devopsy.kinsta.cloud

Author: Mike Vizard

  • Vercel Adds Observability Tools to Web Application Framework

    Vercel Adds Observability Tools to Web Application Framework

    Vercel today announced it has added a suite of observability tools to its platform for building web applications using Next.js, an instance of the open source React framework based on a JavaScript library.

    Vercel CTO Malte Ubl said the Vercel Monitoring tools make it simpler for developers to troubleshoot applications without relying on a DevOps team to surface data. Instead, developers can take advantage of Vercel Monitoring tools to access metrics that track user interface and application programming interface (API) issues, he added.

    That capability is not intended to replace the need for observability tools employed by DevOps teams. Instead, the goal is to provide metrics that are specific to issues that developers encounter as an application is being built or after it has been deployed so they can be addressed.

    Developers can select from pre-made queries provided by Vercel or create their own by defining what metrics they want the Vercel Monitoring tools to track, including bandwidth and web traffic spikes and requests by bots that are indicative of a cyberattack. In total, there are more than 500 potential errors that developers can track. Logs then make it possible to drill down into errors and issues once they are identified to pinpoint the root cause of an issue.

    As more responsibility for applications shifts left toward developers, there’s a clear need for tools that provide more visibility into the applications they develop. Many of the issues that DevOps teams encounter today could have been resolved if developers had been aware of them earlier.

    The Vercel platform employs caching, routing and a React framework to optimize application performance in a way that reduces the dependency on backend infrastructure to optimize application performance. That approach puts less strain on backend infrastructure as more requests are automatically processed using a framework that spans clients and servers. In effect, developers can take advantage of frameworks such as React to treat the underlying IT infrastructure as if it were serverless whenever possible with tools that have a familiar JavaScript construct. The overall goal is to increase developer productivity at a time when many organizations are much more focused on the issue.

    As a result, DevOps teams will need to make some adjustments as more applications capable of providing richer experiences are built and deployed without relying as much on backend infrastructure. That shift has implications for everything from the amount of network bandwidth consumed to the performance of web applications. Frameworks such as Next.js also tend to increase the overall rate at which web applications can be built and deployed.

    One way or another, it’s clear that the way web applications are being built is fundamentally changing. The only issue now is determining to whether DevOps teams are prepared to absorb that level of change across an ever-increasing portfolio of applications that still need to be deployed, secured, updated and optimized.

  • The Economic Downturn and DevOps Hiring Trends

    The Economic Downturn and DevOps Hiring Trends

    An analysis of more than 494,000 interview requests for software engineers coupled with a survey of more than 1,300 software engineers and 120 hiring professionals found a lot more employers prefer to hire DevOps professionals who work in the office—but resistance to working in an office (rather than working remotely) remains high.

    The survey also found that, despite the preference for hiring local software engineers, more than half of hiring professionals (52%) are filling positions on fully remote teams while another 40% are focusing on hybrid teams.

    The interview requests analysis conducted by Hired, a provider of a platform for vetting potential employees, found that nearly three-quarters of interview requests (72%) were for software engineers with more than six years of experience. Backend engineers received 59% of all interview requests.

    Hired CEO Josh Brenner said the survey makes it clear that organizations are less inclined to hire less experienced talent. In fact, there’s a decided shift toward DevOps professionals that have degrees versus individuals that are largely self-taught, he added.

    Hiring those individuals, however, remains challenging. More of them are willing to consider other employment options given the current state of the economy, but more than two-thirds of engineers (68%) said they are not worried about losing their jobs in the next six months.

    However, enterprise IT organizations are now finding it easier to hire DevOps engineers because competition from startup companies and technology vendors such as Google and Microsoft slackened in the wake of a series of ongoing layoffs, noted Brenner.

    Competition for software engineers is also impacted by geographic region, with the Los Angeles area seeing the largest negative impact on local salaries, decreasing by 6% to $152,000. Philadelphia had the greatest amount of growth, with local salaries increasing by 7% to $150,000. Despite demand for remote engineering talent, growth in salaries for these positions has flattened. The San Francisco Bay Area had the highest salaries for remote roles at $180,000, followed by Seattle ($175,000) and New York ($169,000). The percentage of active engineering positions “open to remote” fell to 69%, the survey found.

    The highest-paying markets for in-office jobs, in contrast, were San Francisco ($180,000), Seattle ($169,000) and New York ($162,000).

