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Tag: devops

  • Supercharging Ansible Automation With AI

    Supercharging Ansible Automation With AI

    Whether the latest incarnations of generative AI will live up to their seemingly great promise remains to be seen. This does not always happen with new technology, but the torrent of AI advancements is promising disruption across nearly every industry. Instead of posing a threat to human roles, AI now aims to make tasks easier, faster and more efficient. “This is precisely what we have been trying to achieve with Ansible and automation through generative AI models,” according to Richard Henshall, senior manager of Ansible Product Management at Red Hat. “At the end of the day, AI is another form of automation, and while we do not have thinking computers yet, we do have highly advanced and powerful models that can automate access to data.”

    Case for Integration

    Picture two scenarios: Consider a user who is already skilled in using Ansible. Their limitation is often the amount of time it takes to find the right way to do something, as Ansible is very broad and encompasses many different technologies. The user may be an expert in one or a few of these technologies, but not all of them. In this case, AI can act as an assistant, helping the user find answers more quickly and efficiently. For example, if they want to automate something in AWS, the AI can help them find the necessary resources, and the user can then tweak and curate the automation to create a final product that can be distributed to a large number of people. “This essentially superpowers the individual user,” said Henshall.

    Another type of user may not know how to automate, but they do know how to use a specific technology. “They may not automate that technology because it’s difficult for them to learn something new,” Henshall added. “AI capabilities can help these users get started with automation more quickly, enabling more people to write automation and manage both new and existing technologies.”

    In both cases, the purpose of automation is to help users solve problems. AI serves as an accelerator for these capabilities. “Once users have created automation, the AI model can be trained on the organization’s DNA, including best practices, security posture, policies and enforcement points,” Henshall asserted. This allows the AI to become a co-worker, empowering users and helping them maintain and manage their technologies more effectively.

    “As the AI model learns from the organization and its users, it can help train junior engineers, who will eventually become senior engineers, continuing the cycle of knowledge transfer and improvement,” Henshall predicted. “In this way, generative AI technologies can be integrated into the Ansible ecosystem, benefiting users and organizations in a wide range of contexts.”

    Solving Problems With AI

    As the DevOps leader of cloud testing organization LambdaTest, Shahid Ali Khan has led the company through open-source engagements and investments in next-generation automation solutions, addressing key challenges with Ansible. “We are dealing with a very large scale inventory in terms of geologically separated data centers and multiple OS. Managing this inventory on the basis of these characteristics becomes a hefty task and requires continuous efforts from the team,” he said. “With AI, Ansible can analyze the inventory and automatically group the hosts based on their characteristics, such as their operating system or their geographical location. This makes it easier to manage the inventory and apply changes across multiple hosts at once.”

    Khan also highlights the issue of auto-healing in the industry. “Auto-healing has always been a problem, and while tools in the market have somewhat addressed it, they still require defining errors and categorizing fixes,” he said. “With current AI technology in place, Ansible can detect and automatically fix issues on the basis of historic data in the inventory without the need for manual intervention, reducing the risk of human error.”

    “Inventory forecasting has always been a pain in order to calculate how much inventory we will need based on usage and on what fronts like location and OS we need to scale,” Khan shared. “AI here with Ansible can help detect the usage and forecast based on exact demand.”

    Benefits and Challenges for the Ansible Community

    In the Ansible community, which consists of people who want to solve problems, the benefits and challenges of AI-driven automation are similar to those faced by individuals in their jobs, just on a different scale.

    Community members want to see how generative AI can help them accomplish more tasks, whether or not they are part of an enterprise. Having access to a community model allows them to contribute to training the AI, ensuring its usefulness, as they may eventually work in an enterprise setting during their careers. “The access to community services enables them to take advantage of the benefits offered by AI-driven automation,” Henshall remarked. “It is a mutually beneficial relationship between the community and AI-driven automation.”

  • Coming Soon: AutoOps

    Coming Soon: AutoOps

    I’ve spent a lot of time looking at products in the DevOps toolchain and paying attention to where they are going, what they are doing that is new and how they are differentiated. That has led me to one inescapable conclusion: AutoOps is on the way.

    What is AutoOps?

    AutoOps is full robotic automation; the continuing progression of what has been building for the last couple of years. We have DevOps, GitOps, DevSecOps, IaC, CloudOps, AIOps … and the list is still growing. But they all move toward one thing: AutoOps.

    Now that development has been fully integrated into DevOps, the market is moving to do the same for operations. It is harder with operations because DevOps had Agile, but it is coming. And it is coming to your org.

    That is a good thing, in my opinion. Compliance almost demands it; what we currently call AI enables it and staffing in hard macroeconomic times will make it appealing.

    The questions that you should start asking now are what does the organization need to enable rapid deployment, management and upgrades while leaving humans in control for the majority of decisions?

    Some of you are living this. I have a friend that is in a GitOps shop with automated CloudOps where they are implementing AIOps to handle minor day-to-day things and monitoring for drift … but they’re the exception; most of us are nowhere near there.

    Knowing that is where we are heading means you can plan – and it is glaringly obvious this is where we are heading. Vendors are branching from their current subset of XOps to take us there and eventually, we will have a full end-to-end vendor that can do everything from GitOps to fully automated rollout, green/blue deployment, monitoring and drift correction with human intervention only where the organization wants it. Some vendors are already very close to this environment and I expect that several will get there in the next year or two via acquisitions or partnerships.

    We just don’t think of the overall DevOps/concept to retirement SDLC process in the absolute terms that AutoOps requires, but we’re going to want to because the environment is changing. The changes are for the better, but they will be massive and cause disruptions. However, they’ll make everything better in the end by eliminating error-producing bits while leaving us in control of the flow and process.

    Start Planning for AutoOps

    So start planning, understand what your organization wants and needs from the coming full-robotic automation I call AutoOps, and keep kicking rear. Eventually, we’ll have systems that can do a lot of Dev and Ops work, but that day is a ways off, and will require people to manage the systems that manage the systems… Funny but true.

