continue to Evolve

    In today’s networking environment, where the expectation is to have an “always on” approach, teams are expected to build and release new applications at a faster pace than ever before. And every team has its own processes and methods for doing that which can create delays in the time it takes to execute the application. Incorporating network automation into the pipeline can empower teams to make application changes and deploy them to the network in seconds instead of the multiple days/weeks required for manual network changes. By integrating CI/CD pipelines and NetDevOps principles with network automation and orchestration, organizations can reduce the network change bottleneck even more.

    As we face the new year, we expect the way in which teams consume network automation and orchestration services as part of their software pipelines to become more sophisticated, with NetDevOps and CI/CD practices becoming a standard. And as more services become available, the adoption of IAC becomes easier. By abstracting and tweaking to abstract the traditional CI/CD pipeline, organizations of all sizes can accelerate the use of network automation and orchestration.

  • AI Chatbots: Game Changers for DevOps Transformations

    AI Chatbots: Game Changers for DevOps Transformations

    In today’s fast-paced digital world, organizations are constantly looking for ways to accelerate their software development processes and ensure the seamless integration of their development and operations teams. DevOps has emerged as a popular methodology to achieve this goal, but even with its adoption, many companies still face challenges in their transformation journey. AI chatbots can revolutionize DevOps transformations, making them more efficient and effective than ever before.

    How AI ChatBots Improve DevOps Transformations

    Enhance collaboration by automating routine tasks and facilitating communication between teams: AI chatbots can handle repetitive tasks, freeing up team members to focus on more critical issues while also providing a centralized communication hub for both development and operations teams.
    Expedite incident management by providing real-time alerts and smart recommendations: AI chatbots can analyze logs and system metrics to quickly identify issues and suggest the most appropriate resolution steps, reducing downtime and improving overall system performance.
    Streamline deployment and monitoring processes using AI-driven automation: AI chatbots can intelligently manage deployment pipelines, ensuring that the right code gets deployed to the right environments while also monitoring system health and performance in real-time.
    Offer real-time analytics and insights into the development pipeline for informed decision-making: AI chatbots can gather and analyze data from various sources, providing valuable insights that help teams make better decisions related to code quality, release schedules and resource allocation.
    Identify skill gaps and recommend targeted training resources for continuous improvement: AI chatbots can analyze team performance and individual skill sets, helping organizations identify areas for improvement and providing personalized learning resources to close those gaps.

    Roadmap to Implement AI Chatbots in DevOps Transformations

    Evaluate your organization’s current DevOps maturity and identify areas for improvement: By assessing your existing processes and infrastructure, you can better understand the gaps where AI chatbots can make the most significant impact.
    Select an AI chatbot platform that aligns with your organization’s technology stack and goals: Choose a platform that complements your existing tools and supports your specific DevOps objectives to ensure seamless integration and adoption.
    Define and prioritize use cases for AI chatbot implementation in your DevOps processes: Identify the key areas where AI chatbots can provide the most value, such as incident management or deployment automation and prioritize their implementation accordingly.
    Develop a phased implementation plan that includes integration, testing and deployment of AI chatbots: Create a step-by-step plan to integrate AI chatbots into your existing DevOps processes, ensuring proper testing and validation at each stage to minimize potential disruptions.
    Train your teams on how to leverage AI chatbots effectively and continually refine their usage based on feedback and performance metrics: Provide education and training resources to help your team members understand how to use AI chatbots to their full potential and foster a culture of continuous improvement.

    Benefits of AI Chatbots in DevOps Transformations

    AI chatbots offer several benefits that can significantly improve DevOps transformations, including:

    Increased efficiency by automating manual tasks and streamlining processes: This leads to faster development cycles, quicker releases and a more agile response to changing business requirements.
    Enhanced collaboration and communication among development and operations teams: AI chatbots can break down silos between teams, fostering a more collaborative and unified approach to software development and delivery.
    Faster incident resolution and reduced downtime: With AI chatbots’ help, issues can be identified and resolved more quickly, minimizing the impact on end users and maintaining a high level of service quality.
    Real-time insights into the development pipeline for more informed decision-making: AI chatbots enable teams to make data-driven decisions by providing real-time analytics and insights into various aspects of the development process, such as code quality, resource allocation and release timelines.
    Continuous learning and improvement through AI-driven recommendations: By identifying skill gaps and suggesting targeted training resources, AI chatbots can help teams continuously improve their knowledge and expertise, ultimately leading to better performance and more successful DevOps transformations.