    Software engineers, as always, are motivated to work for organizations based on new challenges and continuous learning (69%), building products/solutions and problem-solving (66%) followed by earning potential and career stability (60%), the survey found. When learning a new skill, the survey found the biggest motivator remained the simple joy of learning new things (40%) followed by the fact that their current job required it (25%).

    Regardless of the state of the economy, it’s clear that demand for DevOps expertise continues to outstrip available supply. However, software engineers may be unable to command as high a salary as they did a short while ago. Of course, no one can be sure what lies ahead, but economies do eventually rebound. In the meantime, DevOps professionals would be well-advised to continue to invest in their own skills to maximize their earning potential, no matter which direction the economic pendulum swings.

  • 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 as part of an effort 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 creates 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 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

    LongTail Ad Solutions
    New York, New York
    Senior DevOps Engineer
    $170,000

    LinkedIn

    TuneIn
    New York, New York
    Staff Infrastructure Automation Engineer
    $166,000 to $249,500

    SimplyHired

    The Hartford
    Remote
    Senior Staff Software Engineer
    $123,120 to $184,680

    Indeed.com

    Volunteer Corporate Credit Union
    Nashville, Tennessee
    Senior DevOps Engineer
    $109,000 to $139,000

    Dice.com

    MRoads
    Phoenix, Arizona
    DevOps Engineer- Associate
    $100,000 to $120,000

  • Tabnine Extends Generative AI Platform Reach to Testing

    Tabnine Extends Generative AI Platform Reach to Testing

    Tabnine today announced it extended its generative artificial intelligence (AI) platform that enables developers to write code faster to now include the ability to automatically generate tests for that code.

    Available in beta, this capability promises to make it easier for developers to test code at the time it is being developed. That approach should make it simpler for developers to address issues without losing any context.

    Brandon Jung, vice president of ecosystems and business development at Tabnine, said this latest addition to the Tabnine platform is part of a larger effort to extend the reach of the Tabnine generative AI platform across the entire software development life cycle. The testing capability is expected to be generally available in the third quarter, and Tabnine expects to also employ generative AI to automate a much wider range of tasks across the software development life cycle within the next 12 months.

    The Tabnine platform supports multiple programming languages including Python, Java and JavaScript and is designed to be integrated with integrated development environments (IDEs) such as Visual Studio Code and Jetbrains. The company has also previously integrated its code completion tool with the GitLab continuous integration/continuous delivery (CI/CD) platform. The overall goal is to make it easier for developers to automatically write code based on custom models using approved source code hosted in a secure private repository, said Jung.

    Generative AI creates a large language model that assesses the probability of what the next line of code will be based on what has preceded it. Tabnine is now extending the model it created to also generate tests for the code its platform helped a developer write. It’s not clear what impact this ability to automate test creation will have on the need for dedicated testing teams, but it is about to become considerably easier to shift more responsibility for testing further left toward developers.

    It’s not likely DevOps teams will be replaced as generative AI is extended across workflows, but the overall size of those teams might shrink, noted Jung. DevOps teams will soon be able to do far more with fewer people, he noted. At the same time, the barrier to DevOps adoption will also fall as AI platforms make it simpler for more organizations to embrace DevOps best practices, added Jung.

    The challenge will be making sure the data collected to train AI models is of a high enough quality to ensure the desired outcome, noted Jung.

    One way or another, it’s now only a matter of time before generative AI capabilities are applied more broadly. Platforms such as ChatGPT developed by OpenAI are only the tip of the iceberg that impacts almost every manual process, including software development and deployment. The issue will be determining at what rate those innovations will become practical to employ. In the meantime, it is already apparent generative AI platforms are having a significant impact on the rate at which code can be developed. Inevitably, that means the amount of code moving through DevOps pipelines at any one time is about to increase significantly.

  • Nobl9 Adds Service Level Analyzer to SLO Management Platform

    Nobl9 Adds Service Level Analyzer to SLO Management Platform

    Nobl9 today added a free tool for analyzing service level objectives (SLOs) to its platform to enable DevOps team to more accurately set reliability goals.

    Brian Singer, chief product officer for Nobl9, said Service Level Analyzer collects metrics and observability data from more than 24 DevOps tools and platforms that can then be used to create an SLO via a single click.

    Included as part of the free tier of the Nobl9 service, Service Level Analyzer also enables DevOps teams to more easily update SLOs as error budgets and error budget burn rates change over time based on actual incidents that have occurred, added Singer.

    DevOps teams can also perform “what-if” analysis to test out the impact of different targets and thresholds on error budgets, he added. That capability ultimately reduces costs because DevOps teams become less likely to overprovision resources to ensure application performance and resiliency, noted Singer. In effect, Service Level Analyzer is providing DevOps teams with an observability tool that spans the entire software development life cycle, said Singer.

