Tag: devops

  • API Management: A Weak Link in the Cloud-Native Chain

    API Management: A Weak Link in the Cloud-Native Chain

    Managing APIs is like navigating unfamiliar terrain during a road trip: There’s an old way and a new way to do it, and the new way is just better in many cases. For API management, the old way of doing things involves practices like writing proprietary scripts, manual transaction monitoring and relying on individual employees to take charge of managing specific APIs – leading to more manual work, slower release processes, slower incident recovery and lower reliability. It’s akin to navigating a highway using a paper map, stopping at gas stations to ask for directions and crossing your fingers that you end up at your intended destination.

    The new way of managing APIs looks like this: Using declarative configurations, automated processes and stateless APIs, teams scale and centralize the tasks of configuring, monitoring and updating APIs. They also deploy API changes several times a day, and they can instantly mitigate incidents in production.

    It’s like navigating with a GPS: A simpler, faster and more flexible approach that gets you where you want to go with much less hassle and risk.

    To prove the point, allow me to walk through the reasons why traditional API management strategies fall short for modern environments, then discuss what it takes to manage APIs better.

    What is API Management?

    Let me begin by defining what I mean by API management.

    I’m referring to all of the practices, processes and tools that businesses use to support the APIs their workloads depend on. API management encompasses API design, development, monitoring, testing and security, as well as making updates to APIs after they are in production.

    These tasks are important, of course, because APIs are everywhere today. They handle 83% of internet requests, according to Akamai, which means that keeping APIs documented, updated and monitored is a critical requirement for virtually any organization that deploys Internet-connected applications.

    Without an efficient and scalable means of managing APIs, it becomes difficult not just to defend against challenges like security risks involving APIs but also to guarantee a positive developer experience. The more time and toil your developers have to invest in API management, the less time they have to do the things they want to do and that matter most to the business – like developing cool apps and bringing them to market.

    The Pitfalls of Traditional API Management

    APIs are not new, and most teams that support them have long had API management practices in place. However, in many cases, those practices were conceived in the era when monolithic application architectures and bare-metal servers or virtual machines dominated the IT landscape. They weren’t designed for today’s cloud-native, software-defined, distributed-everything world.

    As a result, traditional API management falls short in modern environments in several key respects:

    ● Slow change management: When you are continuously pushing out updates to apps and APIs, you need an API management strategy that can handle continuous change. Traditional approaches can’t do this. They are too human-intensive to keep pace with rapid change.
    ● Lack of reliability: If you manage API configurations manually, you face a higher risk of introducing changes that accidentally break something. And if that happens, fixing the issue is slow because you have to reconfigure everything by hand.
    ● Slow recovery speed: When you experience an incident related to an API – such as a latency increase due to a misconfigured API – recovery is slower because it’s difficult to trace the surface-level problem back to its root cause, given the lack of a central source of truth for tracking the API’s status and revisions.
    ● Lack of scalability: Traditional API management uses imperative configurations that are stored in stateful databases, which have to be provisioned for each API you want to deploy. That works if you have just a handful of APIs to manage, but it’s impossible to scale if you need to scale to support dozens or hundreds. It also creates particular challenges if you have geo-distributed teams and infrastructure.
    ● Lack of modularity: Conventional API management architectures are not modular or composable, making it difficult to reuse configuration components between APIs. Instead, engineers are forced to “reinvent the wheel” every time they need to configure a new API or move an existing one from dev/test to production.
    ● Lack of standardization: Traditional APIs typically depend on proprietary technologies. As a result, they’re difficult to integrate with modern stacks that are based on widely used tools like Kubernetes, OpenTelemetry and Git.
    ● Poor collaboration: Documenting APIs and sharing monitoring insights between teams is also challenging when configurations are imperative and there is no single source of truth that all stakeholders can refer to when tracking the status of an API.

    These challenges lead to a nightmare for teams tasked with Day 2 operations, meaning those that support APIs after they are in production. Slow, inefficient and unscalable management processes lead to struggles in detecting and fixing problems with API performance or security, as well as in updating APIs. And again, they hamper the developer experience by forcing developers to devote time and energy to deciphering APIs and versions to use, finding up-to-date documentation, and so on. As a result, developers whose main job is to write software, not manage APIs, become burdened by API management requirements.