    Summary

    In conclusion, AI chatbots are poised to revolutionize DevOps transformations by addressing common challenges, enhancing efficiency and fostering a culture of continuous improvement. By automating routine tasks, facilitating communication and providing valuable insights into the development pipeline, they can significantly improve the effectiveness of DevOps processes. As organizations continue to adopt and refine their DevOps strategies, incorporating AI chatbots into their workflows will become increasingly essential to remain competitive in the ever-evolving digital landscape.

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.

    Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.

    Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.

    The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.

    We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:

    Monster.com

    Ace American Insurance Company (Chubb)
    Philadelphia, Pennsylvania
    DevOps Engineer
    $157,477 to $172,000

    LinkedIn

    DRISHTICON
    Mountain View, California
    Senior DevOps Engineer
    $120,000 to $150,000

    Dice.com

    Srivango Technologies
    Cary, North Carolina
    Java Developer with Azure and DevOps Expertise
    $120,000 to $140,000

    Indeed

    The Coca-Cola Company
    Atlanta, Georgia
    Cloud DevOps Engineer
    $102,00o to $109,000

    SimplyHired

    Moody’s
    Remote
    DevOps Engineer
    $90,000 to $159,300

  • Turning Software Testers Into 10xers

    Turning Software Testers Into 10xers

    How do you make technical processes faster and more scalable? If your answer is simply to automate them, you’re stuck in the 2010s. Today, automation is widespread, and automating processes in a basic sense is no longer enough on its own to maximize efficiency and velocity. Instead, teams who want to excel–who want to become 10xers, so to speak–need to look beyond automation in its simplest form. They must take automation to the next level in a way that makes processes not just a little faster but orders of magnitude faster. Allow me to explain by discussing how software testers can put this principle into practice.

    The Limitations of Basic Test Automation

    When I talk about “automation in its simplest form,” I’m referring to automation that helps to execute core processes.

    In the context of software testing, basic automation means using frameworks like Selenium or Appium, which provide a way for engineers to execute tests automatically. That saves a lot of time and effort compared to executing each test manually.

    To be sure, there’s nothing wrong with simple automation. But the fact is that by now, most teams have caught on to the value that test automation provides. Automatically executing most of your tests no longer makes you much faster than average. It might have been a big deal a decade ago, back when test automation was less widespread, but it’s hardly enough to make you a 10xer today.

    Going Beyond Simple Automated Testing

    What does set you apart? What can testers do to supercharge their efficiency and velocity?

    The answer, again, is to take automation to the next level. Rather than simply using test automation frameworks to execute tests, they can also use automated tools to generate the tests in the first place.

    For example, modern automated testing solutions can automatically create test scripts based on actions that testers perform manually, making it possible to generate tests without having to write any code. That means you can create many more tests in much less time.

    As another example, next-generation automation can supercharge the interpretation of test results. If engineers want to know, for instance, when a screen finishes rendering in an application or when a crash occurred, they don’t have to parse test results manually, or write custom scripts, to find that information. They can leverage modern automation tools that collect that data on their own.

    In a similar vein, modern test automation tools can accelerate the process of troubleshooting tests that fail to run. For example, imagine that an automated test fails because it is supposed to test an element that developers relabeled in the newest version of an application. Traditionally, fixing that issue would have required engineers to review the test, sort through application code and then update the test with the new element ID. But this process could instead be automated using tools that can identify the application elements that a test should assess and update test scripts with the appropriate element IDs, all without requiring manual intervention.