    Nobl9 is trying to spur greater adoption of SLOs by making available an open source SLO specification that defines a common interface for constructing SLOs across a DevOps workflow. SLOs, of course, are not a new idea. They have been employed as a metric to track the performance of IT services for decades. However, as more microservices-based applications are built and deployed, it’s becoming more challenging to maintain SLOs across applications because they have many more dependencies than legacy monolithic applications.

    Ultimately, each DevOps team needs to provide some sort of objective benchmark that assesses their overall effectiveness at delivering application services. SLO-as-code is intended to make it simpler to gather the metrics that confirm whether service levels are being achieved. As organizations become more dependent on software to drive digital processes, those SLOs are then tracked across everything from financial services to supply chains.

    In general, DevOps teams are more comfortable with the SLO concept, said Singer. The biggest challenge is making them simpler to implement, he added. The Service Level Analyzer lowers the bar for implementing an SLO by making historical data stored in DevOps tools and platforms more accessible, noted Singer. Rather than having to manually collect that data and then normalize it in an external repository, Nobl9’s Service Level Analyzer automates that process on behalf of a DevOps team using actual data versus relying on less precise statistical analysis, said Singer.

    As IT environments become more distributed, the number of dependencies that exist across application environments only continues to expand. IT teams can spend weeks trying to discover the root cause of issues that are negatively impacting application performance, only to discover that, for example, calls to an application performance interface (API) are being rerouted in a way that significantly increases latency. Tools that enable DevOps teams to more easily understand the relationships between all the services that make up an application environment are more crucial than ever.

  • Tricentis Extends Mobile Testing Reach

    Tricentis Extends Mobile Testing Reach

    Tricentis has added the ability to test native iOS and Android applications to its Testim software-as-a-service (SaaS) platform.

    Previously, the Testim platform was designed to test web applications, but is now being extended as organizations build and deploy more native mobile applications. Capabilities of the Testim Mobile platform include codeless authoring of mobile tests to eliminate the need to write and maintain Appium, Espresso or XCUITest scripts and the ability to set up, configure and manage mobile devices in minutes using the Tricentis Mobile Agent.

    Additionally, applications can be uploaded to the cloud to enable collaboration across testing teams as they opt to run tests on real or virtual devices. DevOps team can also customize tests with JavaScript, branch tests, and run tests from a scheduler or continuous integration (CI) build.

    Tricentis CTO Mav Turner said Tricentis is moving to implement a consistent set of mobile testing services across its entire portfolio of testing platforms now that mobile applications are equal, first-class citizens alongside web applications. The primary driver of that shift has been the COVID-19 pandemic, which saw more organizations relying on external-facing mobile applications to engage customers, partners and employees working remotely, he added.

    Testing those applications is more critical than ever because the cost of failure—in terms of impact to the business—is now much higher as more organizations depend on software to drive business processes, noted Turner.

    The goal is to make it simpler to test these applications as they are being developed to ensure quality, he added. The challenge is that many developers don’t have a software engineering mindset. Hence, they need to rely more on testing platforms infused with artificial intelligence to discover issues relating to, for example, scalability and security, said Turner.

    Most organizations are trying to shift more responsibility for testing toward developers as part of an effort to remediate issues earlier in the software development life cycle. It’s more challenging for developers to remediate issues after an application has been developed simply because they have lost context. Testing platforms infused with AI capabilities make it easier for developers to identify issues at the time they are writing code. In the future, Tricentis expects to take advantage of generative AI platforms to make it easier to author tests using a set of best practices that can be easily reused, noted Turner.

    That approach doesn’t necessarily eliminate the need for a dedicated quality assessment review, but it does allow testing teams to focus more of their time and effort on testing the application experience versus identifying lower-level errors that many developers routinely make.

    Over time, the quality of applications should steadily improve as many of the low-level mistakes that developers make are eliminated. That should improve everything from application security to the user experience as applications are continuously updated. In the meantime, organizations that embed testing within DevOps workflows are steadily reducing the number of issues that need to be addressed just before applications are supposed to be deployed.

  • Logz.io Integrates ChatGPT With Observability Platform

    Logz.io Integrates ChatGPT With Observability Platform

    Logz.io has integrated the ChatGPT generative artificial intelligence (AI) platform with its Open 360 observability platform to extend its automated recommendation capabilities to reduce mean-time-to-remediation.