    *I’ve never seen AutoOps used anywhere with regards to IT, I just thought it describes where we’re headed pretty well. Let me know if you’ve seen a better term coined by a smarter person. Or a not-smarter person that has a cooler name for it.

  • Mastering DevOps Automation for Modern Software Delivery

    Mastering DevOps Automation for Modern Software Delivery

    DevOps automation services are a crucial aspect of modern software development that helps organizations to improve their software delivery processes and keep up with the pace of innovation. With DevOps automation, developers can automate repetitive and time-consuming tasks, enabling them to focus on writing code and delivering features faster and with higher quality. By automating processes such as continuous integration, continuous delivery and infrastructure-as-code, organizations can reduce costs, improve collaboration between teams and respond quickly to changing business needs and customer requirements.

    In this article, we will discuss the basics of getting started with DevOps automation services, including popular tools and best practices that organizations can use to implement a successful automation strategy.

    Agility and Efficiency for Smart Digitalization

    Emerging technologies like multi-cloud, IaC, AI/ML and IoT have only increased the importance of DevOps automation, as they bring new challenges demanding agility, flexibility and efficiency in software delivery processes. Multi-cloud and hybrid cloud environments, for example, can benefit from automating the deployment and management of software across multiple cloud environments. Similarly, AI/ML and IoT require organizations to collect and process large amounts of data in real-time, which can be challenging without automation. DevOps automation can help organizations automate data pipelines and analytics, enabling them to process data faster and more accurately.

    Here are some benefits of DevOps automation:

    ● Speed and Efficiency: DevOps automation enables organizations to deliver software faster and with higher quality. By automating repetitive and time-consuming tasks, developers can focus on writing code and delivering features.
    ● Agility and Flexibility: DevOps automation provides organizations with the agility and flexibility to adapt to changing business needs and customer requirements. Automated processes make it easier to release software updates and respond quickly to market demands.
    ● Cost-Effectiveness: DevOps automation reduces the costs associated with software development by reducing manual labor and minimizing errors. It also enables organizations to use cloud resources more efficiently, which can result in cost savings.
    ● Improved Collaboration: Bringing the different stakeholders along with devs and operations, DevOps fosters a collaborative approach toward enhanced CI/CD. By automating processes, teams can work together more effectively and share knowledge and feedback in real-time.
    ● Scalability: DevOps automation enables organizations to scale their software delivery processes quickly and efficiently. Automated processes make it easier to deploy software across multiple environments and platforms, which is essential for organizations that need to scale rapidly.

    Getting Started With Efficient DevOps Automation

    Implementing DevOps automation requires a well-planned strategy, best practices and the right set of tools. In this section, we will discuss how to successfully implement DevOps automation in your organization.

    Understanding DevOps
    Before you begin with automation, it is essential to understand the principles and practices of DevOps. DevOps is a culture that emphasizes collaboration and communication between software development and IT operations teams to build, test and release software more quickly and reliably. There are some common tools that DevOps teams use for their work, collaboration and communication:
    ● GitHub: The Git repository hosting service used for version control and source code management functionality, especially for distributed projects.
    ● JIRA: A project management tool that helps teams plan, track and manage software development projects.
    ● Slack: A communication platform that enables real-time messaging, file sharing and collaboration.

    Choose the Right Tools
    Automation is at the heart of DevOps, and selecting the right tools is crucial. Choose tools that fit your requirements, budget and infrastructure. Some popular DevOps automation tools include:

    ● Jenkins: An open-source automation server that enables continuous integration and continuous delivery (CI/CD) pipelines.
    ● GitLab: A web-based Git repository manager that provides CI/CD capabilities, project management, and collaboration tools.
    ● Puppet: An open-source configuration management tool that automates the deployment and management of software across multiple environments.
    ● Chef: A configuration management tool that automates the deployment and management of software across multiple environments.
    ● Ansible: Open source tools for automating software provisioning, deployment and configuration management

    Identifying Processes for Automation

    First, list all the critical processes within your organization that can be enhanced with automation. Start with the repetitive and time-consuming tasks that can be automated, such as code testing, deployment and monitoring. There are a number of popular tools that can help with such automation:

    ● Selenium: An open source testing framework that enables automated testing of web applications.
    ● Docker: A containerization platform that enables the creation, deployment and management of containerized applications.
    ● Kubernetes: The open source container orchestration tool is essential in the DevOps pipeline for orchestration and automation of the deployment, scaling and management of containerized applications.

    Creating a Plan

    Once you have identified targets for automation and selected tools, create a plan for automation that includes a roadmap and milestones. Start with small projects and gradually expand the automation to cover all aspects of the software development life cycle. Some popular tools for strategizing these life cycles are:

    ● Trello: A project management tool that enables teams to organize and prioritize tasks using Kanban-style boards.
    ● Asana: A project management tool that enables teams to track and manage tasks using to-do lists, calendars and timelines.

    Training The Team

    Automation requires new skills, so it is essential to train your team to use the tools and processes effectively. Provide training and resources to help your team learn new skills and techniques. Some popular education and training resources include:

    ● Udemy: An online learning platform that provides courses on DevOps automation tools and techniques.
    ● Coursera: An online learning platform that provides courses on DevOps and related topics.
    ● Pluralsight: An online learning platform that provides courses on DevOps automation tools and techniques.

    Implementing Continuous Improvement

    A continuous feedback loop is essential for DevOps’ iterative process and continuous improvement. Integrate mechanisms for evaluating your automation processes, identify areas for improvement and make necessary changes. Here are two common tools used to do this:

    ● New Relic: An application performance monitoring tool that provides real-time visibility into the performance of software applications.
    ● Splunk: An operational intelligence tool that enables organizations to analyze and visualize machine data in real-time.

  • The Future of Continuous Testing in CI/CD

    The Future of Continuous Testing in CI/CD

    If you ever wonder why continuous testing is so important, think about the following: In the past, software testing was typically done after the code was written and sent to the QA department for independent testing. When bugs were discovered, the code was returned to the developers for correction. While this testing method worked, it was time-consuming. Nowadays, companies want to produce high-quality products quickly, so they need a faster and more efficient testing process. This is when continuous testing comes in.