    A Copilot-Inspired Approach to Software Testing

    You could draw analogies between the types of opportunities that modern test automation software makes available to testers and those that AI-powered tools like GitHub Copilot have opened up for software developers.

    If you’re a software developer today, you don’t have to settle for basic automation tools, like continuous integration servers (which automatically integrate code into larger codebases) or source code management platforms (which automatically track changes to code over time). You can also benefit from a new breed of automation tools, like Copilot, which can automatically generate code, unit tests and much more.

    For developers, Copilot is a prime example of a next-generation tool that can dramatically accelerate software development. Developers who learn to use Copilot effectively can work potentially ten times faster than their counterparts.

    The types of next-generation test automation solutions I’ve described above do the same thing for software testers. Instead of relying just on basic automation tools to run tests, modern testers have a host of additional, more powerful automation at their disposal to help create, manage, interpret and troubleshoot tests. Those who learn to use those tools will become the 10xers of today’s generation of software testers.

  • The Now and Next of Automation

    The Now and Next of Automation

    Today’s never-ending flood of data has many of us drowning in noise. And while humans are still essential for thoughtful analysis and driving action, it’s no longer possible for a human to parse gigabytes of data alone.

    How do you begin to filter out the noise and then understand what to do with it? How can you get to the root cause of an issue so you can act on and resolve it? Most of all, how can you do all of this while being more productive and driving more innovation instead of constantly putting out fires?

    Automation is the obvious answer. For many organizations, automation isn’t just about efficiency – it’s about survival. Automation can make organizations more agile, resilient, and capable.

    Where you sit within an organization or team, however, often dictates how automation can help with your specific goals or alleviate your immediate pain points. Mandi Walls, a DevOps advocate with PagerDuty, offers some advice on how even less experienced teams can use automation to perform simple tasks, solve problems and improve the work environment.

    We hear a lot these days about A.I. and machine learning, or tools like Chat GPT which to some is a harbinger of robots finally replacing humans. Putting the hyperbole aside, where do you see automation today? Is it still a “nice to have” for engineering teams or a “must-have” to do their jobs effectively?

    Automation is definitely a “must-have.” Manual processes are risky; they can inadvertently introduce too many errors, especially in complex environments. The scope and complexity of modern technical ecosystems is just too vast for teams to effectively cope with using only manual methods.

    This is especially true for teams that develop and manage customer-facing services. Having a successful product leads to more customers, which requires more capacity and creates more complexity. To deliver the kind of performance customers expect, teams need to be fast and efficient. That’s where automation comes in.

    What are some specific types of automation that engineers and IT pros should be aware of?

    Some automation has become more or less ubiquitous, such as tools that enable configuring and building large numbers of systems with little human intervention. Other processes might feel less like “big-A” automation–for example, continuous integration/continuous delivery (CI/CD) pipelines are automation.

    Humans also aren’t manually building many software packages anymore. The use of automation in the software testing process has greatly expanded in the last several years, changing the jobs of QA engineers significantly.

    Is self-service automation – aka true autonomy that doesn’t require human intervention – the ideal end state for a digitally mature organization? When is the best time to jump in?

    Organizations that wait for a perfect opportunity to automate their processes risk wasting a lot of time and resources in the interim. Teams can look at any of the tasks that they regularly perform and assess them as candidates for automation.

    Tasks can be assessed in a number of different ways, but for teams just starting out with automation, seeking tasks that are low complexity and low impact is a strong starting point. Even if we look just within the realm of tasks that might be utilized during incident response, teams might have any number of tasks related to information gathering, log preservation, system diagnostics, and other telemetry that responders run for every incident. There’s no reason to require humans to run those well-understood tasks!

    As teams get more comfortable with creating and using automation, they can seek out more complex tasks to automate. And it’s entirely possible that there will be tasks that teams aren’t able to automate fully! Those tasks might be too complex, require a lot of human input or interaction or have too many edge cases for the team to create automation for.

    Are the principles of automation the same in simple versus more complex environments like Kubernetes?

