Category: DevOps Culture
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The Ruby on Rails Resurgence
Ruby combines functional and imperative programming to create an easy-to-use, powerful language where everything is an object. Introduced in 1995, the open source programming language became popular in the 2000s during the dot-com era, when developers at startups and established companies were under pressure to rapidly launch new web applications.
Ruby on Rails, released in 2004, is a framework for Ruby that provides foundational structures for building and deploying web applications. Ruby on Rails enabled even faster development because it provided most of the nuts and bolts. Thanks to Rails, Ruby’s popularity continued to soar through the early 2010s. Among the many applications built with Ruby on Rails are Airbnb, Basecamp, Github, Hulu and Shopify.
Ruby on Rails took a backseat to JavaScript in the late 2010s, as rich front-end clients and single-page applications became more common. But even in these use cases, many developers continued to use it to rapidly build out an API layer built for Javascript front ends, in large part because Rails makes development so simple. While Ruby on Rails eased development, it also created issues, such as logic appearing in both layers, especially with functions like authentication.
Yet, Ruby on Rails’ popularity has resurged in recent years. Hired’s 2023 State of Software Engineers report found Ruby on Rails was the most in-demand skill for software engineering roles. Proficiency with Ruby on Rails resulted in 1.64 times more interviews than those without it.
This resurgence has been significantly bolstered by innovations like Hotwire and the sorting out of its JavaScript integration. JavaScript’s dominance in creating interactive, client-side applications challenged Ruby on Rails, particularly as single-page applications grew in popularity. Hotwire, a modern tool designed to streamline the use of HTML over the wire, allows developers to create highly interactive applications in Ruby on Rails with minimal JavaScript. This integration simplifies the development process by keeping Ruby on Rails at the forefront of both backend and frontend developments, which enables more seamless, full-stack development experiences. By resolving complexities associated with using JavaScript, Ruby on Rails has enhanced its appeal, enabling quicker development cycles and reducing the need for extensive cross-disciplinary expertise among development teams. This dual-front optimization is crucial for Ruby on Rails’ continued relevance and competitiveness in a fast-evolving tech landscape.
Another reason Ruby on Rails is resurging is its ability to encourage speed of development — an important element in business innovation.
One reason is that Ruby on Rails encourages speed of development — an important element in business innovation. Organizations always want technology innovation, but particularly so when they need to cope with the challenges of doing business in the 2020s. They need resilience, flexibility and scale to remain competitive and grow. Disruption, and not in a good way, has become the norm for business. According to a 2023 IDC report, three in four organizations say their business functions are dependent on innovation, and nearly half (49%) feel keeping pace with technology innovation is a major risk for 2024. That’s exacerbated by uncertainties in business supply chains and other financial uncertainties.
In short, development teams are being asked to produce more with the same or fewer resources, and that’s precisely where Rails shines.
Another driver is the recognition of the importance of developer happiness. The contentedness of professional developers is critical to productivity, as well as attracting and retaining top talent. According to Spiceworks, in 2023, recruiting qualified talent was the software industry’s primary challenge.
Further, in the Hired report, the top two draws for software engineers to their careers were:
- New challenges and continuous learning (69%)
- Building products and problem-solving (66%)
Ruby on Rails enables developers to indulge in exactly these pursuits. They spend much more time on the challenging, creative aspects of projects because the framework takes care of the components that are a part of nearly every web application.
Ruby on Rails is having a much-deserved resurgence because it enables teams to rapidly develop innovative web apps for clients who need to see them deployed quickly. Just as important, it improves developer happiness. And, as a free, open-source project, it’s available to all. So, while it may not be the right framework and language for every project, in many cases, it can be the secret sauce that enables faster-than-expected delivery and more developers to make it home in time for dinner after the arduous commute from the back bedroom office.
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Five Great DevOps Job Opportunities
devopsy.kinsta.cloud is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals are highlighted. Our goal in these challenging economic times is to make it just that much 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 hiring company, 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:
Indeed.com
SAP
Bellevue, WA
DevOps Engineer
$182,312CareerBuilder.com
HRL Laboratories
Malibu, CA
Senior DevOps Engineer
$145,745 to $186,688LinkedIn
Visa
Atlanta
DevOps Data Engineer
$142,000 to $179,900SimplyHired.com
Amazon Web Services (AWS)
Seattle
Senior Software Development Engineer
$134,500Dice.com
Travelers
Hartford, CT
DevOps/Machine Learning Operations (MLOps) Engineer
$94,900 to $156,600 -
How Live Team Coaching Can Solve Tech’s Skills Problem
Tech has a skills issue – and it’s one that’s not going away in 2024. Live team coaching can be an important part of the solution.