    A Better Way to Manage APIs

    The good news is that a better world is possible – at least when it comes to API management. For modern apps and environments, API management should be rooted in the following principles:

    ● Composability: Your API management stack should be composable so that you can pick and choose from different ingress controllers, monitoring and analytics tools, load-testing solutions and so on. You shouldn’t be bound to a rigid, inflexible “stack.”
    ● Statelessness: Stateless API management, which frees APIs from dependency on stored configurations, enables scalability, simple rollbacks and consistency across environments because you can deploy APIs anywhere with minimal provisioning.
    ● Automation: The more you automate the processes involved in API management, the faster you can implement changes and the lower your risk of mistakes and inconsistencies due to manual management.
    ● Immutability: The underlying infrastructure that powers your API management stack should be immutable so you can replicate it easily and quickly – a benefit during processes like troubleshooting following a rollback.
    ● Auditability: You should be able to maintain a timestamped log of every change to your API that documents when changes are made, who made them, why they happened and what the potential implications are.
    ● Open and standards-based: The ability to monitor and manage APIs using open, standardized tools and frameworks – such as OpenTelemetry and kubectl – also leads to consistency and simplifies collaboration between teams. In addition, API contracts should be standardized using specifications like OAS.
    ● Efficient incident response: When you discover a performance problem or failure linked to an API, you should be able to troubleshoot and resolve the issue quickly without having to chase down documentation or find the one person at your company who understands how the API works.

    When you manage APIs according to these principles, you escape the tedium and inefficiency of traditional API management. By extension, you can easily support the many APIs that tie your cloud-native environments together.

    The Role of GitOps

    Going a step further, a declarative, stateless, automated approach to API management also enables GitOps, meaning that you can manage APIs via declarative configurations that you store in Git repositories.

    This practice also leads to greater reliability (thanks to consistent, repeatable and scalable configurations), collaboration (because Git serves as a shared, central source of truth for API configurations and workflows) and efficiency (due to the ability to automate and customize deployments using Git-based triggers and actions).

    Conclusion: API Management at Cloud-Native Speed

    In a world where more than half of existing applications are cloud-native and a majority of new apps will leverage cloud-native principles, traditional API management just doesn’t work for these new platforms. It leads to change management processes that are slow and unreliable – exactly the opposite of what businesses need to thrive in a dynamic, cloud-native world. It also slows down recovery, leading to lower levels of reliability and higher risk for the organization. On top of this, it’s tedious, inefficient and unscalable. So yes, cloud-native API management is currently broken.

    We need a radical new approach to API management with one goal in mind: Automate Day 2 API operations with a modern approach. Modern solutions should be rooted in stateless, declarative configurations, open standards and Git-based automation. They must enable change management with high reliability and instant recovery, so you don’t have to worry about API management becoming the weakest link in your ability to innovate quickly and with minimal risk.

    We envision a new world where managing APIs is no longer a burden that developers and platform teams must bear but instead a process that helps teams innovate as quickly as possible, no matter how complex the API architectures they are working with. We also want to help businesses bring products to market faster, as well as troubleshoot incidents more quickly and with less effort.

    Ultimately, we want to empower organizations to take full advantage of cloud-native technologies and paradigms by adopting a cloud-native approach to API management.

  • Survey Surfaces Raft of Ongoing Agile Development Challenges

    Survey Surfaces Raft of Ongoing Agile Development Challenges

    A survey of 788 software development professionals finds 71% are using agile development methodologies within their software development life cycle (SDLC) but only 44% said it is working very well (11%) or somewhat well (33%) in the enterprise.

    Conducted by Digital.ai, the survey identifies end-to-end visibility and traceability from business initiative through deployment as the top opportunity for improvement (44%), followed by an ability to measure cycle time, wait time and bottlenecks (34%), continuous testing (29%) and identification and measurement of technological risk (28%).

    Nearly half (46%) identified too many systems as a primary reason Agile was not scaling, followed by siloed teams (37%), culture clash (34%), inconsistent adoption (30%) and the inability to measure business value (28%).

    Despite these issues, nearly two-thirds (63%) said they have visibility into their Agile pipeline, with 55% claiming they have complete visibility. However, only 36% said DevOps teams currently work together as one team to accelerate innovation and deployment of high-quality and more reliable software. At the same time, only two-thirds (66%) claimed at least 50% of their applications were delivered on time and “with quality.”

    Joyce Tompsett, general manager of the State of Agile project at Digital.ai, said the survey makes it clear many organizations are still trying to strike the right balance when it comes to developing software. For example, the survey finds the top two reasons organizations adopt Agile (tied at 41%) are to accelerate time to market and prioritize delivery and measure customer/business value. Most software development teams are being evaluated both based on velocity (33%) and the value delivered (29%), the survey finds.