    The principles are essentially the same, but the constraints and tools may be different. In some environments, a simple shell script might be enough to accomplish our goals. In other environments, we might need to make use of APIs or vendor SDKs to do what we want.

    On that topic, is self-service automation in cloud environments the preferred path?

    Actually, self-service is sort of how the cloud really got going! Development teams armed with nothing more than a credit card got the environments they needed without going through lengthy procurement processes.

    Now that cloud is a critical part of many workloads, organizations are putting more rigorous guidelines in place. The cloud platforms are also much more complex, with more service offerings than in the early days, which makes automation more important than ever. Automation helps organizations ensure that their cloud environments meet their security and compliance requirements, helps manage spend and still provides enough flexibility for all teams to get work done. And with self-service automation, developers can request an environment in the cloud to develop their service and have that service meet all the organization’s standards without having to keep up with all the details themselves.

    What about toil? Can that be fully eliminated through automation, or is some toil still necessary?

    I’m not sure I’d say toil is necessary, as much as it tends to be inevitable. There will likely always be some boring, repetitive tasks that are too unpredictable or unstable to automate well.

    That said, teams certainly don’t need more toil; having too much repetitive work tends to lead to less job satisfaction and higher attrition rates, so teams should be looking to minimize as much toil as possible where they can.

    Is there one myth or misconception, either about automation overall or self-service automation in particular, that you’d like to clear up? What’s the reality?

    First of all, don’t worry. You won’t be able to replace your team with a set of shell scripts! Seriously though, automation isn’t about “doing more with less.” It’s about doing more with the same. Automation is an approach to help teams cope with the day-to-day running of complex environments and will need to be managed as part of those environments. Managers need to make sure they are allotting time for the creation and maintenance of automation in the same way they would for other tools and applications.

    Interested in learning more about PagerDuty? Stop by Booth S49 at KubeCon to speak with an expert or see a demo!

  • How Platform Engineering Makes Software Sustainable

    How Platform Engineering Makes Software Sustainable

    The world is constantly becoming more and more aware of the importance of sustainability in every aspect of our daily lives from buying groceries to traveling, and the IT world is no exception. Indeed, sustainability is already gaining importance in software development. For example, in the latest version of Thoughtworks’ Technology Radar, “carbon efficiency as an architectural characteristic” is one of the new techniques of interest. Moreover, in its research “Predicts 2023: Environmental Sustainability Is Now an IT Sourcing Imperative,” Gartner states that “by 2026, 75% of organizations will increase business with IT vendors that have demonstrable sustainability goals and timelines, and will seek to replace vendors that do not.”

    This shows that there is a growing need for sustainability. How is the software development industry responding to this need? Many great initiatives are arising, but platform engineering, in particular, helps make software more sustainable.

    What is Platform Engineering?

    According to Gartner’s definition, platform engineering is “the discipline of building and operating self-service internal developer platforms (IDPs) for software delivery and life cycle management.” This trend has gained momentum in the last few years, and an increasing number of organizations are adopting internal developer platforms (IDPs) to scale their development process. IDPs allow developers to self-service the technology they need, whether it is infrastructure, runtime resources or ready-to-use services. This helps speed up the development process and ensures higher-quality software.

    Platform engineering is becoming more and more popular and foundational. According to Gartner, “by 2026, 80% of software engineering organizations will establish platform teams as internal providers of reusable services, components and tools for application delivery.” Therefore, given the expected widespread adoption of IDPs in the next few years, sustainability emerges as a requirement. Ignoring or underestimating the importance of sustainability may cause organizations to waste all the benefits ensured by an internal developer platform.

    Platform Engineering for Economically Sustainable Software

    To survive and operate properly in the market, a company has to be economically sustainable and therefore stable. Usually, software is an asset that contributes to revenue generation for the company, but it still has maintenance costs. To make software economically sustainable, the total cost of ownership (TCO) needs to be lower than the generated income.

