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Category: Enterprise DevOps

  • Generative AI: The 90% Rule

    Generative AI: The 90% Rule

    There is a concept we need to solidify that I will call the 90% Rule—until someone smarter than me comes up with a better name. The 90% Rule is simple: Generative AI will be able to do 90% of the work in areas that it is good at. Right now, that would be writing, but coming up soon will be image generation and coding and, not too long after that, video generation. For those of you buried in the space who are offended, I’m not rating it as advanced as it is for specialists; the point of generative AI is not to let AI specialists do things, but to let average people in the space do so. And that list is representative of the current space.

    This is just based upon the state of things at the moment I wrote this blog, but that seems to be the immediate order. I’m only talking generative AI here—AI for things like filtering test results is a completely different beast (that might just meet that 90% rule … Hmmm). I can tell generative AI to write a story, a script or code for me, and tweak the results. The amount of tweaking certainly depends on the topic and how much time I spent zeroing it in, but 90% seems right. There are those looking far ahead to a time that will herald the arrival of our AI overlords, but for the foreseeable future, we will not be ditching authors, coders and ops people. Our jobs will just change.

    Crikey—given the last few decades, a change to our jobs that means less work is not a terrible thing. Today, writers have to be publishers and marketers, coders have to be ops and ops have to be coders and the entire group has increasingly had to be security and architecture specialists, too.

    Let’s celebrate a tool that says, “Here’s a start, make it useful in your specific situation.” I know I will. I sometimes contract people to do initial research for me and send me links and short descriptions. I encourage them to use generative AI to do so. I do not contract people with my level of knowledge for this work, so they’re going to have things I need to read carefully and follow the links for—they’re not specialists, after all. Generative AI suffers from this syndrome, too, but that means there is no difference for me and they can work smarter, not harder. It’s not like they are writing content; merely providing introductory research through a scan of web properties.

    As time goes on, I expect they will become specialists. But not in the things I pay them to research—in making generative AI do what they want. I would argue the person I contract with the most often is better than me at it, just because she’s doing it regularly. That skill set will inevitably be transferrable, and I may lose her to a full-time position making the I in generative AI actually mean something, and I’m great with that. Meanwhile, I’m getting the background info I need to do my job(s).

    Embrace it. Aim for 90%. Turn out even more rockstar-level apps for the org, and maintain them with less effort. This is where we’re headed, and it’s entirely possible you’ll be less buried in work for some of that time. Win/win. Go out and enjoy. The business will thank you for being even more productive … then waste that extra time you created by implementing new projects. But that’s in the future; use AI in the short term to get some breathing room and still be the force behind the apps.

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    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:

    CareerBuilder.com

    The DarkStar Group
    Fairfax, Virginia
    AWS DevOps Engineer (Security Clearance)
    $250,000

    Indeed.com

    Dutch Bros. Coffee
    Grants Pass, Oregon
    Senior DevOps Engineer
    $139,473 to $161,688

    LinkedIn

    Claravine
    Remote
    DevOps Engineer
    $105,000 to $115,00

    SimplyHired.com

    Motorola Solutions
    New York, New York
    Infrastructure Operations Engineer
    $93,620 to $180,000

    Dice.com

    Scholastic Inc.
    New York, New York
    Associate DevOps Engineer
    $58,500 to $93,500

  • DevOps is Making Gains on Mainframe Platforms

    DevOps is Making Gains on Mainframe Platforms

    A survey of 800 executive and technical professionals conducted by BMC found close to two-thirds (62%) are employing DevOps workflows to build and deploy applications.

    In addition, the survey also finds that just under a third of respondents (32%) are applying artificial intelligence to IT operations (AIOps) across all platforms.

    John McKenny, senior vice president and general manager of intelligent Z optimization and transformation at BMC, said the survey shows that organizations continue to invest in mainframe applications alongside distributed computing environments.

    In fact, 61% of respondents noted that data volume on mainframes increased in the last year, with 56% reporting an increase in the number of databases running. Well over a third of respondents (35%) also reported increased interest in taking advantage of cloud technologies to handle, for example, storage and data protection.