    In this article, we will explore continuous testing from its current state and its future in CI/CD to the emerging technologies and trends that will shape its development. We will also cover the importance of continuous testing for businesses and some of its challenges.

    What is Continuous Testing?

    Continuous testing is a software testing approach in which tests are automated and run throughout the software development life cycle. It is a part of the continuous delivery (CD) process, in which software changes are delivered to customers continuously and iteratively.

    Continuous testing aims to identify defects and issues as early as possible, reducing the cost and time of fixing them later. Continuous testing provides immediate feedback to the development team, helping them to detect and address potential problems quickly and efficiently.

    According to the Reports and Data source survey, 21% of QA professionals have incorporated continuous testing, source code management and continuous integration into their DevOps process to speed up code release.

    Advantages of Continuous Testing

    Here are several advantages of implementing continuous testing as a part of the software development process:
    ● Continuous testing enables teams to identify bugs earlier in the software development process;
    ● Continuous testing provides immediate feedback to developers on the quality of their code, enabling them to address any issues immediately;
    ● You can minimize business risks by assessing and identifying potential issues before they become real problems;
    ● Automating your test cases can reduce the time spent on testing;
    ● Accelerating the process of software delivery to production helps release the product faster;
    ● You can run performance tests in similitude, which can increase the speed of test execution.

    Disadvantages of Continuous Testing

    While continuous testing has become an integral part of the software development process, it’s not without its challenges. As software development teams work to incorporate testing into their CI/CD pipelines, they may encounter obstacles that can hinder their progress.
    ● Continuous testing requires a different skillset than traditional testing, including expertise in DevOps, automation and CI/CD pipelines;
    ● Different tools and technologies are available for continuous testing, and choosing the right ones can be tough. Integrating these tools with other parts of the development process can also be challenging;
    ● To fully realize the benefits of continuous testing, you need to update your testing strategy by moving away from relying solely on traditional testing methods and adopting a well-defined approach to test data management;
    ● Failing to integrate code regularly can result in issues such as duplicated coding efforts and non-compatible code, leading to defects.

    Emerging Technologies in Continuous Testing

    With the increasing demand for faster and more reliable software releases, emerging technologies such as AI/ML, automation and DevOps appeared in continuous testing. These technologies help teams to automate and streamline their testing processes, identify defects more quickly and improve overall software quality. Let’s examine some of them:

    Artificial intelligence/machine learning (AI/ML). AI and ML refer to the field of computer science that deals with developing intelligent machines that can simulate human intelligence and learn from data.

    One of the main benefits of AI and ML in continuous testing is the ability to automate test case generation, which reduces the need for manual testing and frees up time for other tasks. This enables testers to test applications more comprehensively and frequently, ensuring defects or issues are identified and addressed quickly. AI and ML increase testing accuracy by identifying patterns and anomalies in the testing data.

    AI and ML techniques can efficiently and rapidly analyze extensive amounts of data, allowing them to recognize patterns and irregularities in testing data that human testers might overlook.

    Automation: Automation in continuous testing refers to using automated tools and processes to execute software tests repeatedly and consistently. It involves automating various stages of the software testing life cycle, such as test planning, test case design, test execution and test reporting.

    Automation can efficiently perform regression testing, ensuring that changes do not introduce new defects or issues in the software. Tests can be executed 24/7 without human intervention, reducing the time and cost required for testing.

    DevOps: DevOps in continuous testing is the application of DevOps principles to continuous testing, focusing on integrating testing into the software development life cycle and enabling faster and more reliable delivery of software.

    In the context of continuous testing, DevOps emphasizes testing integration into the development process, enabling developers to detect and fix defects early in the cycle. DevOps also promotes automation, collaboration and communication between development and operations teams, allowing faster and more efficient testing and deployment.

    Blockchain: Blockchain technology has been gaining increasing attention in the software development and testing industry due to its ability to provide secure and tamper-proof data storage.

    In continuous testing, blockchain stores testing results and data in a decentralized and transparent way, making it virtually impossible for anyone to modify or manipulate the data. This enhances the credibility of the testing process and outcomes and provides an added layer of security to ensure the integrity of the data.

    Blockchain technology enables efficient and seamless data sharing between stakeholders involved in the testing process, enabling faster and more accurate decision-making.

    Internet-of-Things (IoT): In continuous testing, IoT simulates many IoT devices and platforms, enabling testers to test their applications across multiple devices and platforms. This ensures that applications are compatible with various IoT devices and platforms and perform reliably and consistently in various environments.

    For instance, when creating an application that needs to run on multiple devices, it is possible to use an emulator to test the app on your laptop without setting up a development environment for each type of device.

    IoT-enabled continuous testing provides real-time feedback on the performance and functionality of applications, enabling organizations to identify and address issues quickly and effectively.

    Trends in Continuous Testing

    Continuous testing is rapidly evolving to meet the needs of modern software development practices, with new trends emerging to address the challenges development teams face. Three key trends currently gaining traction in continuous testing are cloud-based testing, shift-left testing and security testing. These trends are driven by the need to increase efficiency and speed in software development while ensuring the highest quality and security levels. Let’s take a closer look at these trends.

    Cloud-Based Testing: Continuous testing is deployed through cloud-based computing, which provides multiple benefits like ease of deployment, mobile accessibility and quick setup time. Businesses are now adopting cloud-based services due to their availability, flexibility and cost-effectiveness. Cloud-based testing doesn’t require coding skills or setup time, which makes it a popular choice for businesses.

    AI-Driven Testing: AI-driven continuous testing is often integrated into cloud-based testing platforms, providing end-to-end automation for web, mobile and API testing. AI and cloud-based testing allows for access to testing environments anywhere, anytime, and provides for regular automated testing using platforms like Selenium.

