Category: Enterprise DevOps

  • 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:

    LinkedIn

    SiriusXM
    Associate Director for Software Delivery
    Deerfield Beach, Florida
    $157,000 or $250,000

    SimplyHired

    Nagarro
    Remote
    Senior Staff Engineer – DevOps
    $155,000 to $196,000

    Dice.com

    Engage Partners
    Davie, Florida
    DevOps Architect
    $130,000 to $160,000

    Indeed.com

    ServiceNow
    Orlando, Florida
    Staff DevSecOps Engineer
    $114,000 to $144,000

    Monster.com

    Swift Group
    Fort Belvoir, Virginia
    DevOps Engineer
    $80,000 to $180,000

  • Linux 6.3: What’s New ¦ AWS Layoffs are a Worry

    Linux 6.3: What’s New ¦ AWS Layoffs are a Worry

    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: A new Linux kernel drops and layoffs at Amazon Web Services point to trouble. (more…)

  • InfluxData Makes Processing Observability Data at Scale More Efficient

    InfluxData Makes Processing Observability Data at Scale More Efficient

    InfluxData today made available an update to its open source time series database that can now analyze metric, event and trace data in a single datastore with unlimited cardinality in terms of how they are aggregated.

    The company is now making available a single-tenant instance of InfluxDB as a managed service alongside its already-existing multi-tenant cloud service that is based on a serverless architecture.

    Finally, InfluxData also announced that later this year it will make available InfluxDB 3.0 Clustered and InfluxDB 3.0 Edge to provide a curated version of the database that organizations can deploy themselves where they best see fit.

    InfluxData CEO Evan Kaplan said InfluxDB has been developed to support a wide range of emerging applications that require access to time series data, including the observability platforms core to DevOps workflows that require visibility into metrics, events and trace data.

    Over the past three years, InfluxDB has been revamped to run on a columnar engine, dubbed IOx, that leverages the open source Apache Arrow memory format and written in the Rust programming language. Kaplan said that approach makes it possible to continuously ingest, transform and analyze hundreds of millions of time series data points per second without limitations.

    At the same time, InfluxDB takes advantage of high compression object storage to reduce the total cost of storing all that data. It also provides interoperability with Open Data Architecture (ODA) to integrate with data lakes based on open source platforms such as DataFusion, Flight SQL and Parquet that are being advanced by the Apache Software Foundation.

    The arrival of version 3.0 of InfluxDB comes as many DevOps teams are starting to struggle with the amount of observability data being generated. DevOps teams want to be able to move beyond monitoring a set of pre-defined metrics and query data in a way that enables them to surface anomalies indicative of a potential issue before there is a major disruption to an application service.

    One of the major challenges today is the tradeoff between how much data is collected and analyzed versus the cost of processing and storing it. InfluxDB provides a mechanism for analyzing high cardinality data involving metrics, events and traces cost-effectively.

    Observability is, of course, only one of several use cases for a time series database capable of processing data in near-real-time. As organizations embrace digital business transformation initiatives, they need to be able to process data in near-real-time at the point where it is being created and consumed. Those applications won’t necessarily replace existing applications based on batch-oriented processing but, over time, they create two distinct classes of applications that process data in fundamentally different ways. The challenge, as always, will be defining the DevOps workflows required to manage applications running on multiple distinct types of architectures.

  • Things We Should Acknowledge, Part Three: Remotely Good

    Things We Should Acknowledge, Part Three: Remotely Good

    This installment of my off-and-on “Things We Should Acknowledge” series is going to focus on an area that most DevOps teams are skirting, but increasingly will not be able to: Remote work.

    For background, I have worked remotely for decades; my experience spans Fortune 100s, publishing companies and startups—and that’s just full-time work. I’ve worked for a ton of vendors and publications as a contractor, too.

    I will start by making clear my stance on remote work—a stance I’ve happily talked with people about since long before COVID-19 measures made remote work a regular thing. You either thrive working remotely or you wither. There is a small group in the center that can make it work with some effort, but inevitably that effort becomes routine and those in the middle become part of one of the two groups.