    The survey also identifies customer satisfaction (43%), time to delivery (39%) and competitive advantage (34%) as their top priorities for 2024.

    In general, the survey finds that 33% of respondents reported business leaders and executives are actively leading and participating in company-wide agile transformation. However, more (41%) said there was not enough leadership participation. Well over a third (37%) said business teams simply don’t understand what Agile is or what it can do. More than a quarter (27%) said there is not enough training.

    Not surprisingly, the most widely adopted methodology is Scrum (63%) with the Scaled Agile Framework (SAFe) being the most widely used (26%). Another 22% don’t have a mandated framework, while 12% said they have created their own.

    The debate over what precisely constitutes agile development is, of course, ongoing. Each organization tends to adopt the elements of the framework that make the most sense for them, so the actual state of adoption from one organization to the next will vary widely. The one thing that is certain is the more complex the IT environment, the more challenging it can be to embrace agile development.

    And there continues to be lots of room for improvement. If only two-thirds of organizations are delivering half (50%) of their applications on time with an acceptable level of quality, it means there are more projects that are failing to meet that standard than many software development teams might care to admit.

    As more organizations become more dependent on software, however, tolerance for that level of failure will only continue to decline as businesses and leaders lose patience with application development practices that do not live up to expectations.

  • 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:

    Dice.com

    Travelers
    Hartford, Connecticut
    DevOps Tech Lead
    $133,500 to $220,300

    Indeed.com

    Lawrence Berkeley National Laboratory
    San Francisco, California
    Identity and Access Management DevOps Engineer
    $126,900 to $214,140

    SimplyHired.com

    Workday
    Pleasanton, California
    Software Development Engineer
    $112,500 to $204,000

    LinkedIn

    IBM
    Denver, Colorado
    DevOps Engineer
    $110,100 to $161,100

    CareerBuilder.com

    Cognizant Technology Solutions
    Oakdale, New Jersey
    DevOps Manager – Technology Modernization
    $95,500 to $212,600

  • How to Manage Multi-Cloud Infrastructure and Reduce Costs

    How to Manage Multi-Cloud Infrastructure and Reduce Costs

    It’s no secret that the public cloud has been a game changer for businesses. In today’s world, every organization is looking for ways to store data more efficiently, support agile application development and reduce the burdens associated with on-premises data centers.

    The public cloud is essential for meeting those objectives and achieving the higher degree of innovation and digital transformation that many organizations seek. While the shift to the cloud has unlocked unprecedented innovation, it has also created a new layer of complexity and sometimes unexpected costs.

    The Nutanix Enterprise Cloud Index found that nearly every organization (99%) has moved one or more applications to a different IT infrastructure in the past year, and the vast majority (86%) agree that moving applications is both complex and costly. Accordingly, 56% plan to focus on gaining more visibility into their cloud consumption and costs this year, while 52% plan to leverage solutions that minimize cloud waste.

    As Scott Sinclair, senior analyst for TechTarget’s Enterprise Strategy Group explains, “The typical enterprise now has multiple data centers and public cloud providers, and many are also using lots of different colocation facilities and multiple edge locations.” As such, “It presents a new paradigm with companies having to look at different factors and considerations like cost, portability and security because their data is no longer all locked in one room.”

    Organizations must carefully plan their cloud strategy to avoid getting bogged down by complexity and sticker shock. But in most cases, this is easier said than done. The majority of companies are not using a single environment — their infrastructure includes some combination of on-premises and public cloud resources. One of the challenges this creates is additional expenses and complexity associated with data mobility and data protection: Moving data around so that it’s available where and when it’s needed while simultaneously keeping it safeguarded requires careful planning and budgeting. In other words, data has gravity, and unless data movement and orchestration are planned, one can end up stranded when the primary location for a dataset is unavailable.

    Even companies that were born in the cloud have their work cut out for them. Managing different cloud environments might be relatively straightforward for a cloud-native organization initially, but if at some point their data needs to be relocated on-premises to address compliance, data sovereignty issues or budget, it can result in soaring costs if not properly executed.

    With this being said, here are four considerations CIOs should take into account to keep cloud complexity and costs in check from the start.