‘The skills gap has become somewhat of a fixture in the tech industry, and after a turbulent year, rapid tech hiring is no longer a viable option for the many companies pursuing more productivity or innovation at lower cost.
With VC funding less forthcoming in 2023, teams slimmed down to make ends meet. Leaders are being pushed to do more with what they already have. That’s not helped by the rapid pace of technological change – it’s estimated that the half-life of technical skills is already less than 2.5 years. That means that skills gaps are opening up within teams all the time, and competition is fierce for software engineers with the right niche of skills (e.g., deep expertise in AI and data science). After all, non-tech companies, such as those in retail and finance, now want those developers, too. And remote working has meant local talent is being pursued by the whole world.
It’s a crisis that has the potential to severely curb business ambitions. A 2023 Gartner survey found technology executives see the talent shortage as the most significant adoption barrier to 64% of emerging technologies that enable innovation versus 4% in 2020. Not being able to utilize the best technology is leading to delays, low productivity, more errors and the release of inadequate products.
The tech sector is between a rock and a hard place, and something needs to change. By considering how skills are nurtured internally, startups and scaleups can begin to overcome the barriers holding them back.
Coaching Can be the Fast Track to Team Success
The tech industry has been addicted to hiring as a means of improving a team’s abilities and an organization’s output. That’s no longer a viable option. The sector needs a marked shift in favor of developing its current people so they’re agile, resilient and rapidly able to achieve expertise in technologies with ease.
This is important for software developers, too. Learning and growth are a developer’s second priorities when choosing a job – higher than their compensation. Historically, this need has been half-solved by giving people access to resources – books, videos, lunch and learns, etc. These kinds of perks have become the standard, and they remain nothing more than perks. Few leaders have invested in meaningful learning and real growth in their existing developers’ capabilities toward a strategic goal. What would that look like? Adopting technologies, increasing abilities, and improving outputs through learning rather than hiring.
Live team coaching taps into the potential of a company’s people. It focuses on the specific needs and goals of an organization and the skills gaps that exist within the individuals on a team. With regular coaching from a live expert, software engineers can build a deep understanding and develop new capabilities through problem-solving and feedback. More innovative features are built faster, with higher-quality code and fewer bugs. And the confidence and resilience of the team as a whole is improved. Technologies and targets that once seemed out of reach become achievable.
That has clear business benefits. With next-generation technologies, developers can work more productively and innovatively. AI, for example, can automate large parts of coding and debugging, leaving software engineers with more capacity for higher-level work. Research by GitHub, which polled 90,000 developers in its most recent survey, found a third of developers said AI has increased their productivity, and one in four are achieving greater efficiency.
Targeted learning can also help developers work smarter, removing bottlenecks and streamlining workflows. Almost two-thirds (63%) of the developers GitHub surveyed spent more than 30 minutes a day searching for answers or solutions to problems, and 53% agreed they’re slowed down at work by waiting for answers. Imagine the boost in productivity if they had access to targeted learning, which would bring solutions to those problems well within reach.
Confidence and Resilience
That’s what happened at Datamaran. The company has created a software analytics platform that identifies and monitors the risks and opportunities of ESG strategies, and they have been using live team coaching with its 20 developers for just over a year, with a particular focus on developing their skills in Python and JavaScript.
Datamaran’s CTO and co-founder Jérôme Basdevant has noted that it’s already had a big impact. Efficiency has improved significantly, and the team can now appropriately use more advanced coding patterns and practices in their work. Basdevant says they’re more autonomous now because he can trust them to write code correctly. Plus, the team has improved their understanding of the complexity of estimation, which has resulted in a more predictable workflow. He’s also been able to hire people from more diverse backgrounds and use live coaching to upskill them on the job.
Constantly Bridging the Gap
In some respects, the industry must become comfortable with the fact that the skills gap is here to stay–with rapidly changing technology, then the skill set of a perfect team will be a moving target. However, if organizational leadership—and that means the whole C-suite, not just the CTO—finds an ongoing continual solution, then teams will reach their potential and achieve more. If managers change their mindset and adopt long-term and continuous learning, which is built into the working day, then their teams will have the ability to not just keep moving forward but to accelerate.
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Survey Sees AI Playing Larger Role in Test Automation
A survey of 1,028 senior IT professionals and application developers in the United Kingdom, U.S., Germany and Singapore finds nearly half (47%) still use manual testing for mobile applications. More than a third (38%) noted that their organization could save between half and three-quarters (51-75%) of funding allocated to testing per year by fully automating mobile application testing.