    Because people drift toward one of two camps, we get the currently popular stance on, well, everything, which is polarization. But we shouldn’t. Just like positions and people that should/should not be in meetings to resolve problems or people who should/should not have direct reports, we should consider positions and people that remote work is best for.

    And post-pandemic, the vast majority of DevOps roles can be performed effectively in a remote position. That leaves us with the people question. Mostly. Note: There are organizations and roles that need to be local; those are not a topic of this blog. The 99% of other roles/orgs are.

    This topic gets really ugly, pretty quick. If a significant portion of every organization goes remote, city centers in several metropolises will die. That is not conjecture; you can go look into commercial real estate in any information worker hub. It is in a massive slump (and calling it a ‘slump’ is being nice), with occupancy circling the drain and property values plummeting. That is largely because formerly high-demand real-estate is not needed in the volume it was.

    That should not be the concern of any given corporation. It is the corporation’s responsibility to see to shareholder value, not the real estate market (unless they are invested in real estate, which many large organizations are). But if orgs are keeping any offices downtown, it becomes an issue about safety. In other words, it’s about if the collapse of the market means the offices become less safe for workers, regardless of investment. This is not a decision-making bullet point, but it does inform actions. I have a ton more to say on this topic, but it’s a side bit, so for now, this is enough.

    For people who thrive working remotely—and a lot more people now know that about themselves than knew that five years ago—being forced into the office is a non-starter. I have been offered astounding positions at organizations on both coasts over the years, but the ones that came with the “…and you have to move here” demand—I immediately turned down. More people are doing that; some of them truly great employees.

    So, like it or not, DevOps teams are going to have to consider whether all being in the same building is more important than hiring the most qualified co-workers. That is a team/management/corporate decision and always has been. I consider any “We all do this one thing” stance—from “We’re an X shop” to “We don’t do remote” to be a negative, but I don’t have to make these decisions for an organization.

    It does need to be part of the conversation, though. Because the work environment is unlikely to return to pre-pandemic standards, no matter how much some want it to. The more organizations are willing to work with both remote and on-site employees, the more options those employees—your potential coworkers—have. That changes the dynamic, even in light of recent layoffs, because the new job hunt is not tied to “local” anymore.

  • Atlassian Embeds Generative AI Across Cloud App Portfolio

    Atlassian Embeds Generative AI Across Cloud App Portfolio

    At its Team 23 conference today, Atlassian announced it is adding an artificial intelligence (AI) capability to the enterprise editions of its cloud portfolio to automate a wide range of routine tasks.

    Sherif Mansour, distinguished product manager for Atlassian, said Atlassian Intelligence uses natural language to drive a virtual agent that resolves routine help desk issues from within Slack or Microsoft Teams. Atlassian Intelligence can also identify incidents that have been previously resolved along with issues that are related to one another.

    In addition, IT teams can also leverage Atlassian Intelligence to create summaries for projects being tracked using the company’s Jira project management software, noted Mansour.

    The overall goal is to streamline incident management processes while also making it simpler for application stakeholders to collaboratively participate in software development, he added.

    Generative AI capabilities are likely to be infused into almost every imaginable application going forward, but Atlassian is clearly at the forefront of an effort to leverage AI to improve productivity. For example, instead of spending time handling routine issues that increase the amount of daily toil, IT teams should be able to spend more time resolving more complex issues, noted Mansour.

    The cumulative effect of AI should result in a significant decrease in the backlog of tasks that many IT teams would otherwise have to manually perform, he noted.

    It’s not clear just yet how much generative AI will impact the way software is developed and how IT is managed, but it’s already apparent that fundamental change is on the way. The Atlassian Intelligence approach is intended to augment the capabilities of IT teams so they can do more with the same number of or fewer personnel.

    The challenge IT teams will face is identifying tasks they couldn’t previously accomplish simply because there wasn’t enough time available.