    1. Have Your Head in the Cloud but Your Feet on the Ground

    To reap the benefits of the cloud, organizations need to adjust their expectations and plan realistically. There’s a false perception that companies can run an application on AWS in the morning, Azure in the afternoon, and Google Cloud Platform at night. In actuality, multi-cloud is a medium-to-long-term strategy that occurs over months or years. Building a cloud operating model with this in mind lets organizations benefit from the flexibility of leveraging multiple clouds while avoiding the cost and complexity associated with trying to do too much too fast with the cloud. When appropriate expectations are in place, hybrid multi-cloud is achievable, affordable, and undoubtedly beneficial.

    2. Learn on Others’ Dime as Much as Possible

    There are several best practices organizations should follow as they begin their hybrid multi-cloud journey. These include things like ensuring key application capabilities have API wrappers, containerizing modern applications as much as possible so they are portable, and automating application deployments through CI/CD pipelines to avoid manual errors and reduce costs. Public clouds have been around for almost 15 years, and designing for the public cloud is now quite well understood across the industry. Still, companies need to be mindful of how they leverage public cloud for certain applications or workloads. For example, modernizing a legacy application portfolio might sound like a good idea in theory, but in reality, it is cost-prohibitive and it will not provide ROI —lift and shift is a much better option for legacy applications.

    3. Standardize, Automate and be Paranoid

    Public cloud environments are fundamentally different from traditional on-premises infrastructure, so organizations need to think about them differently—especially where security is concerned. While a fortified exterior with unfettered internal freedom might be a workable model for on-premises, the same model could spell disaster in the cloud. Host-based security approaches have become more critical in the public cloud. Automation and standardization ensure that security measures are applied consistently across all cloud resources and services within an organization and shorten the window to detect unauthorized behaviors. Organizations must also consider the non-technology aspect of this: training and education for their teams. All teams and departments must be on the same page when it comes to security so they can collaborate effectively on security-related initiatives.

    4. Build in Optionality From Day One

    The public cloud is by far the best place to begin innovation. However, as companies experiment in the public cloud and a smaller portfolio of applications gain traction, enterprises need to adjust to the reality that — for the vast majority of applications — the public cloud will always be more expensive than running them on-premises (or perhaps even another cloud). To avoid lock-in, enterprises must build in optionality for migrating their compute when needed, as well as the mechanism to move their data out of public clouds when needed. Organizations that are flush with cloud engineering talent can do this on their own, but the vast majority will require an underlying platform that’s explicitly designed for data and computation mobility.

    The perks of one platform to run all workloads across clouds, on-premises, and the edge are undeniable: From increased flexibility to optimized performance, compliance, agility and more. By heeding the advice above, companies can keep multi-cloud challenges like complexity and unexpected costs at a minimum and fuel innovation.

  • How to Make the Most of In-House Software Development

    How to Make the Most of In-House Software Development

    Let’s be blunt: Maintaining an in-house software development team can be tough. You must hire skilled developers – which is no easy feat in today’s economy, where talented programmers remain in short supply – and then manage them on an ongoing basis. You must also ensure that your development team is nimble enough to respond to changing business needs and that it can adapt as your technology stack evolves. Given these challenges, it’s no surprise that most organizations now outsource application development instead of relying on in-house teams.

    But I’m here to tell you that just because in-house development can be hard doesn’t mean that outsourcing is always the best approach. On the contrary, IT organizations that choose to invest in in-house development for some or all the work can realize lower overall costs and a competitive advantage by creating domain-specific expertise. Keeping development in-house can help organizations address unique security requirements and maintain full control over the development lifecycle and roadmaps. For businesses with specialized technology, security and operational needs, in-house development is often the best strategy.

    Managing an in-house development team presents some steep challenges. At Komprise, we have refined several practices to keep our team efficient and productive, even in a business environment that is constantly changing.

    Scaling Up

    Scaling teams up is a key challenge for in-house development teams. Finding skilled developers to grow a team can be very difficult, especially when working with a bespoke technology stack.

    There is no simple solution to this challenge, but there are steps you can take to maximize your ability to find and hire development talent. For example, we invest significantly in engaging with college students, allowing us to identify promising developers while they are still in training.

    At the same time, we invest in upskilling our developers who are already on our team so they can take on additional responsibilities. We train our engineers in the latest cloud development and security skills and knowledge. The notion of embedding security into our designs and practices is fundamental to what we do. We also rotate engineers between different teams so they learn new technologies that are important to our business. This strategy allows us to align the work for every developer with their interests and to build reusable skills within our teams. We also hold a technical brown bag three times per month where we cover issues like development for cloud and how it differs from on-premises due to costs or how to design for performance and scalability.