    An internal developer platform can lower software product costs, making them more sustainable. How so? In a nutshell, by sharing resources that can serve multiple products and can be reused across multiple projects. Such resources include, but are not limited to, capabilities, processes, infrastructure, configuration, etc. Since the platform at scale manages shared capabilities, there product teams won’t waste time and effort replicating it every time. While lowering the TCO, this characteristic of platforms also contributes to reducing the software’s time to market.

    Moreover, platform costs are shared across the whole organization and are not part of the TCO of each software product. This helps software to be more sustainable from an economic point of view.

    Platform Engineering for Environmentally Sustainable Software

    When talking about sustainability, most people usually think about the environment. While respecting the environment is one of the most important factors, it is important to note that sustainability is not limited to this aspect.

    An internal developer platform can help software be more sustainable on two levels.

    • Observability: The platform should collect and easily expose metrics on the environmental impact of the software. Such metrics can include hardware resource usage, energy consumption and CO2 footprint.
    • Optimization: The platform should foster the adoption of sustainable patterns, like the ones provided by the Green Software Foundation. It should also include solutions that actively help reduce energy consumption, such as kube-green.

    It is important that both of these capabilities are present and implemented in the platform. Observability without optimization is useless, and optimization without observability cannot be done.

    Platform Engineering for Socially Sustainable Software

    When speaking about sustainability, social responsibility cannot be forgotten. The first social responsibility of each organization is toward its members and employees. Does developer self-service also have social implications? Yes, it does; mainly by improving the developer experience.

    Indeed, an internal developer platform helps organizations’ members to:

    • Reduce friction between teams: Since everyone can have everything they need right when they need it, there will no longer be bottlenecks and misunderstandings between different teams. This contributes to a better working environment where everyone feels welcome and more productive.
    • Decrease repetitive tasks and cognitive load for developers: Since the platform will manage and automate interactions with infrastructure and runtime, developers will no longer need to waste time and energy on such out-of-scope secondary tasks. This way, developers are more focused on their actual job of developing new features and they will feel more satisfied.
    • Enable work from anywhere at anytime: Since the platform can provide all technology and capabilities whenever a developer needs them, it will no longer be required that people work together in the same location and at the same time. In other words, IDPs enable remote and asynchronous ways of working. This allows organizations to offer remote working and to create distributed teams, ensuring a better work-life balance for the team members.

    All of this brings happier employees that will be more productive and satisfied with their work, which will reduce turnover.

    Conclusion

    The need for a more sustainable world is gaining momentum and involves every industry, including software development. No wonder Gartner included sustainable technology in the “Top 10 Strategic Technology Trends for 2023,” along with platform engineering. As platform engineering is being adopted by more and more organizations to accelerate software development, sustainability has to be addressed.

    An internal developer platform can help developers build more sustainable software. Platform engineering can help in all of these aspects, improving not only the developer experience but also the organization and the environment. This is just the beginning of the sustainable path platforms should follow, but the outlook is very encouraging.

  • Why Monitoring-as-Code Will be a Must for DevOps Teams

    Why Monitoring-as-Code Will be a Must for DevOps Teams

    Over the last decade, we’ve seen an increasing number of operations teams and development teams using an infrastructure-as-code (IaC) process for provisioning cloud-based infrastructure. In that model, IaC tools like CDK, Pulumi or Terraform are employed to allow developers to code, deploy and manage cloud-based infrastructure. The advent of cloud services and IaC tools has freed engineering teams from error-prone (and slow) manual infrastructure provisioning processes. The ability to codify your infrastructure next to your version-controlled application code has been a game-changer for many organizations.

    An Introduction to Monitoring-as-Code

    With IaC becoming an increasingly valuable tool for managing infrastructure, it’s only natural that other parts of the ecosystem follow that shift left philosophy, including how testing and monitoring are done.

    Similar to IaC, the monitoring-as-code (MaC) concept allows us to describe a monitoring infrastructure as code. Today, many DevOps teams are employing MaC by using some of the aforementioned IaC tools like Terraform or Pulumi—in conjunction with monitoring platforms like Checkly—to create, run and manage their browser and API monitoring regimens. In these examples, monitoring checks can be defined in JavaScript, TypeScript or HCL, and then those can be committed together with application code. Modern MaC matures fast; it does not even require traditional IaC tools but enables MaC without extra tools. Modern platforms enable this by shipping CLIs to achieve this. Making monitoring an integral and companion part of your codebase comes with many benefits and advantages.