    Shift-Left Testing: Shift-left testing is software testing that involves testing earlier in the development cycle rather than waiting until later stages, such as system or acceptance testing. The goal of shift-left testing is to identify and address defects as early as possible.

    Delaying testing until the end of development often results in more complicated bug fixes since the software is already fully built. This can cause an increase in cost and time to market. Identifying and fixing bugs earlier in the software development life cycle is generally more cost-effective.

    As IBM’s Systems Sciences Institute claimed, repairing a bug discovered during implementation costs roughly six times more than one found during design. If a mistake is detected after product release, the cost of fixing it is four to five times greater than one found during design and up to 100 times greater than one found during the maintenance phase. This suggests that a bug’s cost increases exponentially as the software progresses through the SDLC.

    Low-Code/No-Code: Low- or no-code in continuous testing refers to using visual interfaces and pre-built elements or modules to create and execute automated tests instead of writing complex code. Low-code/no-code testing tools often provide drag-and-drop interfaces and pre-built components that can be combined to create tests, reducing the time and effort required to develop and maintain test scripts.

    This type of testing provides several advantages, such as allowing non-technical business experts to run testing, speeding up test production and execution and enhancing product quality by enabling more thorough and efficient testing.

    Generally, according to statistics, by 2025, around 70% of new business applications are predicted to use low-code or no-code technologies.

    Security Testing: Due to the rising incidence of cybersecurity threats and data breaches, security testing has become a crucial aspect of software quality assurance. In the year 2023, the tech world anticipates that there will be a stronger focus on security testing, with the emergence of more advanced tools and methodologies aimed at detecting and resolving security weaknesses in software applications.

  • How to Build Successful DevOps Teams

    How to Build Successful DevOps Teams

    The DevOps sector is still in urgent need of new talent, with 38% of tech companies stating that they struggle to find skilled developers in 2023. These same respondents named DevOps as the number-one area where they are feeling the pinch. Because of this, businesses must ensure they prioritize and nurture the right set of core skills when building DevOps teams.

    Below, we discuss the key qualities that businesses should look for when taking on new DevOps engineers and what leaders can do to get the most out of existing employees.

    Tech companies that prioritize the development of these skills will be rewarded with an intelligent, inquisitive and high-performing DevOps team, setting themselves apart from competitors.

    An Eagerness to Learn

    The DevOps sector is constantly evolving, so the talent organizations hire should be similarly open to evolution in the way they work.

    Emil Krawczak, DevOps engineer at STX Next, said this is a key trait necessary for success. “To be successful as a DevOps engineer, you need to have a natural thirst for knowledge and desire to develop, and also be highly persistent when faced with challenges. This area of IT is developing so quickly that it’s difficult to keep track of everything new. It is not enough to learn at work; you need to take steps to grow your capabilities outside of the day-to-day, as well.”

    An Analytical and Strategic Approach

    System administration and development skills are essential, but they aren’t enough if developers don’t keep the bigger picture in mind at all times. Therefore, the ability to think strategically and the ability to apply analytical skills are invaluable for DevOps engineers.

    Piotr Pawłowski, DevOps specialist at The One Question, explained how these traits made it easier for engineers to tackle a DevOps project. “Being successful in DevOps means having an analytical mind and taking a strategic approach. First and foremost, you should be to analyze the project and decide the right tools and approaches. You might know a lot about different tools and cloud solutions, but if you don’t know how to use them, you will not be successful in the world of DevOps.”

    Putting DevOps Theory Into Practice

    The ability to put theory into practice is where true progress is made. With that in mind, what DevOps employees glean from training, education, books and research has little relevance unless it is supplemented by practical experience.

    Versatility is an integral skill in the industry, and successful DevOps pros should be able to use—or at least have working knowledge of—a broad range of tools. They should also specialize in at least one area of Dev or Ops. Python is a great example; Python is highly effective in the automation process and the language should be viewed as a key stepping stone in growing DevOps engineering skillsets.

    Identifying Transferrable Skills

    Employers searching for new talent should look for relevant skills in other IT disciplines that could help a candidate make the switch to DevOps. For example, experience in SysOps or a specific programming language can be extremely useful when opting for a change of focus.

    “DevOps unites the world of admins (SysOps) with the world of programmers/developers (Dev). The easiest way to get into DevOps is to have experience in both of these areas,” Krawczak said. “As an admin, you can build on your experience in scripting, and as a developer, you can leverage your experience in setting up local infrastructure for testing.”

    A Clear and Focused Approach to Specialisation

    Marcelo Grebois, senior DevOps Engineer at Toptal, said businesses should keep things as simple as possible when establishing their DevOps approach. “Try to make things easy. Always think about automation, keep all infrastructure ‘as-code’ and stay up-to-date on tools and technologies.”

    Roman Kamiński, cloud system architect at Atos, agreed. “Using automation tools such as Ansible and Terraform, for example, stops developers from getting left behind while helping them adjust to an ever-changing industry.”

    Developing the Next Generation

    It’s important that tech companies invest in the growth of DevOps engineers and help them focus on the skills that will maximize their potential. The organizations that put time and resources into nurturing the next generation of talent will benefit from a larger pool of well-trained engineers. In the long term, this will ease the DevOps skills shortage and make for a more prosperous IT department.

  • Revolutionizing the Nine Pillars of DevOps With AI-Engineered Tools

    Revolutionizing the Nine Pillars of DevOps With AI-Engineered Tools

    In my book “Engineering DevOps,” published a few years ago, I classified DevOps into “nine pillars of DevOps best practices” – a comprehensive framework that covered the full scope of DevOps practices. Each of these pillars represents a vital area of DevOps implementation and, together, they ensure a holistic approach to integrating development and operations for effective collaboration and continuous delivery.

    The current generation of AI-engineered tools is now being applied to each of the DevOps pillars and has the potential to significantly increase the efficiency, reliability and speed of the software delivery process. By automating repetitive tasks, improving decision-making with predictive insights and facilitating proactive issue resolution, AI can drive a higher level of quality, security and performance in the software development life cycle. Furthermore, AI’s capacity to learn and adapt over time means continuous improvement, fostering a truly data-driven, learning-oriented DevOps culture.