    Focus on Customer Value

    Figuring out which features customers want most is a constant challenge. To address it, we quantitatively track how our customers use our product – which functionality they use most often, for example, and how usage patterns vary across different customer demographics. We evolve these metrics as we develop the product. Additionally, we identify customers who are “power users” and occasionally set up calls between them and our developers to gain additional insight into what customers want. Our marketing team also conducts customer advocacy interviews with our users to learn more about what they like and don’t like.

    Looking at third-party applications and interfaces helps us gain perspective on how best to meet user needs, too. For example, we might examine how Wayfair or Amazon are evolving their UI constructs to inform how we could change our product interface in ways that better meet customer needs. We want our B2B product to be as easy to use as consumer products.

    Invest in QA

    Quality Assurance (QA) operations are distinct from development operations, but if you want a successful in-house development team, you need a great QA team, too. Our QA engineers complement our in-house developers by helping them to deliver the highest-quality product, while also strategizing over how best to handle scale and efficiency challenges. In addition, we’ve invested heavily in QA automation, allowing us to test our applications thoroughly while keeping our team lean and efficient. This ensures that as our product feature set expands, our ability to test the entire product does not increase and we can maintain a monthly release cadence. We use test harnesses to simulate error and failure conditions, by injecting failures into the system or making our network experience a brownout or a blackout. You can’t automate every software test, but you can automate most of them, and QA automation is one of the key ingredients in effective development operations.

    Leverage AI Strategically

    AI is not the solution to every development challenge, but when used properly, AI can accelerate development and help in-house developers work more efficiently. Using AI tools to churn out code has become a hot use case for AI in development since the introduction of solutions like Copilot. AI can be a very useful solution, but the quality of the results it delivers hinges on the quality of the data it ingests. Inhouse development teams working on AI applications have much to consider from the data perspective. To leverage unstructured data, they need ways to find it across data silos, enrich it with new metadata as needed to support search and leverage AI tools like Amazon Macie and Rekognition to further refine the data.

    Optimizing In-House Development for the Long Term

    The importance of keeping in-house development teams happy and productive is likely only to increase as the tasks they work on grow more complex and as new paradigms, like those driven by AI, change the way developers work. The more IT organizations and enterprises can upskill their existing developers and empower them with the right tools and processes to succeed, the better they can leverage their teams to build products and features that bring value to the business.

  • Security Across the SDLC

    Security Across the SDLC

    We’ve seen some interesting movements in the security space over the last couple of years. My focus is on application security, but one of the movements we’ve seen is the blurring of what is application and what is infrastructure security, so I do a fair amount of that also. And then, with the advent of WAAP, we have network security playing a role in AppSec… It’s a growing snowball.

    I will start with the mandatory happy declaration that DevOps no longer treats security as an annoyance. While we’re well past that, I think the shock of the whole “security is a problem, not protection” mentality that DevOps started with is still hanging with us, and some still actively believe that. But at this point, we’re well on the “Security is part of the process” trail we should be on.

    If you look at the top-level changes, static application security testing (SAST) vendors have grown to be development security, covering SAST, DAST, IAST, RASP and even a bit of data security. Meanwhile, load balancers and WAF slowly evolved into full-on web applications and API security that covers the production side with active protections for things like DDoS and DLP.

    Likewise, we’ve seen this with the growth of cloud workload protection and how the entire digital architecture is being pulled into CSPM.

    I’ve said it before – we’re in a consolidation phase. It looks like it will go even further than I thought, though. Now, we are starting to see an interesting development: WAAP vendors are starting to include some or all of DAST, SAST and RASP. Arguably, they always did some IAST because their focus was on runtime protection, but their emphasis is more on protection than test, so it will be interesting to see that bit expand also.

    And all of this includes an inconvenient fact. We don’t have any more people to do security than we ever did; in fact, we have less. That’s part of the reason that we haven’t shoved security everywhere – there aren’t people to do it. In true DevOps fashion, we have distributed the work across other teams – Dev is right there where code security and API security start, so we’ve implemented SAST and DAST bits right into the IDE, for example. Or Ops has always done some amount of security, and we’ve just upped the amount. But they don’t magically have more time, either. I’m sure you are feeling it if you are a practitioner; almost all of us are. AI promises to help with that, and in security, it will likely deliver. Another thing I’ve pointed out before is that security is one space that really does stand to gain from generative AI – in helping make more secure code, in helping make all the permutations of tests, in making rules based upon observed data and more. The time-saving benefits will deliver, in my opinion. But it’s still a lot of work, and some of it is straight-up additional work because we weren’t doing things like container security a few years ago.