    Just as IaC has become an integral part of the new paradigm for managing cloud-based infrastructure, here are a few reasons why I believe MaC will also become a must for modern DevOps teams.

    The Benefits That Make MaC a Must for DevOps Teams

    Flexibility and Programmability
    By adopting MaC, your automated testing infrastructure is defined as code and lives next to your application code in your code repository, giving you all the benefits that entails. Testing and monitoring are no longer treated as separate from your application code.

    Update Monitoring With Your Code Changes
    As mentioned in the previous bullet, MaC places your testing and monitoring next to your application code. So when your application code is updated, your testing and monitoring ships right alongside it, streamlining the process. That makes your testing and monitoring solutions deployable, with MaC giving you the ability to schedule tests on a continuous, around-the-clock basis from distributed locations across the globe.

    Monitor and Test on Staging
    In the past, synthetic monitoring was primarily meant to run on production: Once your apps were deployed, monitoring kicked in and alerted you when things failed. With MaC, that monitoring can now run locally alongside your application code in a CI/CD flow, allowing that app code to be tested and even monitored continuously on staging before getting deployed on production.

    Speed and Automation Efficiency
    With monitoring now embracing the “as code” paradigm, new checks can be spun up quickly and in far less time than a non-MaC monitoring approach. All of that increased speed and efficiency will have a real-world impact on dev, ops and QA teams, saving engineers hours now automating once-siloed manual and inflexible work.

    Scale as Needed
    Finally, given that your tests live right next to your application code, it’s much easier to observe, manage and understand what is being monitored. That insight and visibility also make it much easier to scale your testing and monitoring efforts across teams, organizations and even across the globe. All of that enhanced efficiency frees dev and QA teams to focus on other tasks while simultaneously providing better testing and monitoring support across the organization.

    MaC Benefits in Action
    As an example, all of the aforementioned benefits are being employed by a large health care provider that recently adopted a MaC workflow. That organization relies on a MaC testing and monitoring workflow—based on tests written in Playwright—to automate its synthetic monitoring efforts. In particular, a MaC approach has enabled the QA and dev teams there to quickly build, validate and deploy an entire suite of checks from their local development environment.

    The Future of Testing and Monitoring

    With MaC now moving in the same leftward direction as IaC, I believe that the future of monitoring will become increasingly accessible, flexible and frictionless for any modern developer. MaC will also be increasingly based on open source testing and automation frameworks like Playwright, Puppeteer and others, which have been used and adopted for automation and testing for years.

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.

    Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.

    Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.

    The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.

    We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:

    Monster.com

    SnapLogic
    San Mateo, California
    Infrastructure Engineer
    $183,310

    LinkedIn

    Metropolitan Transit Authority – NYC
    New York, New York
    Cybersecurity Officer – DevSecOp/Cloud Infrastructure
    $144,500 to $191,000

    Dice.com

    Flexion
    Remote
    Azure DevOps Engineer
    $140,000 to $170,000

    Indeed.com

    Woven Teams
    Remote
    DevOps Engineer
    $116,000 to $147,000

    SimplyHired.com

    Raytheon Missile and Defense
    Schriever Air Force Base, Colorado
    Senior Software Engineer
    $75,000 to $161,000

  • Creating a Growth Loop for Your Open Source Project

    Creating a Growth Loop for Your Open Source Project

    Every open source project hopes to achieve escape velocity, attracting enough users and contributors to become a successful, sustainable project that expands over time. But how do you attract developers and create the cycle of adoption that can lead to a thriving ecosystem?

    It obviously starts with building a great product that developers love to use, and there’s an element of luck in finding this sweet spot and breaking through. But there’s also a lot that you can control. I was fortunate to have helped create an open source project, Streamlit, that expanded quickly and our company was acquired last year. I wanted to share what worked for me in the hope that it also can be helpful for you.