    AI Applications for the Nine Pillars of DevOps

    The following paragraphs briefly explain how the current generation of AI-engineered large language model (LLM) tools are being applied or could be practically applied to improve each of the nine pillars of DevOps.

    Leadership Practices: Leadership is vital to drive cultural changes, set vision and goals, encourage collaboration and ensure resources are allocated properly. Strong leadership fosters a successful DevOps environment by empowering teams and supporting innovation. AI can assist leaders in decision-making by analyzing large datasets to identify trends and predict outcomes, providing valuable insights to guide strategic planning.

    Collaborative Culture Practices: DevOps thrives in a culture of openness, transparency and shared responsibility. It’s about breaking down the silos that can exist between different teams (like development, operations and QA) and promoting effective communication and collaboration. AI-powered tools can improve collaboration through smart recommendations, fostering more effective communication and knowledge sharing.

    Design-for-DevOps Practices: This involves designing software in a way that supports the DevOps model. This can include aspects like microservices architecture, modular design and considering operability and deployability from the earliest stages of design. AI can be used in the software design phase to automatically suggest improvements in code quality or to identify potential performance issues early.

    Continuous Integration Practices: This is about developers frequently merging code changes into a central repository. After that, automated builds and tests are run. The main objectives are to detect problems early and reduce integration issues. AI can analyze patterns in past build failures to predict potential issues with new builds, leading to proactive error mitigation.

    Continuous Testing Practices: Continuous testing involves automating tests to provide instant feedback on business risks and allowing for faster, more reliable releases. This approach enhances the quality of the software and reduces the time it takes to resolve defects. AI can be used to generate and optimize test cases automatically, reducing manual effort and speeding up the testing process.

    Elastic Infrastructure Practices: Elastic infrastructure involves automated provisioning and management of computing resources, often leveraging cloud-based solutions for scalability and resilience. This includes practices like infrastructure-as-code (IaC). AI can help manage cloud resources by predicting future needs based on usage trends and automatically adjusting resources to optimize cost and performance.

    Continuous Delivery Practices: This is an approach where code changes are automatically built, tested and prepared for release to production. It aims to make releases less disruptive and more frequent, increasing speed and efficiency. AI can help predict the potential impact of new releases on the system, aiding in decision-making related to deployment.

    Continuous Security Practices: Also known as DevSecOps, this involves integrating security practices into the DevOps workflow. The goal is to introduce security considerations early and often in the life cycle rather than leaving them as an afterthought. AI can be used in anomaly detection to identify potential security threats and automate security policy enforcement.

    Continuous Monitoring Practices: Monitoring systems and application performance in real-time allows teams to detect and react to problems quickly, often before they impact users. It’s crucial for maintaining application health and informing future improvement efforts. AI can analyze logs and metrics to predict potential system failures or performance degradation, allowing for proactive maintenance and issue resolution.

    Pitfalls and Challenges

    Applying AI to DevOps is a complex process with certain challenges. Here are some potential pitfalls, along with ways to address them:

    Data Quality and Availability: AI relies heavily on data for training and making accurate predictions. If the data is inaccurate, incomplete or biased, it can lead to misleading outcomes. Ensure you’re collecting high-quality, relevant and unbiased data for training AI models. Regular data audits can help maintain data integrity.

    Over-Reliance on Automation: While automation is beneficial, over-reliance on it without human oversight can result in overlooked errors or system failures. Maintain a balance between automation and human intervention. Implement periodic manual checks and reviews to validate AI-generated results and actions.

    Security and Privacy Concerns: As AI tools are more integrated into the DevOps pipeline, they may become targets for security threats. Also, the handling of sensitive data used for AI training can raise privacy issues. Implement strong security practices, conduct regular security audits and consider privacy requirements during the data collection process.

    Black Box Problem: AI, especially complex models like neural networks, often operate as a “black box,” meaning it’s difficult to understand why they made a certain decision. This lack of transparency can make troubleshooting difficult. Consider using explainable AI (XAI) techniques, which aim to create more understandable AI systems, providing transparency into how decisions are made.

    Skills Gap: The application of AI in DevOps requires a certain level of expertise and understanding of both domains, which can be hard to find. Invest in training and upskilling existing staff, or consider bringing in external expertise through hiring or consultancy. Sharing knowledge and encouraging continuous learning can also help bridge this gap.

    Summary

    AI-engineered tools can enhance all of the nine pillars of DevOps by increasing efficiency, speeding up processes and making predictions to aid decision-making. However, implementing AI in DevOps comes with challenges such as ensuring data quality, maintaining a balance between automation and human intervention, addressing security and privacy concerns, managing the “black box” problem of AI and overcoming skills gaps. Each of these pitfalls can be addressed through proactive strategies like maintaining data integrity, implementing manual checks, bolstering security practices, using explainable AI techniques and investing in training and skills development.

  • How to Choose an Observability Data Pipeline

    How to Choose an Observability Data Pipeline

    A complete full-stack observability platform combines data from multiple sources to gain holistic insights into system behavior. In fact, organizations without a full observability solution could find themselves facing departmental silos since teams with different data sources cannot share insights.

    While a key step in observability is the ability to combine logging data with metrics, companies that have developers viewing logging data and their metrics elsewhere are not collaborating on their data in the way observability requires. The solution is an observability data pipeline that best fits your business needs.

    What is an Observability Data Pipeline?

    An observability data pipeline does two things—centralizes data from a number of different sources and provides teams with the right tools to turn data into business insights. Observability data pipelines are typically user-friendly so that you can easily set up and manage your data.

    Observability data pipelines integrate data from a range of sources, including AWS, FluentD and Kubernetes. They have capabilities for searching and viewing log data and notifying engineers through email and even Slack.

    An observability pipeline ingests logs, so they can be viewed in a log viewer. Pipelines can also analyze data in real-time, allowing engineers to quickly spot critical security issues before they affect critical systems.