    In the short term, if full SDLC security is applied, there will be more work. In the long term, the mega platform for security may well reduce this work as it limits notifications across tools. Maybe we should name the coming mega platform SecArch. Also in the long term, AI will help alleviate some of the load. Until then, buckle in. You’re not likely to get staff to cover new work. But it will be worth it. Just keep plugging along, and know when to say you just can’t add one more thing to the pile.

  • Anima Adds Ability to Personalize Generative AI Code

    Anima Adds Ability to Personalize Generative AI Code

    Anima today revealed is has added an ability to personalize the code its generative artificial intelligence (AI) platform creates based on how an application is described using tools from Figma and Adobe.

    That capability makes it possible for the code being generated to resemble the coding style of an organization or specific developer more closely.

    Anima CEO Avishay Cohen said the company developed this capability to reduce the editing time that is otherwise required when trying to embed code generated by a large language model (LLM) within an application that has a well-defined coding methodology.

    The platform that Anima provides is based on a mix of LLMs and heuristics that the company developed to generate code. In this case, the code being generated is written in React or HTML to make it simpler to build an application’s front end. In contrast, a general-purpose LLM such as ChatGPT is not optimized for creating these types of graphical applications.

    Developers no longer need to worry about, for example, making sure each pixel is perfectly placed because the Anima platform automates that part of the application development process, including adding responsive behavior, animations and logic, noted Cohen. In addition, customized instructions may be applied to an entire project or even a specific component.

    In effect, the platform enables backend developers to automatically create a front-end for their application from a description in tools provided by Figma or Adobe. As a result, developers can allocate more time to writing business logic and optimizing application performance, said Cohen.

    Later this year, Anima will integrate its platform with GitHub and other continuous integration/continuous delivery (CI/CD) platforms as part of an effort to extend the reach of the platform across an entire codebase, he added.

    There is, of course, already no shortage of generative AI tools for writing code, but the degree to which they have been incorporated into DevOps workflows varies from one organization to the next. Much of the code created using platforms such as ChatGPT can vary widely in terms of quality. These platforms were trained using code collected from across the web so it’s not uncommon for the code generated to include known vulnerabilities that DevOps teams need to scan before incorporating into a build. In other cases, the code generated might simply break when installed.

    Regardless of these issues, however, the amount of code being generated by machines is only going to increase, especially as more LLMs that are specifically trained to create code based on examples that have been closely vetted become more widely available.
    In the meantime, developers who never especially enjoyed building the visual front ends of applications can now focus more of their time on tasks they prefer. Alternatively, developers who specialize in the front ends of applications will find that much of the toil involved will soon be automated in a way that should provide more time for them to create customized experiences.

    One way or another, the overall pace at which applications are built is about to exponentially increase. The challenge now will be finding ways to take advantage of AI to enable DevOps teams to keep pace.

  • Using LLMs to Automate Pipeline Conversions From Legacy to Tekton

    Using LLMs to Automate Pipeline Conversions From Legacy to Tekton

    There have been many technological advancements in the market today, but the main hindrance is the cumbersome process of switching from the existing technology/framework to the new one. This is where teams and organizations are left behind in the tech curve. In the DevOps landscape, CI/CD pipelines are the backbone of DevOps, and how you work with them largely dictates your DevOps efficiency.

    CI/CD pipelines, too, like other major technologies and frameworks in the past, have seen various evolutions; the latest among them is being made reusable in an attempt to standardize CI/CD. However, here too, there are several cons:

    1. The pipelines are not proprietary to end-user enterprises and belong to the DevOps platforms which are not open-source
    2. Migrating to these platforms and pipelines is a huge task
    3. They may not be as efficient as depicted

    Speaking of new advancements in technology, most might be familiar with Tekton for CI/CD pipelines: It is Google’s open source framework that is now a graduated CDF project. It redefines how pipelines are built (through reusable tasks) and these tasks are open source and community-vetted. They allow for pipeline authorship, and enterprises can re-use them across platforms that are compatible with Tekton, thus preventing vendor lock-in.

    While this mitigates the first challenge of pipeline ownership and migrations, however, the same problem plagues DevOps teams – transitioning from existing legacy technologies/frameworks to newer, much more capable frameworks like Tekton.