    Overall, the survey found that 94% of respondents have a positive perception of the mainframe, with nearly two-thirds (63%) reporting increased mainframe investments over the past year.

    Compliance and security lead the list of priorities for mainframe organizations, with 61% of respondents citing these as top issues, followed by cost optimization (49%), enhancing automation (41%) and data recovery (40%).

    In general, organizations with mainframes are adopting modern application development tools such as VS Code to attract a younger generation of developers. It’s critical to continue to attract developers to mainframe platforms that continue to run the most mission-critical applications in the enterprise, said McKenny. Many of those applications are being modernized using DevOps tools and practices rather than being moved to another platform, he added.

    He added that the appetite for using generative artificial intelligence (AI) to write code for mainframe applications is less clear because of intellectual property concerns. Most organizations are not going to want to expose their mainframe code to a general-purpose large language model (LLM) that drives a public generative AI platform, but there will be a role for private LLMs that are trained using code that has been validated, noted McKenny.

    Regardless of how generative AI evolves, there’s still much work to be done to further the adoption of DevOps best practices in mainframe environments. Each organization will determine how stringently to apply those practices based on the frequency at which applications running on mainframes need to be updated. In most cases, organizations with mainframes will use a mix of waterfall and DevOps-based approaches to building and deploying applications for many years. The decision as to which approach to employ will, naturally, have as much to do with culture as it does the rate at which the application needs to be built and deployed to drive, for example, a digital business transformation initiative.

    The days when mainframes were managed in isolation from the rest of a distributed computing environment are slowly coming to an end. A mainframe is, arguably, just another node for deploying applications. The challenge and the opportunity now is getting the right DevOps tools in the hands of the IT team managing heterogeneous IT environments, including mainframes.

  • Google De-Recruits 100s of Recruiters ¦ ARM Valued at $45½B in IPO

    Google De-Recruits 100s of Recruiters ¦ ARM Valued at $45½B in IPO

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Google fires hundreds of recruiters, and ARM gets a sky-high valuation. (more…)

  • Latest eBPF Advances Are Harbingers of Major Changes to IT

    Latest eBPF Advances Are Harbingers of Major Changes to IT

    The open source community advancing the extended Berkeley Packet Filter (eBPF) gathered at a virtual eBPF Summit today that featured a demonstration of eBPF being used in a Windows environment. eBPF enables applications to run within a sandbox in the Linux microkernel.

    Microsoft has signaled its intent to employ eBPF within Windows like the latest version of Linux already does today. That capability makes it possible to, for example, run networking, security, observability and storage software closer to the kernel to dramatically improve performance versus running that software in user space.

    Liz Rice, chief open source officer for Isovalent, a provider of networking software based on eBPF, said that while it’s clear eBPF will have a major impact on IT, the transition to eBPF won’t be a single event. Instead, it will be more of an evolutionary process that will occur as organizations upgrade their operating systems and the various tools and applications they employ.

    The eBPF Foundation, an arm of the Linux Foundation, was formed to expand the use of eBPF across all operating systems. Members of the eBPF Foundation include Facebook, Google, Isovalent, Microsoft and Netflix.

    In effect, eBPF changes the way operating systems are designed. It bridges the boundary between kernel and user space by enabling developers to combine and apply logic across multiple subsystems that were historically completely independent. That approach enables, for example, a security tool to scale to the point where it can identify threats at much higher levels of throughput at a time when brute-force cyberattacks are overwhelming existing cybersecurity tools.

    Similarly, networking, storage and observability tools should be able to run at much higher levels of scale. That’s especially critical for DevOps teams embracing observability platforms that need to dynamically process massive amounts of data in near-real-time.

    Less clear is the degree to which eBPF might one day drive convergence across distinct categories once multiple tools and applications run at the microkernel level.

    IT organizations would be well-advised to ask their vendors when they plan to support eBPF as they plan their upgrade cycles. It’s not likely IT teams are going to want to upgrade every tool and application that can benefit from eBPF all at once. In fact, updates to these applications that don’t support eBPF might even be postponed in the knowledge that a much larger and arguably more critical update is on the immediate horizon.