    There are a number of full-stack observability platforms, including Coralogix, Splunk, Elastic and even options like Azure Log Analytics and Google Cloud Logging. Coralogix, for example, is a popular observability data pipeline that can analyze data in real-time using Streama and use Kafka Connect to integrate data from a variety of sources and extract trends.

    Business Benefits of Observability Data Pipelines

    Using observability data pipelines has the potential to transform the way a business does DevOps. Let’s go through a few key benefits of this technology:

    ● Reducing Cost of Observability Data
    Many organizations use systems like Cloudwatch to analyze logging data. However, these often charge per GB of logging data analyzed, raising the question of whether Cloudwatch is really cost-efficient. And it’s not just Cloudwatch. For many systems, logging costs can be increased due to slow query times, noisy logs and the wrong type of storage. Observability data pipelines help organizations manage high volumes of logging data and move away from expensive legacy platforms. This enables enterprises to increase their observability for a modest price.
    ● Reduced Onboarding, Increased Problem-Solving
    Pipelines are straightforward to learn. Unlike options such as ELK, which can be quite specialized and difficult to maintain, observability data pipelines are easy to use and easy to set up. This reduces the time needed to onboard new engineers. Less time onboarding means more time solving problems and serving clients, which increases business revenue.
    ● Better Data Shaping and Merging
    Data from different sources is often stored in multiple formats and structures. Because observability depends so much on well-integrated data, it’s vital to transform this data into standardized formats. Observability data pipelines use log parsing to store key features of logging data for search and analysis.
    ● Reducing MTTD/MTTR
    Having an optimized, user-friendly observability data pipeline reduces downtime by helping engineers solve problems faster.
    ● Enabling DevSecOps With Observability
    We’ve written about the rising prominence of DevSecOps, a paradigm that involves shifting security left in the development life cycle. Key to DevSecOps is the fruitful collaboration between DevOps and Security. This can only be achieved by high levels of observability. Observability data pipelines facilitate this with their ability to integrate data from multiple different sources and present it intuitively. This means security teams can apply their expertise to log analytics that would otherwise have remained siloed.

    Wrapping Up

    Observability, the ability to use logs, traces and metrics to “picture” the internal state of a system, is vital to any DevOps team. Observability data pipelines can help by integrating and presenting data from different sources. This mitigates the effects of organizational silos and allows DevOps teams to solve problems faster.

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    staging-devopsy.kinsta.cloud 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:

    LinkedIn

    Samsung Electronics America
    Mountain View, California
    Lead Site Reliability Engineer (SRE) – DevOps Specialist
    $180,000 to $210,000

    Indeed.com

    Federal Reserve Bank of New York
    New York, New York
    DevOps Engineer
    $178,100 to $207,900

    CareerBuilder.com

    SAIC
    Chantilly, Virginia
    DevOps Advisor
    $130,000 to $200,000

    Dice.com

    Saicon Consultants
    Alpharetta, Georgia
    DevOps Engineer
    $110,000 to $140,000

    Simply Hired.com

    Hewlett-Packard Enterprise
    Fort Collins, Colorado
    DevOps Automation Engineer
    $109,400 – $251,500

  • How Test-Driven Methodologies Reduce CI/CD Lead Time

    How Test-Driven Methodologies Reduce CI/CD Lead Time

    Modern development environments, in which rapid continuous delivery is facilitated by automated continuous integration/continuous development (CI/CD) pipelines, require thorough and automated testing in development prior to integration. This article explains how test-driven development (TDD) and acceptance test-driven development (ATDD) methodologies reduce CI/CD lead times.

    Lack of test automation in development impacts CI/CD lead time for subsequent integration, delivery and deployment stages of a CI/CD pipeline in the following ways:

    • Increased defect rate: Without automated testing during the development stage, more defects and issues are likely to be introduced into the codebase, increasing the time spent on debugging and fixing issues in later stages.
    • Slower feedback loop: Without test automation, the feedback loop between code changes and test results is slower, making it more challenging to identify and resolve issues quickly. This delay can impact the overall lead time in the CI/CD pipeline.
    • Reduced confidence in releases: When testing is not performed adequately or isn’t automated, there is less confidence in the quality of the software being released. This may lead to more manual testing and validation, further increasing lead times.
    • Inefficient use of resources: Without test automation, developers and testers need to spend more time on manual testing, reducing the time available for developing new features or fixing existing issues.
    • Increased merge conflicts: When test automation is not performed early in the development stage, the risk of merge conflicts increases as different developers work on the same codebase. Resolving merge conflicts can be time-consuming and may cause delays in the integration, delivery, and deployment stages.
    • Decreased stability and reliability: Inadequate test automation during the development stage can lead to less stable and reliable software. This may require additional time and effort to address issues and ensure that the software is production-ready.
    • Inconsistent test environments: Manual testing may not always be conducted in a consistent environment, leading to potential discrepancies between development, staging, and production environments. This can cause unexpected issues and delays in the pipeline.
    • Hindered collaboration: Lack of test automation may reduce visibility into the quality of the codebase, making it more difficult for team members from development, QA, security and operations to collaborate effectively and identify issues early.

    Although CI/CD pipelines help catch issues and ensure code quality through automated builds and tests, having a strong foundation of test automation during the development stage helps minimize the potential for issues in the pipeline and ensures a smoother CI/CD process.

    There are several reasons why many development teams do not automate tests prior to integration, despite the benefits of doing so.