    However, what if there was a way to seamlessly automate your transition from legacy pipelines to cutting-edge, reusable Tekton pipelines without toil? This is precisely what the article aims to explore – automating the conversion of Yaml scripts of existing pipelines to Tekton Yaml scripts.

    Let us consider the three most popular CI/CD platforms for this use case – Jenkins, GitLab and Azure DevOps. Before we proceed to the proposed approach to achieve this (which is detailed in the second half of this article), let us first look at the limitations of Jenkins, GitLab and Azure Pipelines and why one should convert.

    Common Limitations of Jenkins, Azure and GitLab Pipelines

    Resource Intensive: Legacy pipelines can be resource-intensive, especially for larger organizations or projects. Managing server resources, optimizing configurations and ensuring sufficient hardware can be a challenge.
    Complex Setup: Setting up and configuring Jenkins, GitLab and Azure DevOps can be complex, particularly for self-hosted instances. This complexity may require experienced administrators and substantial time investment.
    Plugin and Integration Compatibility: All three platforms may face compatibility issues with certain plugins or integrations.
    Scaling Challenges: Scaling in organizations, especially when dealing with multiple microservices, necessitates a shift from individualized scaling to a template-based approach. Failing to do so leads to a chaotic increase of pipelines, undermining standards and organizational processes.
    Costs: While both Jenkins and GitLab offer free and open-source versions, some advanced features or capabilities may only be available in higher-tier paid plans. This can lead to cost considerations at scale.
    Customization Complexity: These platforms can be complex to customize workflows and integrate custom scripts as they require advanced scripting knowledge.
    User Interface Complexity: Though they have feature-rich interfaces, it can sometimes be overwhelming for new users to navigate the platforms and find specific settings or options.
    Limited Native Integration & Plugins: The native integrations of Jenkins, GitLab and Azure may not cover the full spectrum of tools and services a team may need. Additional integration efforts or third-party tools may be required to bridge gaps in the development workflow.

    As can be seen in the above limitations, many are common including scalability challenges, being resource intensive, limited integrations etc. All the challenges boil down to one common denominator: Complex pipeline management at scale.

    This is where a framework like Tekton can help tackle all the challenges at once, by just easing pipeline management: with its K8s-native pipelines that are reusable, open source and tasks that are community-vetted.

    Tekton Pipelines: Advancing CI/CD with Kubernetes-Native Automation

    Tekton, an open-source project, is rapidly gaining traction as a game-changer in the world of CI/CD pipelines. Its Kubernetes-native design is a standout feature, seamlessly integrating with Kubernetes clusters to harness its scalability and resource efficiency.

    Furthermore, Tekton’s cloud-native roots ensure it can execute tasks in lightweight, ephemeral containers, optimizing resource utilization. The ability to declare CI/CD pipelines as code, coupled with GitOps practices, enhances automation and reproducibility.

    How are Tekton Pipelines Different?

    Kubernetes-Native Automation: Tekton pipelines are deeply integrated with Kubernetes, utilizing Custom Resources and Operators to define and execute CI/CD workflows.

    Declarative and Modular: These pipelines are defined declaratively using YAML manifests, promoting version control, collaboration and code reviews. The modular design of tasks and pipelines enables teams to create reusable components for building, testing and deploying applications.

    Containerization and Isolation: Each task in a Tekton pipeline runs within a dedicated container, ensuring isolation and reproducibility of the build and deployment environment.

    Event-Driven Architecture: Tekton pipelines can be triggered by various events, such as code commits, pull requests, or external events, providing a responsive and event-driven CI/CD process.

    Scalability and Efficiency: Tekton pipelines leverage Kubernetes’ inherent scalability, enabling efficient execution of tasks across a dynamic pool of worker nodes.

    Visibility and Observability: They offer built-in monitoring and logging, allowing teams to gain insights into pipeline execution, track progress and diagnose issues.

    Integration and Extensibility: Tekton pipelines can be easily extended with custom tasks and integrations, enabling teams to connect with various tools, services and cloud platforms.

    Cloud-Native Best Practices: By embracing Tekton pipelines, organizations align with cloud-native best practices, promoting the use of Kubernetes and containerization for streamlined application delivery.

    Why Transition From Jenkins, GitLab, and Azure Pipelines to Tekton Pipelines?

    Migrating from Jenkins, GitLab, or Azure pipelines to Tekton pipelines can be a strategic choice, especially for organizations deeply entrenched in Kubernetes and cloud-native methodologies.