    In the meantime, as the number of organizations running the latest versions of Linux continues to increase, more hands-on experience with eBPF will be gained. IT teams may not need to concern themselves with what is occurring in the microkernel of the operating systems, but they should at least understand how eBPF ultimately helps reduce the total cost of running IT and how it makes the Linux microkernel more programmable. Those IT teams, however, will soon discover how stark a difference in performance there will be.

  • Unused Code: Proving a Negative

    Unused Code: Proving a Negative

    We have a ton of code that is no longer being exercised. We really don’t talk about it much, but it’s out there. From feature flags shutting off code to API versioning making an interface obsolete, it’s just hanging around out there. Like an albatross. Waiting for someone to misuse it. Or, to address the more pedestrian concerns, confusing people new to the codebase. But that code is not contributing a thing.

    Easy example. You are on version 3.2 of your API. Version 2.1 is not called by anything, but the code is still hanging out there. It is not that 2.1 cannot be called, but that it is not currently called. The problem with that is that new devs have to work through what’s current, and since 2.1 and 3.2 probably share 95% of their code bases, what portion of that code base is unused? And the API is sitting there, waiting for someone to penetrate your network and find malicious uses for it.

    Proving a Negative

    But here is the problem: Identifying that a bit of code–be it an API, a method, just a few lines in an if statement, whatever–is never exercised is terribly difficult and, in highly complex environments, borders on the impossible (it is never impossible, assuming IT owns all of the code that could exercise that bit). The old adage about proving a negative absolutely applies. We can–and testing tools in both the functional and security spaces do–generate a list of code that is exercised, or identify code that isn’t being exercised. But not a list of code that is never exercised. That functionality in test tools is super useful, because it helps prioritize issues that are being used a lot and leaves the ones not seeing a ton of traffic for later resolution.

    But I’ll say that in most organizations, that’s no longer far enough. The cruft in your code causes problems, and you need to clean it up. If you’ve worked in IT for any length of time, you’ve been through the “Oh, that bug is because the developer called something no longer used” scenario at least once. And it is a straight-up waste of time.

    So here is my proposal. Test tools generate a list of what isn’t called from systems under test. Pick one–security testing or functional testing–and require it for any bit of code, no matter how small, to go into production. That means that the system knows about all source code and thus knows definitively if a bit of code has been exercised.

    Clean Up as You Go

    Is that a tall order? Yes. Testing isn’t free. But given a scenario where you reduce technical debt by eliminating excess code, I think it will pay for itself. And that is not counting the security posture improvement that reducing the code base implies.

    You’re running the systems that present your organization to the world, so you may as well do some due diligence on yourselves. Keep making cool new stuff and upgrading the old, but clean up as you go with some form of monitoring, this or a more procedural one, so the quality is high and the excess is low.

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    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:

    CareerBuilder.com

    Cognizant Technology Solutions
    Phoenix, Arizona
    Senior DevOps Architect
    $130,000 to $180,000

    Indeed.com

    IBM
    Research Triangle Park, North Carolina
    SAP DevSecOps SME
    $126,000 to $235,000

    Dice.com

    DGN Technologies
    Dallas, Texas
    Azure DevOps Engineer
    $110,000 to $115,000

    SimplyHired.com

    Sabre Systems
    Lexington Park, Maryland
    Cloud Engineer – DevOps
    $106,000 to $134,000

    LinkedIn

    Deloitte
    Miami – Fort Lauderdale, Florida
    Junior Cloud Infrastructure Architect
    $65,000 to $97,000

  • Operational Intelligence: AI-Powered SRE Measurements and Observability

    Operational Intelligence: AI-Powered SRE Measurements and Observability

    In my recent blog, Revolutionizing the Nine Pillars of SRE with AI-Engineered Tools, I indicated AI can help analyze vast amounts of data from monitoring and observability systems, identifying patterns and correlations that may be difficult for humans to detect. In this blog, I explain, in more detail, how AI-engineered tools can be used to improve the site reliability engineering (SRE) measurements and observability pillar of practice.