    • Lack of awareness or understanding: Some development teams may not be aware of the benefits of test automation or may not fully understand how to implement it effectively in their development process.
    • Insufficient skills or experience: Test automation often requires specialized skills and knowledge, and some development teams may lack the necessary expertise to implement it effectively.
    • Time constraints: Developing and maintaining automated tests can be time-consuming. Teams working under tight deadlines might prioritize feature development over creating automated tests, believing that it will save time in the short term.
    • Limited resources: Test automation tools and infrastructure can require significant investments in terms of cost and resources. Some organizations may not have the necessary budget or resources to invest in test automation, especially for smaller projects or teams.
    • Resistance to change: Some teams may be resistant to adopting new practices, particularly if they have been successful with their existing processes. Changing established workflows and learning new tools can be challenging and may encounter resistance from team members.
    • Incomplete or rapidly changing requirements: In situations where requirements are unclear or frequently changing, development teams may struggle to create meaningful automated tests, as they may require continuous updates to stay relevant.
    • Overemphasis on manual testing: Some teams may place a greater emphasis on manual testing due to a belief that it provides more accurate or thorough results. This perception can lead to a reluctance to invest time and effort into developing automated tests.
    • Fear of increased maintenance: Automated tests, like any other code, need maintenance and updates as the application evolves. Some teams may be concerned about the additional effort required to maintain a test suite, particularly if they have limited resources.

    The amount of test automation that should be done in development prior to the integration stage of a CI/CD pipeline depends on several factors, including the complexity of the application, team resources and the specific project requirements. However, here are some general guidelines to help you determine the right balance:

    • Unit tests: Developers should aim to automate most, if not all, unit tests during the development stage. They are typically fast to execute and provide quick feedback, allowing developers to catch issues early and minimize the number of defects that make it to the integration stage.
    • Component and integration tests: While unit tests focus on individual components, component and integration tests verify the interaction between different components or services. These tests should be automated as much as possible to ensure that any issues arising from component interactions are identified and resolved quickly.
    • Static code analysis: Automating static code analysis checks during development helps identify code quality issues, security vulnerabilities, and potential performance bottlenecks early in the process.
    • Code coverage: Strive for a high level of code coverage with your automated tests. However, it’s important to remember that code coverage is just one indicator of test quality and should be complemented by other testing strategies.

    Striking the right balance between automated and manual testing is important. Automate tests that are repetitive, time-consuming or prone to human error. Manual testing should be reserved for exploratory testing, usability testing and other areas where human intuition and judgment are necessary.

    Test-driven development (TDD) and acceptance test-driven development (ATDD) are development methodologies that emphasize creating automated tests before writing the actual code, helping teams ensure code quality and catch issues early prior to integration.

    Test-driven development (TDD) is a software development methodology that emphasizes writing tests before writing the actual code. The primary goal of TDD is to create clean, maintainable and bug-free code by catching issues early in the development process. TDD follows a short iterative cycle consisting of three main steps: Write a test, make the test pass and refactor the code. The developer starts by writing a test for a specific feature or function, which initially fails. Next, the developer writes the minimal amount of code necessary to make the test pass. Finally, the developer refactors the code to improve its structure and readability while ensuring that all tests still pass.

    Acceptance test-driven development (ATDD) is an extension of TDD that focuses on defining and validating the high-level requirements and acceptance criteria for a feature or functionality before development begins. ATDD involves collaboration among developers, testers, and business stakeholders to ensure that the application meets the needs of its users. The process begins with writing acceptance tests, which are derived from the user’s perspective and based on the acceptance criteria. These tests are written in a human-readable language to facilitate clear communication among all team members.

    Once the acceptance tests are defined, the development team follows the TDD process to implement the feature, using the acceptance tests as a guide. As the team develops the code, they also create unit tests and other lower-level tests to validate the implementation. The acceptance tests are executed as part of the development process and serve as a measure of progress. When all acceptance tests pass, it indicates that the feature or functionality meets the defined acceptance criteria, and the development task is considered complete. By using ATDD, teams can improve collaboration, ensure that the application meets user expectations and reduce the risk of miscommunication or misunderstanding requirements.

    Hurdles to overcome when transitioning to TDD and ATDD include:

    • Mindset shift: Adopting TDD and ATDD requires a change in mindset, where testing is considered an integral part of the development process rather than an afterthought.
    • Skill development: TDD and ATDD require specialized skills in writing effective tests and implementing test automation. Development teams may need to invest time and resources in acquiring these skills.
    • Time and resource allocation: Transitioning to TDD and ATDD might initially lead to a perceived increase in development time, as writing tests before code can seem slower. However, the long-term benefits of improved code quality and reduced debugging time should be considered.
    • Collaboration and communication: ATDD relies on effective collaboration and communication among developers, testers, security, operations and business stakeholders. Establishing a culture of open communication and collaboration is crucial.
    • Tooling and infrastructure: Implementing TDD and ATDD may require investments in new tools or changes to the existing development infrastructure to support test automation and continuous integration.

    In the context of TDD and ATDD, the roles of quality assurance (QA), security and operations teams are crucial for ensuring a well-rounded and robust software development process. Their involvement helps to address various aspects of the application, leading to a more reliable and secure product.

    • Quality Assurance (QA): QA team members collaborate closely with developers in both TDD and ATDD methodologies. They can help define test cases, create test plans and provide input on acceptance criteria. In ATDD, they play a particularly significant role in developing and validating acceptance tests, ensuring that the application meets the defined requirements and user expectations. QA team members may also be involved in setting up and maintaining test automation infrastructure, analyzing test results and identifying areas for improvement.
    • Security: Security professionals contribute to TDD and ATDD by identifying potential security vulnerabilities and ensuring that the application is developed with security best practices in mind. They can help define security-related test cases and help to embed security into the application, reducing the risk of vulnerabilities and improving the overall security posture.
    • Operations: Operations teams are responsible for managing the infrastructure and deployment processes that support the application. In TDD and ATDD, they can collaborate with developers to ensure that the application is designed and developed with operational concerns in mind, such as scalability, performance and maintainability. Their involvement also helps to promote a DevOps culture where development and operations teams work together to ensure the application’s success in production environments.

    A practical roadmap for teams to transition to TDD and ATDD while overcoming the hurdles mentioned earlier could look like this:

    Build awareness and buy-in:

      Start by educating the team on the benefits and principles of TDD and ATDD. Obtain buy-in from team members and stakeholders to ensure a smooth transition.

    Provide training and skill development:

      Organize workshops, training sessions or pair programming exercises to help team members develop the necessary skills for writing effective tests and implementing test automation.