    Optimize Kubernetes-Centric Workflows: Projects tightly integrated with Kubernetes may find Tekton pipelines more aligned with their ecosystem.

    Accelerate Cloud-Native DevOps: Organizations aiming to embrace a cloud-native approach and leverage Kubernetes infrastructure.

    Performance and Scalability: Tekton pipelines run on clusters that can dynamically be created and destroyed according to demand, thus taking resource optimizations to the next level.

    Cloud-Native Best Practices: Best practices, such as containerization, declarative configuration and infrastructure-as-code are a natural fit due to their Kubernetes-native design.

    Proposed Workflow for Automating Pipeline Conversions at Scale

    The overarching objective of this workflow is to facilitate a seamless conversion process using large language models (LLM) without delving into the intricacies of fine-tuning but rather using the benefits of prompt engineering. Central to this approach is the use of the chat context and the strategic application of roles for LLM models to establish context. Along with this, sample pipelines are leveraged to train and fine-tune the model to define how the corresponding YAMLs of all these platforms look for any given pipeline.

    The use of prompts and instructions tailored to fit the use case of generating contextual pipeline YAML and conversion of Jenkins/Gitlab pipelines to Tekton pipelines makes this workflow possible. We maintain a context and a contextual mapping of real-world examples and the most appropriate prompt designs to produce the most accurate responses. The role of users in this context adds weight and further refines the response received.

    However, before proceeding into details and examples, let’s set the base for what exactly generative AI means and its context in our use case:

    Generative AI and LLM for Automating YAML Script Conversions

    Generative AI, such as LLMs, create content like text, benefiting from patterns in large datasets. Large language models (LLMs), with billions of parameters, excel in generating human-like text by understanding language structures. They have broad applications, including natural language understanding, summarization, translation and content generation, promising automation and human-like interactions with AI.

    Adapting the Model to Solve Real-world Use Cases

    Fine-tuning and prompt engineering are two common methods used to train large language models (LLMs) like GPT-3, BERT and others. These methods help adapt pre-trained models for specific tasks or generate contextually relevant responses. Both fine-tuning and prompt engineering play crucial roles. Fine-tuning helps the model comprehend complex use cases, while prompt engineering provides detailed requirements, extracting the best results from the model.

    Why Prompt Engineering?

    Prompt engineering stands out as a highly advantageous approach in specific use cases where precision, control and customization over AI model behavior are paramount. This method empowers developers and users to meticulously design input prompts, effectively guiding AI models to produce contextually relevant and desired responses.

    Prompt engineering is both an AI engineering technique for refining large language models (LLMs) with specific prompts and recommended outputs and the term for the process of refining input to various generative AI services to generate text or images.

    The process of prompt engineering to fit our use case includes :

    Defining the goal: The first step in the process of AI prompt engineering involves setting a clear objective.

    Crafting the initial prompt: With the goal in mind, it’s time to draft an initial prompt. This could take the form of a question, a command or even a scenario, depending on the goal.

    Testing the prompt: The initial prompt is then input into the language model, and the response is analyzed.

    Analyzing the response.

    Refining the prompt: With the insights gathered from testing and analysis, it’s time to revise the prompt. This could involve making it more specific, adding more context or changing the phrasing.

    Iterating the process: The testing, analyzing and refining steps are repeated until we are satisfied that the prompt consistently guides the model toward generating the desired response

    To conclude, here’s an example of typical YAMLs from Jenkins, GitLab and Azure DevOps, and how the same YAML looks when converted to a Tekton YAML. This goes a long way in facilitating teams to finally make a jump from their existing pipelines to cutting-edge reusable pipeline frameworks:

    Jenkins YAML

    GitLab YAML

    Azure YAML

    Converted Tekton YAML

  • 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:

    SimplyHired.com

    D2 Consulting
    Springfield, Virginia
    DevOps Engineer
    $150,000 to $180,000

    Indeed.com

    Mariner Finance
    Remote
    DevOps Engineer
    $120,000 to $150,000

    ZipRecruiter

    Cogility Software
    Irvine, California
    DevOps Engineer
    $110,000 to $140,000

    CareerBuilder.com

    BAE Systems
    Manassas, Virginia
    DevOps Engineer
    $92,290 to $156,860

    Dice.com

    Rackner
    Falls Church, Virginia
    Software DevOps Engineer
    $90,000 to $140,000