    Fundamentally, generative AI changes the way humans interact with machines. Instead of requiring a developer to create a level of abstraction to communicate with a machine, it’s now possible for machines to understand the language humans use to communicate with each other. That makes it possible for anyone to make use of large language models used to create generative AI platforms to, for example, automate a routine task or, one day soon, create code.

    At this point, like it or not, the generative AI genie is out of the proverbial bottle. Just about every job function imaginable will be impacted to varying degrees. In the case of IT organizations, the ultimate impact should involve less drudgery as many of the tasks that make managing IT tedious are eliminated.

    Hopefully, as IT continues to evolve, there will still be plenty of demand for human expertise. For better or worse, the nature of the expertise required, however, will undoubtedly be at a much higher level than it has been for the past four decades or more.

  • Start Automating Now

    Start Automating Now

    A bit of feedback I received from last week’s post is that I didn’t provide any background, I just jumped right in with examples. So let’s take a step back and talk about the bigger picture.

    It is always a surprise to me when I talk to enterprise friends and associates and hear that automation has not taken over their DevOps processes. Yes, we still (and might always) want the ability to have a person review what is being done and make course corrections if the automation has something wrong, but most of the automation that is available for DevOps is no longer new and should be a no-brainer.

    The ability for a build to block based upon any given criteria and notify Jira or ServiceNow—or whatever issue tracking system you use—while alerting the people responsible for the build is simply obvious … and yet, some organizations aren’t even that far yet. The ability to stop a build, create a ticket, assign it to the appropriate person and give it a priority is built into most of these products. It should be looked at to see if it’s suitable to the organization.

    This goes on and on. We’re now to the point where everything in the toolchain can be automated. IaC and GitOps are tackling the broad operations end, and if the application is 100% public cloud-based, CloudOps simply solves the problem. Automation from check-in to production is possible, with very few places where intervention from humans is needed.

    Given that most of this technology is mature—even though some of it has not been around as long but has broad enough adoption that it can be considered mature (Flux and Argo come to mind here)—it is time for enterprises to start another round of automation. Just like AI won’t eliminate most development jobs this year, automation won’t eliminate jobs, either. But we’ve been so strapped for time for so long that this may be a surprise. Automation might reduce the need to scramble for new team members while asking current employees to give a bit more … over and over.

    So get started. The math that made DevOps appealing in the first place—streamlining to reduce time to market and free up IT hours—is the same math that applies to the current round of automation. The more that can be automated, the more time team members will have to perform more productive work.

    Having mentioned AI in passing, I’ll use that as a segue to include AI in the “automation” discussion. At this time, nearly all of the tools we call AI are simply advanced automation tools and should be considered in the process of increasing IT automation. The ability for an app to run, learn your systems and improve them (be that improvement in development, security or ops) is just advanced automation. In the places where it works well in a given environment, it should be adopted. It is highly unlikely that AI will get worse over time, so even marginally acceptable AI results make it worth implementing, counting on technology advancements and continued training to improve that benefit moving forward.

    So, start automating now. Free your time, take a holiday at the beach, do the same job in less time or, to quote a previous generation, “Work smarter, not harder.” You’re doing the job that keeps the servers humming and users coming, now do it with less effort. And keep rocking it.

  • AI Chatbots: Game Changers for DevOps Transformations

    AI Chatbots: Game Changers for DevOps Transformations

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

    How AI ChatBots Improve DevOps Transformations

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

    Roadmap to Implement AI Chatbots in DevOps Transformations

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

    Benefits of AI Chatbots in DevOps Transformations

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

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

    Summary

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

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    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:

    Monster.com

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

    LinkedIn

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

    Dice.com

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

    Indeed

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

    SimplyHired

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

  • Can ChatGPT Fix Bugs? ‘Wolverine’ Dev Says YES

    Can ChatGPT Fix Bugs? ‘Wolverine’ Dev Says YES

    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: Instead of AI aids for programming, what about for debugging? The pseudonymous “BioBootloader” says he’s persuaded GPT-4 to make his code self heal. (more…)

  • Harnessing the Power of AI Chatbots for Continuous Testing

    Harnessing the Power of AI Chatbots for Continuous Testing

    Artificial intelligence (AI) is revolutionizing the way we develop and test software, especially with the advent of AI chatbots. These intelligent agents have the potential to streamline and automate continuous testing while improving software quality. However, software and QA managers must understand the urgency, opportunities, pitfalls and best practices associated with integrating AI chatbots into continuous testing processes. This blog explains crucial aspects to help your team harness the full potential of AI-driven continuous testing.