    ChatGPT, developed by OpenAI, is based on a large language model that Logz.io uses to enrich crowdsourced data. That approach enables ChatGPT to surface links to related information and best practices for resolving IT issues.

    Logz.io CTO Asaf Yigal said the company has integrated ChatGPT with its existing AI engine, dubbed Cognitive Insights, that applies machine learning algorithms to identify anomalies indicative of potentially critical IT issues. The company expects to be able to add other generative AI capabilities as additional advances are made, he added.

    The Logz.io Open 360 platform is based on open source OpenSearch, Prometheus and Jaeger software that the company has infused with machine learning algorithms. The integration with ChatGPT is enabled via an application programming interface (API) that OpenAI has provided.

    The overall goal is to employ AI to surface the most relevant issues a DevOps team needs to focus on without having to search huge volume of logs, tracing and metrics data, said Yigal. Generative AI platforms solve a fundamental cognitive load problem created today as DevOps teams collect more data, noted Yial. The more data a DevOps team collects, the harder it becomes to determine what’s going on in an IT environment, said Yigal. He added that generative AI platforms make it possible to analyze massive amounts of data at a scale that isn’t otherwise achievable.

    AI tools should make it possible to manage IT at a level of scale that eliminates many of the low-level data engineering and analytics tasks that previously required manual effort from a DevOps engineering team. It’s not clear how many other DevOps tools and platforms will be invoking generative AI capabilities in the months ahead, but DevOps teams should assume that large language models will soon be augmenting DevOps teams in ways that previously would have been thought unlikely.

    For example, discovering vulnerabilities in software running in production environments is about to become trivial as artificial intelligence (AI) platforms such as ChatGPT evolve. A generative AI platform can identify code vulnerabilities in much the same way as a traditional scanning tool. An AI platform, however, could theoretically monitor code repositories and scan them for vulnerabilities as updates and commits are being made in real-time. DevOps teams should be able to use generative AI platforms to discover and remediate these issues before code is deployed in a production environment.

    It’s just as probable bad actors will use the same platform to achieve the same goal, so from that perspective, many DevOps teams will now find themselves in an AI arms race to improve DevSecOps workflows.

    There are still lots of issues to be worked out in terms of the overall accuracy of generative AI platforms, but they will only become more intelligent as processors continue to advance. The issue now will be determining precisely where the man-machine interface for DevOps teams is today and in the future.

  • 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 as part of an effort 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 creates 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 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

    Pyramid Systems
    Remote
    AWS Cloud DevOps Platform Engineer
    $160,000 to $200,000

    Monster.com

    Apidel Technologies
    Los Angeles, California
    Software engineer
    $140,000 to $160,000

    SimplyHired

    Cox Communications
    Atlanta, Georgia
    Senior DevSecOps Cybersecurity Architect
    $120,000 to $150,000

    LinkedIn

    EdgeLink
    Beaverton, Oregon
    DevOps Engineer
    $110,000 to $120,000

    Indeed.com

    CrowdStrike
    Remote
    DevOps engineer
    $105,000 to $195,000

  • Technical Debt is a Major Threat to Innovation

    Technical Debt is a Major Threat to Innovation

    A survey published today suggested that technical debt is starting to take a heavier toll as IT environments continue to become more complex to manage.

    The survey of 400 IT leaders at organizations with 1,500 employees was conducted by Foundry, the market research arm of International Data Group (IDG) on behalf of Insight Enterprises, a provider of IT services. The survey found that 86% of respondents reported their organization was impacted by technical debt over the past 12 months, with the top areas affected being the ability to innovate (43%), meet service level agreements (41%) and avoid downtime (37%).

    Technical debt is created when organizations cut corners and leave known bugs in the race to meet application delivery deadlines. Application code is often not as thoroughly tested as it should be. In some cases, it turns out that what was intended to be a minimum viable product doesn’t scale all that well. Quick fixes needed to improve performance are also frequently undocumented. Inefficient code also winds up consuming more IT infrastructure than required, which adversely impacts everything from cloud costs to the amount of carbon generated.

    Juan Orlandini, chief architect for the Cloud + Data Center Transformation (CDCT) division of Insight Enterprises, said every organization struggles with technical debt; it will never be eliminated, but it does need to be contained, he added.

    The challenge is instead of trying to keep the proverbial IT lights on during the COVID-19 era, IT teams are now being asked to innovate to advance digital business transformation initiatives, said Orlandini. A full 87% of survey respondents cited modernizing critical applications as a key success driver.