    AI Use Cases for SRE Measurements and Observability

    Making Applications Observable: AI tools like Dynatrace can provide real-time observability of applications, identifying trends and anomalies that might not be evident from raw data. Challenges here include the potential for overwhelming amounts of data, which can be mitigated by AI’s capacity to analyze and prioritize this data effectively.

    Making Infrastructure Observable: Tools like New Relic and Datadog use AI to provide insights into the health and performance of infrastructure. The challenge here can be the complexity of modern, cloud-based infrastructures, but AI tools can model these systems and analyze their behavior to provide actionable insights.

    Monitoring Applications in Production: AI can be used to monitor applications in real-time, identifying potential issues before they impact users. Tools like Splunk use AI to monitor application logs, detect anomalies and automatically alert relevant teams. The key challenge here is filtering the signal from the noise, which AI can help by learning which anomalies are significant.

    Monitoring Production Infrastructure: AI can monitor infrastructure health and performance, predict maintenance needs and identify potential bottlenecks. Tools like Zenoss use machine learning to predict infrastructure failures before they occur. One of the main challenges here is the diversity of infrastructure components, but AI can handle multiple data sources and types and integrate this data into a comprehensive overview.

    Tracing and Logging: AI can help manage the vast amounts of data generated by modern logging and tracing systems, identifying patterns and correlations that might be difficult for humans to detect. Tools like Logz.io use AI to analyze log data, detect anomalies and provide actionable insights. One challenge can be the sheer volume of log data, but AI can handle this by focusing on patterns and anomalies and learning over time which issues are significant.

    Production Dashboard Design: AI can help design more effective dashboards by identifying which metrics are most relevant and should be highlighted. Tools like Grafana use machine learning to optimize dashboard layouts based on usage data. The challenge here is determining what information is most relevant, but AI can learn this from usage patterns and user feedback.

    Performance Forecasting: AI can help with predicting system performance based on current and historical data, helping to prevent performance issues before they arise. Tools like Virtana use AI to provide accurate forecasts of system performance, helping teams to plan for future capacity needs. The challenge here is the variability of performance data, but AI can handle this by identifying patterns and trends in the data.

    Automated Root Cause Analysis: AI can be instrumental in quickly identifying the root cause of issues in complex, distributed systems. Tools like RunWhen and Moogsoft use AI to correlate events across different systems and tools, significantly reducing the time to identify the root cause. The challenge here is the complexity of modern systems, but AI can handle this by analyzing data from multiple sources and identifying correlations that might not be evident to humans.

    RoadMap for SREs to Transform Measurements and Observability With AI

    Here is a practical roadmap that organizations can use to transition to AI tools for SRE measurements and observability.

    Understand the Current State: First, an organization needs to fully comprehend its existing monitoring and observability practices. This includes understanding what tools are currently in use, what metrics are being monitored and where the gaps in visibility exist.

    Define Goals and Objectives: The next step is to define what the organization hopes to achieve by implementing AI in its monitoring and observability practices. This could include goals like improving system performance, reducing downtime or predicting system failures before they occur.

    Identify the Right Tools: Evaluate different AI tools to help achieve these goals. This includes looking at AI-powered monitoring tools like Dynatrace, New Relic and Datadog, log analysis tools like Logz.io and Splunk and AI-driven observability platforms like Moogsoft and Zenoss.

    Test and Iterate: Implement the chosen tools on a small scale initially, using a test environment if possible. Monitor the results and adjust your approach as necessary. This step might involve tweaking the AI models, adjusting the data inputs or even trying a different tool if the initial choice doesn’t provide the expected results.

    Training and Upskilling: Ensure your teams are well-versed in using the chosen AI tools. This could include formal training sessions, workshops, or even bringing in external experts to provide guidance. This will help your team to effectively use these tools and get the maximum benefit from them.