    My guiding principle is to meet developers where they are and make it as simple as possible, at every step in the journey, to discover your project, learn how to use it and build great products. Depending on the type of open source project you’re creating, providing a simple way to share artifacts and use cases built from your code—in our case, this was applications—can also be a powerful way to raise awareness.

    I think about this in terms of a “growth loop” with multiple points of entry where developers can get interested and get started. These entry points include developer forums, source repositories, your documentation page, learning assets, your website and more. The point is to eliminate friction at every point in this loop and make it as easy and fun as possible to start building and sharing with your code.

    For organizations trying to get their open source project off the ground, here are some key learnings about what worked for my company in building this growth loop and some tips for driving continued adoption and innovation.

    Be Visible in the Open Source Community

    We made it a point to go where developers are already at, including forums like Reddit and Stack Overflow and conferences like ODSC and FOSDEM. Engaging with developers to understand what they really need from your project and how to reduce points of friction is key. We were always grateful when people gave us feedback and we reached out to any developer who engaged with our product, whether they had 10 followers or 10,000. I’ve found that developers really appreciate it when you “show up,” and you learn an incredible amount that can help your project succeed.

    Give Developers What They Need

    Don’t hold back features or functionality that developers will find useful. Developers should have access to everything they need and feel confident that they won’t run into a significant paywall that prevents them from scaling. Building a successful open source project is a bottom-up process, and that means eliminating barriers to entry and making your code as easy to use as possible.

    Where developers go, enterprises typically follow. If you can build a tool that developers love so much they’re coding with it in their free time, they’re likely to take it into the workplace. If your goal is monetization, this is where you’re most likely to have an opportunity to recoup your investment.

    Enterprises require particular features around security, access management, IT support, scaling, service level objectives (SLOs) and other areas—capabilities they are often willing to pay for. So, consider a model where you give developers everything they need to become super users and look for adjacent areas where you can charge for enterprise-type features or enterprise implementations that businesses need.

    Make Sharing Incredibly Easy

    If developers build great things with your code, it’s helpful if they can share those things with the community to show what’s possible and drive interest. You can think of this as a type of advertising. Most developers aren’t won over by traditional ads, but discovering cool things that other developers have done with your code can be a great way to attract new users and fuel the cycle of discovery and adoption.

    So think about the natural output from your project that developers could share with the community. Can they put code on GitHub? Share an app or visualization they built? Post a speed comparison? What can they put on Twitter if they love your project and want to show what they’ve achieved with it?

    Our project is for building data apps, so early on, we added a feature that allowed people to take a screen recording and share their work on Twitter so others could see it in action. Later on, we added a free cloud offering so they could directly share apps with the community. Think about what works for your project and how you could make sharing easy.

    Create Amazing How-Tos and Tutorials.

    These can be important entry points on your growth loop, and it’s best to provide them through developers’ preferred channels. YouTube is a go-to for tutorials of all types, and you can also leverage Coursera, Educative and other e-learning platforms. Someone on my team created a Coursera course for our project that has more than 20,000 enrollees, and one of our Code Camp videos has been viewed over a million times.

    Get creative. If your project is starting to see traction among students, think about providing educational materials that professors can use in the classroom. Online interactive courses are not only a great way to learn programming, they make it easier for educators to manage coursework and see who’s keeping up. This is an excellent way to promote your project and capture new developers.

    Documentation is another important entry point. Our documentation page is starting to look like an e-learning course itself, with guides for getting started, sample code and sample apps. Documentation pages can be listed by search engines, and we found it was worth the engineering time to make this happen.

    Contribute to Other Open Source Projects

    Giving back to the community is a good practice for anyone who cares about software, but it can also benefit your project: Contributing to open source projects helps ensure that your software will be compatible with the tools your target developers use. As well as ensuring interoperability, this can help raise awareness of your project in relevant developer communities.