    As a result, many organizations are embracing platform engineering to bring more structure to their DevOps processes, he noted. The challenge, however, is striking a balance between a more centralized approach to DevOps and maintaining the ability of developers to innovate, said Orlandini.

    The issue, of course, is that in addition to massive technical debt, the latest generation of applications are more distributed than ever. The survey found 91% of respondents now rely on multiple public cloud providers for different workloads, with 54% of data residing on a public cloud. However, the survey also found on-premises IT environments are still relevant, with 20% planning to repatriate select public cloud workloads to an on-premises model over the next 12 months.

    Nearly three-quarters (72%) of respondents also noted data volumes are growing faster than their ability to manage them, with 64% ranking implementation of a data governance program to improve data management as their top data-related objective this year. Just over a third (35%) of the data estate is now, on average, driven by edge workloads, with 29% of respondents reporting that at least 50% of data volumes now reside on the edge. Nearly two-thirds (64%) expected that to increase in the next 12 months.

    A total of 85% of respondents also indicated their organization is using artificial intelligence (AI) to some degree to drive business insights and/or efficiencies, although only 36% reported having optimized their use of AI and machine learning (ML) across the enterprise.

    Finally, cybersecurity also remains problematic, with 51% reporting they were impacted by a cybersecurity breach over the past 12 months. Just 20% resolved their issue in one day, while 31% needed less than a week. Nearly half (49%) required one week or more to recover.

    It’s clear that IT isn’t becoming any easier to manage. IT teams need to find ways to build and deploy new applications while reducing technical debt so it doesn’t strangle future innovation.

  • Fiberplane Adds Wasm Plug-in Framework for DevOps Notebook

    Fiberplane Adds Wasm Plug-in Framework for DevOps Notebook

    Fiberplane today announced it is extending the reach of a real-time collaboration notebook designed for DevOps teams by making available tools for building plug-ins based on the WebAssembly (Wasm) framework.

    Micha Hernandez van Leuffen, Fiberplane CEO, said these plug-ins, dubbed providers, will make it much simpler for anyone to connect the Fiberplane notebook to other DevOps tools and platforms.

    The Fiberplane notebook uses the same operational transformation algorithm used in Google Docs to enable teams to easily collaborate around a document or spreadsheet. The core platform itself is built in Rust and WebAssembly. That approach enables a DevOps team to, for example, write and run PromQL queries and display Prometheus charts alongside other data presented via an integrated graph. Wasm makes it possible to run those providers both in the browser and on a backend platform.

    Fiberplane also announced it is open sourcing its Provider Development Kit (PDK), written in Rust, to enable organizations or third-party developers to create additional plug-ins. A Fiberplane Daemon then executes the plug-ins. The overall goal is to provide an open format based on Wasm for consuming DevOps data, noted Hernandez van Leuffen.

    The full open source Fiberplane WebAssembly-based Plugin System includes a Fp-bindgen to provide the bindings generator for building plug-ins, the Provider Development Kit (PDK), a toolchain for building plug-ins, a set of pre-configured plug-ins for Prometheus, ElasticSearch, Sentry, Grafana Loki, Cloudwatch and a generic HTTP provider, the Fiberplane command line interface and the Fiberplane Daemon.

    Fiberplane is essentially taking the concept of notebooks used for collaboration by data science teams and applying it to DevOps workflows. The goal is to improve DevOps teams’ productivity by reducing the amount of time it takes to sort through multiple types and classes of DevOps data to resolve an issue.

    As IT environments become more distributed in the age of the cloud and microservices, troubleshooting issues now invariably spans the responsibilities of multiple members of an IT team. Fiberplane, in effect, makes it possible to spin up a virtual war room that enables the members of an IT organization to collaborate until the issue of the moment is resolved.

    The speed at which any IT issue is resolved has become a crucial metric. Given the complexity of IT environments, it’s inevitable that incidents will arise. Ideally, IT teams would discover those issues before they disrupt the business. However, in reality, the metrics organizations use to evaluate the effectiveness of a DevOps team are often how quickly IT issues are resolved once they are discovered. The longer it takes for a distributed DevOps team to marshal its resources, the more likely it becomes that team will be perceived as being inefficient—regardless of what level of application availability is actually achieved.

    Most DevOps teams have, of course, implemented a wide range of incident management best practices that create a level of muscle memory for responding to a wide range of issues. The trouble is there will always be unforeseen issues that arise. DevOps teams, as a result, need to find ways to quickly focus on an unpredictable issue that they didn’t anticipate.