    Start small and iterate:

      Begin the transition by applying TDD and ATDD practices to small, manageable parts of the project or a pilot project.

    Foster a culture of collaboration:

      Establish regular meetings or workshops for developers, testers, security, operations and business stakeholders to collaborate on defining requirements and acceptance criteria. Encourage open communication and shared responsibility for test creation and maintenance.

    Set up the necessary tooling and infrastructure:

      Allocate time and resources to select, set up and configure these tools and ensure they are properly integrated into the development process.

    Monitor progress with metrics:

      Monitor key metrics, such as code coverage, test execution time, defect rates and lead time to track progress and identify areas for improvement.

    Continuously improve and adapt:

      Regularly review the team’s progress, gather feedback, and adjust the process as needed.

    Summary

    TDD and ATDD methodologies contribute to reducing CI/CD lead times by automating tests during development. This improves code quality, minimizes defects and promotes collaboration among team members from the outset of the development process. By writing tests before the actual code and focusing on meeting acceptance criteria, teams can catch and resolve issues early, leading to fewer delays and reduced time spent on debugging and fixing errors during the integration, delivery and deployment stages. These practices also help ensure that the application aligns with user expectations and operational requirements, resulting in a smoother and more efficient CI/CD pipeline.

    Anyone who is passionate about modern quality engineering processes for building high-quality software to participate is encouraged to join new ShiftSync Community from Tricentis to engage in discussions about this topic and other development methodologies.

  • Infrastructure-as-Code Pitfalls in Platform Engineering

    Infrastructure-as-Code Pitfalls in Platform Engineering

    In the context of cloud operations, the last decade was ruled by DevOps and infrastructure-as-code (IaC). But what is true DevOps? Is it developers running their own operations? Is it operators learning development skills to automate infrastructure provisioning using infrastructure-as-code (IaC) tools like Terraform?

    Though DevOps is widely understood to incorporate both of these, software developers are far from fully understanding or operating infrastructure. IaC is purely a DevOps tool. When an organization claims to have automated its infrastructure, they mean automation for the efficiency of the DevOps teams, not automation for developers. Developers are still left waiting on DevOps for days for even small infrastructure changes.

    While there were efficiencies gained by the current DevOps model, there is an increasing acknowledgment that we require a better approach to enable developer self-service. This has led to the platform engineering discipline.

    The high-level goals of platform engineering are:

    • Developer self-service for significant parts of infrastructure updates without DevOps subject matter expertise.
    • Built-in security and compliance controls.

    Infrastructure-as-Code (IaC) Limitations in Platform Engineering

    A DuploCloud survey of 40+ enterprises that have made substantial investments in platform engineering showed that the prevalent approach is for DevOps teams to build a DevOps platform with IaC as a core underlying technology. They create templates for the organization’s use cases and publish them in a CI/CD pipeline or a self-service catalog.

    Here are the top reasons why platforms that build on top of IaC are failing these platform engineering goals:

    1. IaC templates are rigid and not flexible enough to meet changing developer requirements: It is true that DevOps teams can anticipate cloud infrastructure topologies to some extent and have IaC templates for those with a few customizable parameters. But in a microservices world, there are thousands of other workflows and topologies possible based on changing application needs and security controls. Large data centers and edge infrastructure make this even more complex. A manual approach, relying solely on DevOps personnel to constantly build and update myriad combinations in static scripts simply can’t scale.
    2. Scripting tools can’t build life cycle management: In cloud operations, people-triggered changes are only a subset of possible use cases. Many asynchronous operations need to be continuously performed. These range from detection of configurations, from desired state to reverting, or complex configuration scenarios where individual components have to be set up asynchronously and brought together later. It could be as simple as a certain component going down and needing to be restored. IaC is a script that runs when triggered to completion. It has no active life cycle to operate continuously in the background.
    3. Inability to build a concept of an environment: In any orchestration system, users have a concept of the environment they want to build. When they log in to the platform and navigate to their environment, they expect to update as well as view the state and aspects of resources in that environment, be it the provisioning status, metrics, logs, faults, audit logs, compliance posture, etc. They may want to perform debugging functions, such as restarting services, SSHinginto a VM or accessing a resource’s cloud console (S3 Console, for example) using access control boundaries within that environment. For example, in Kubernetes, you choose the namespace to be the environment and management software like Rancher on top of K8S to provide these functions. If we must replicate this same concept across a broad infrastructure-level platform, we cannot do it using only IaC. We need something with a scope that spans all cloud operations, like Kubernetes. Terraform is a configuration updating and management software, not an orchestration system.

    Many platform teams have tried to work around these problems with point solutions by using a disparate set of jobs for certain aspects of life cycle management, building a thin UI shim on top of cloud accounts for visualization of resources while redirecting to other systems like Datadog for logging, metrics and alerts. But for most of the use cases, the DevOps team is still very much in the operations workflow. This completely defeats the concept of developer self-service as well as continuous compliance goals.

    Learning From Other Successful Platforms

    Two recent examples of successful cloud platforms are Amazon Web Services (AWS) in the context of infrastructure-as-a-service (IaaS) and Kubernetes for container orchestration. These are distributed system implementations using higher-level programming languages like Java and Go. You can’t build such complex systems using scripts and jobs.

    Building a true DevOps orchestration platform requires a systems design approach. It also takes expert systems engineers and many years to build and mature, like it did for K8s and IaaS in the Public Cloud.

    Conclusion

    Since platform engineering teams need systems design expertise that is likely outside of the scope of the typical DevOps role, we need the software development function to intervene with distributed systems programmers and many years of investment. Most organizations trying to build in-house platforms don’t have or don’t want to make this type of investment, as it may not be their core business. Megascale tech companies like Facebook, Uber, Netflix, etc. will likely build an in-house solution, as they have the talent and scale, but for the masses, it is likely that the solution will be an off-the-shelf product from an ISV.

    The industry has solved IaaS with public cloud, container orchestration with Kubernetes and observability with platforms like Datadog. The platform engineering category is heading in the same direction.