    The Urgency of Integrating AI Chatbots Into Continuous Testing

    • Increasing complexity of software applications: Modern software applications are becoming increasingly complex, which makes manual testing time-consuming, error-prone and costly. AI chatbots can analyze vast amounts of data in real-time, identify patterns and learn from the testing process, making them invaluable for continuous testing.
    • Fast-paced development and market demands: Today’s competitive market demands faster release cycles and shorter time-to-market. AI chatbots can help you accelerate testing and development cycles, allowing your team to focus on delivering high-quality software promptly.
    • The growing importance of customer experience: AI chatbots can simulate user interactions and predict user behavior, helping to improve user experiences by identifying potential issues before they become real problems.

    Opportunities for AI Chatbots in Continuous Testing

    • Improved test coverage and accuracy: AI chatbots can execute a large number of tests simultaneously, leading to better test coverage and increased accuracy.
    • Enhanced defect detection: AI chatbots can identify defects early in the development process, reducing the overall cost and time spent fixing them.
    • Efficient resource allocation: By automating repetitive tasks and reducing human intervention, AI chatbots enable teams to allocate resources more effectively, leading to increased productivity.

    Pitfalls to Avoid When Implementing AI Chatbots

    • Over-reliance on AI: AI chatbots are powerful tools, but they should not replace human testers completely. Human intuition, creativity and critical thinking are essential for effective testing.
    • Insufficient training data: To work effectively, AI chatbots require quality training data. Ensure that your chatbot is trained with diverse and representative datasets to minimize the risk of biased or inaccurate results.
    • Integration challenges: Integrating AI chatbots with existing systems and processes can be challenging. Ensure that your team has a clear understanding of the integration requirements and potential issues that may arise.

    Best Practices for Applying AI Chatbots to Continuous Testing

    • Define clear objectives: Establish clear goals and expectations for your AI chatbot to ensure that it aligns with your team’s overall testing strategy.
    • Choose the right AI chatbot platform: Consider factors like cost, scalability, ease of integration and support for programming languages when selecting an AI chatbot platform.
    • Continuously monitor and refine: Continuously monitor your AI chatbot’s performance and make necessary adjustments to improve its effectiveness and efficiency.
    • Foster collaboration: Encourage collaboration between human testers and AI chatbots to leverage the strengths of both.

    Benefits of Applying AI Chatbots to Continuous Testing

    • Improved test coverage and accuracy: AI chatbots can execute a large number of tests simultaneously, leading to better test coverage and increased accuracy. By automating repetitive and complex tasks, AI chatbots can effectively handle edge cases and efficiently test various scenarios, thereby reducing the likelihood of undetected issues in the software.
    • Enhanced defect detection and early issue identification: AI chatbots can quickly identify defects and potential issues early in the development process, reducing the overall cost and time spent on fixing them. By leveraging machine learning and pattern recognition, AI chatbots can predict potential problems and bring them to the attention of developers, allowing for proactive resolution and improved software quality.
    • Efficient resource allocation and increased productivity: By automating repetitive tasks and reducing human intervention, AI chatbots enable teams to allocate resources more effectively. Human testers can focus on higher-level tasks that require creativity, critical thinking, and domain expertise. This leads to increased productivity, as the team can concentrate on delivering high-quality software and improving user experiences.

    Conclusion

    AI chatbots are transforming the landscape of continuous testing, offering unprecedented opportunities to improve software quality and speed up development cycles. By understanding the urgency, opportunities, pitfalls and best practices associated with integrating AI chatbots into continuous testing processes, software managers can effectively harness the potential of these intelligent agents and drive their teams toward success.