    Reaching critical mass with an open source project is about understanding what your target developers need, being smart with your resources and removing friction wherever you can. This concept of a growth loop worked for our project, and I hope it helps you to achieve success with yours.

  • Database Virtualization – Driving the Next Big Transformation

    Database Virtualization – Driving the Next Big Transformation

    Data is both the most important asset and the biggest challenge for most corporations. It impacts every aspect of the business from financial considerations to AI/ML training. Data is used by development and DevOps teams, engineering teams, quality assurance, business intelligence teams and database administrators. Despite its central role in operations, traditional forms of database delivery remain complex, costly and time-consuming.

    In many cases, enterprise data is growing exponentially, increasing management challenges and driving up the cost of data storage. Duplicating data to each new environment as use cases arise is overwhelming DBA teams and incurring a significant storage footprint. This has resulted in data becoming a significant bottleneck for DevOps teams, highlighting the importance of finding a quick and automated solution for the replication process across non-production environments.

    What’s Changed?

    In the past, development teams would work with the same dataset for upwards of six months. This left DBA teams with plenty of time to meet all database delivery needs. However, as technology became more sophisticated and consumers grew more demanding, the data delivery life cycle was cut down dramatically. Today, with agile development, new features may be added every few days, if not hours. Coupled with QA teams’ requirement for testing data continuously, AI/ML teams requiring millions of data points for accurate training, and data analysts constantly needing fresh data for organizational growth, this means DBA teams are flooded with demands they are incapable of managing.

    What Now?

    Database virtualization is an automated and easy solution to the dataset bottleneck. By simplifying the creation and distribution of database replicas, DevOps teams will be able to create fully functioning virtual environments via a self-service process–allowing DBA teams to focus on more critical duties relating to production databases.

    Speed Up

    The first—and most obvious—advantage of this approach is the increased speed at which data is delivered. To keep DevOps teams productive and avoid the use of old, stale data, new environments need to be produced on demand. By integrating the production of new environments into the CI/CD pipeline, enterprises will be able to automate the process and provide DevOps teams with full autonomy. By cutting out the dependency on the DBA, teams will be able to access the data they need, when they need it.

    Scale

    Agility is fundamental to the success of database management. Each team requires an independent environment, unaffected by other use cases. The creation and supply of virtual databases allow for quick provisioning. Once created, these databases look identical to the original physical database and function completely independently of each other. Any changes made to a vDB remain unique to the environment it’s in.

    Save

    As the need for data grows, so does the cost of storage. Creating a physical copy for each team and use case results in a significant storage footprint. This means that these datasets come with a hefty cost. However, the creation of virtual databases led to a dramatic decrease in storage needs. Where traditionally, physical datasets can weigh upward of several TB, vDBs only weigh a few hundred megabytes. Further, virtualization solutions also compress the weight of physical copies – such as the golden copy – actively decreasing the weight and cost of storage across all fields.

    Secure the Database

    Even after the database delivery problem has been solved, enterprises are faced with another major issue – security. With so many different teams accessing data in virtual and low-level testing environments, the risk of ransomware and other cyberattacks increases dramatically. Generally, the investment in securing non-production environments is significantly weaker than in production.

    A common approach to address this issue is masking sensitive data so it is rendered useless to outside sources. However, traditional masking requires a data expert to manually sift through all existing data and implement data masking algorithms on any sensitive information found. This time-consuming solution creates a new data bottleneck. By automating the process and applying it to the golden copy, corporations can be confident that all vDBs created are completely secure and privacy compliant.

    Looking Ahead

    Database virtualization is the future of database replica management. Virtual databases are delivered in a matter of minutes, significantly accelerating the speed of delivery. These environments are easy to use, independent, and can be maintained via a self-service portal. The ongoing sync between the original database and the vDB ensures continuous access to the latest data without the need for timely or expensive processes.

    Effective database virtualization will allow well-established enterprises to more easily compete against those agile startups that face significantly fewer barriers to entry.

    Image Source: joshua-sortino-LqKhnDzSF-8-unsplash