    Full-Scale Deployment: Once testing has been successful and the team is comfortable with the tools, deploy them across your production environment. Continue to monitor their performance and make adjustments as necessary.

    Continual Evaluation and Adjustment: AI tools learn and improve over time. It’s important to regularly evaluate their performance and make adjustments as needed. This could involve feeding them new data types, adjusting their models or retraining them based on the latest performance data.

    Summary

    Harnessing the power of AI to revolutionize your SRE practices can propel your organization to new heights. AI tools can significantly enhance the measurements and observability pillar of SRE, providing unparalleled insights into application and infrastructure performance, enabling more efficient monitoring practices and ultimately driving improved system reliability and performance. These tools, such as Dynatrace, New Relic, Datadog, Logz.io and Moogsoft, can transform how your organization perceives and interacts with its technological environment, providing crucial visibility and actionable intelligence.

    Imagine a world where your monitoring systems not only alert you about a problem but also predict potential system failures before they occur and even recommend preventive actions. With the implementation of AI in your SRE practices, this can become a reality. By following a well-defined roadmap that begins with understanding your current state, defining your objectives, selecting the right AI tools, iteratively testing and upskilling your teams, you can pave the way for a future where improved system performance and reduced downtime are the norms. AI-powered SRE practices today can unlock a new era of operational efficiency and system reliability.

  • Oracle Bill is 5x Client’s Budget ¦ Toyota Out of Space

    Oracle Bill is 5x Client’s Budget ¦ Toyota Out of Space

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Birmingham looks like the Detroit of the UK—is it Oracle’s fault?
    Plus: Was Toyota’s factory failure caused by running out of disk space? (more…)

  • Despite Concerns, AI Increasingly Used to Build Software

    Despite Concerns, AI Increasingly Used to Build Software

    A survey of 1,001 senior technology executives, developers, and security and operations professionals published this week by GitLab finds 67% are working for organizations already using artificial intelligence (AI) to build software, with 60% of those respondents reporting their organizations use it every day. A full 90% said their organizations are either already using or plan to use AI to build software.

    More than half (51%) have already identified productivity as a key benefit, but 32% are also “very” or “extremely” concerned about introducing AI into the software development life cycle (SDLC).

    Those concerns included AI-generated code introducing security vulnerabilities (39%) and whether that code may not be subject to the same level of copyright protection as human-generated code (48%). A full 95% of senior technology executives said they prioritized privacy and intellectual property protection when selecting an AI tool.

    Most organizations will continue to move forward with AI, but nearly two-thirds of respondents (65%) who use or are planning to use AI for software development said their organization hired or will hire new talent to manage its implementation.

    A full 81% cited a need for training to successfully use AI in their daily work, with 75% of respondents saying their organization provides training and resources for using AI. However, a roughly equal percentage also said they are finding resources on their own.

    David DeSanto, chief product officer for GitLab, said it’s already clear that AI will have a major impact on productivity simply because 75% of a developer’s time is typically spent on activities that have nothing to do with writing actual code.

    Less clear is the amount of expertise required to use AI to build software. In some cases, development teams may need to master, for example, prompt engineering to automate DevOps workflows at scale, but in many more instances, it’s likely the DevOps platform will automatically execute the commands required.

    Regardless of approach, it’s clear that as the amount of code being created by developers starts to steadily increase, the need to leverage AI to manage DevOps workflows will become more acute. The challenge is the pace at which developers can leverage AI to write code is, for the moment, exceeding the pace at which it is being embedded into DevOps platforms.

    In addition, organizations must determine whether to converge DevOps workflows with the machine learning operations (MLOps) workflows used to build AI models that are integrated into applications. As more MLOps capabilities are added to the GitLab platform, it will become easier to achieve that goal and enable data scientists to work more collaboratively with DevOps professionals, noted DeSanto. Today, a cultural divide between two distinct IT cultures is slowing down the pace of AI innovation.

    One way or another, DevOps workflows are about to be transformed. The challenge now is determining which tasks and bottlenecks are likely to be eliminated by AI to enable DevOps teams to focus on managing applications at an unprecedented scale.