Conducted by the market research firm Censuswide on behalf of Tricentis, a provider of a test automation platform, the survey also found that just under half of respondents (49%) reported their organization is already applying artificial intelligence (AI) to application testing. Another 21% plan to apply AI in the next six months. Almost three-quarters (74%) said their organization’s general sentiment toward AI in mobile software development testing is positive.
Respondents also expected AI to improve mobile application quality (37%), increase productivity (36%) enhance end-user experience (34%) and to reduce the number of vulnerabilities and/or bugs (32%) in their applications.
Mav Turner, chief product and strategy officer at Tricentis, said that rather than continuing to rely on expensive testing services, more organizations are looking to reduce costs by automating a wider range of testing workflows within the context of a DevOps workflow. In fact, the survey finds just under a third of respondents (32%) expect their organizations will invest more than $1 million to further automate testing.
As modernization of testing processes continues, the return on investment in mobile application development will only continue to improve, noted Turner.
The survey identifies the primary benefits of investing in test automation, which are increased productivity (32%), increased revenue (31%), increased user retention (28%) and increased compatibility across devices (28%).
The primary obstacles organizations encounter as they strive to achieve that goal are competing priorities (28%), lack of technology and talent (25%), time (25%) and cost (24%), the survey finds.
A full 87% said the quality of mobile applications is critical to their business, with 90% noting that poor mobile application quality costs their business as much as $2.49 million in lost revenue per year. However, just over a quarter (27%) said they believe their organization’s current mobile application development and testing strategy exceeds expectations.
As part of efforts to automate testing, it’s not clear just how far responsibility for testing is shifting left toward developers, but there is no doubt many more are being exposed to the results earlier in the software development life cycle. The goal is to provide continuous testing capabilities without overly disrupting code writing. It’s also becoming simpler for developers to leverage generative AI tools to provide summaries of tests along with recommendations to improve code. The end result should be higher-quality applications that require less troubleshooting once deployed in a production environment.
In the meantime, as organizations continue to become more dependent on mobile applications to drive a wide range of digital transformation initiatives, the cost of testing will continue to pale in comparison to the potential amount of lost revenue that might occur when end users either don’t invoke a specific feature or capability or, more commonly, simply abandon the application altogether.
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Survey Surfaces Substantial Platform Engineering Gains
A global survey of 500 application development and deployment professionals finds that while 43% work for organizations that have had a platform team in place for three to five years, there doesn’t appear to be much consistency regarding where that team reports within an organization.
The survey, conducted by Puppet by Perforce, a provider of an automation framework, found that among organizations that have platform teams, well over half (58%) operate within the context of a larger DevOps or infrastructure management organization, compared to 40% that have their own dedicated leadership.
David Sandilands, a principal solutions architect for Puppet by Perforce, said the survey makes it clear that platform engineering, in general, is not a new concept, but it’s clear more organizations are trying to reduce the level of toil currently associated with building and deploying applications.
Regardless of where platform teams reside in an organization, nearly two-thirds of respondents (65%) said they are important to their organizations and would receive continued funding. The primary purpose for forming these teams is to increase productivity (58%) and automate standardized processes (51%), the survey finds. Top goals for platform engineering teams are to solve problems (30%), enforce security processes (27%) and accelerate transitions to cloud-native computing environments (26%). A full 70% said security was built into their platforms from the start.
That suggests that, in addition to assuming responsibility for traditional DevOps workflows, many platform teams are now also being asked to ensure DevSecOps best practices are followed at a time when application security requirements are becoming more stringent, noted Sandilands.
More than three quarters (76%) also reported that they had deployed two or more self-service portals, with 27% having deployed five or more.
Only 22%, however, said they have deployed applications on Kubernetes in a production environment, with just under half (46%) reporting they have no current plans to deploy a cloud-native computing platform, which are widely seen to be challenging to deploy and maintain.
Overall, the survey makes it clear the primary focus is to ensure platforms continue to evolve as developer requirements continue to evolve, said Sandilands. Platform teams are not necessarily trying to dictate what tools should be used; instead, they are finding ways to automate processes that are increasing the level of friction developers encounter, he added.
In the longer term, advances in artificial intelligence (AI) will make it simpler to achieve that goal using natural language interfaces that DevOps teams can more easily invoke, noted Sandilands.
Each organization will need to decide for itself whether platform engineering makes sense as a methodology for managing DevOps at scale, but as application development and deployment continues to evolve, more nuanced approaches will be required. In general, organizations are trying to strike a balance between empowering developers and the need to streamline backend processes to increase productivity while also simplifying compliance and improving security.
The challenge, of course, is enticing developers to buy into that concept rather than using their expertise to resist platform teams that might, from their perspective, limit their prerogatives in ways that ultimately stifle innovation.
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Survey Surfaces Lack of Significant Observability Progress
A global survey of 500 IT professionals suggests organizations are not making a lot of progress in their ability to truly observe application environments, especially as they become more complex with each passing day.
The survey, conducted by Logz.io, a provider of an observability platform, found only one in 10 respondents reported they have full observability into their application environments.
Logz.io CTO Asaf Yigal said while more DevOps teams than ever are collecting logs, metrics and traces, most of them have yet to determine how to operationalize all the data being collected. As data is ingested, it doesn’t just need to be stored; it also needs to be correlated to the various services that make up an application, he noted.
The more organizations deploy cloud-native applications in production environments the more pressing this issue becomes. All the microservices that make up those applications are now generating a massive amount of telemetry data that results in more alerts than ever being generated.
Not surprisingly, the biggest challenges organizations encounter when managing Kubernetes clusters in production environments are monitoring/troubleshooting (40%), followed closely by security (37%) and networking (33%).
Many organizations simply don’t have the skills required to manage cloud-native applications. Nearly half of respondents (48%) specifically cited a lack of knowledge as the biggest challenge they encountered when trying to observe these types of applications. From an IT management perspective, most microservices all use the same basic template, so it’s difficult for DevOps teams to identify which microservices are likely to have the biggest impact on service level objectives (SLOs) and service level agreements (SLAs) in the event of a disruption, noted Yigal.
In the absence of the ability to determine the actual root cause of an issue, alert storms conspire to increase fatigue—and that eventually results in higher levels of burnout across the DevOps team, noted Yigal. In fact, 82% of respondents said their mean-time-to-resolution (MTTR) during production incidents was more than an hour.
The survey also noted that more than half of respondents (52%) worked for organizations that are simultaneously trying to rein in monitoring costs. More than three-quarters (76%) of respondents also noted that open source OpenTelemetry (OTEL) or OTEL-centric tooling was at least somewhat important to their overall observability strategy.
In addition, 87% of respondents said their organization is already using some form of platform engineering to manage DevOps workflows at scale.
It’s not clear how quickly organizations are adopting observability tools and platforms, but too many DevOps teams lack the visibility needed to identify the root cause of an issue. As a result, variations of the same problems often keep manifesting themselves because previous remediation efforts simply didn’t go deep enough to resolve the core issue.
Of course, there may come a day when machine learning algorithms, along with other forms of artificial intelligence (AI), will make it simpler to surface these issues. The challenge, in the meantime, is laying down the observability foundation today to provide access to the data that will be required to train those AI models.
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Survey Sees Cloud Developer Environment Adoption Gaining Momentum
A global survey found that while 95% of developers and business leaders are familiar with cloud development environments (CDEs), the reasons for adopting one vary widely. The survey polled 223 developers and business leaders working for organizations with more than 2,000 workers and 250 developers.
Conducted by Coder, a provider of a CDE, the survey, for example, found that while 15% cited optimizing the developer environment setup process as a reason for adopting a CDE, another 14% cited making management tasks such as onboarding or improving remote access easier.
Coder CEO Rob Whiteley said while CDEs clearly improve the productivity of application development teams and improve security by reducing the amount of source code running on notebook and desktop workstations, organizations looking to adopt CDEs need a significant amount of cultural resistance. Application developers have historically set up their own development environments on their own local machines. That approach, however, means that in addition to making it more challenging to collaborate also requires organizations to spend significant time and effort locking down each endpoint being employed by their developers, noted Whiteley.
In fact, those issues are why many platform engineering teams formed to centralize the management of DevOps workflows are implementing CDEs, he added. The ultimate goal is to establish a set of guardrails while still enabling developers to self-service their tooling requirements, noted Whiteley.
In fact, many DevOps teams have already built a homegrown version of a CDE that they currently maintain. Coder and other providers of CDEs are making a case for using a platform supported by a vendor rather than devoting resources to supporting a custom CDE, said Whiteley.
As DevOps workflows continue to evolve in the age of artificial intelligence (AI), it’s only a matter of time before more organizations embrace CDEs, he added. The sheer volume of data that developers will be working with will make it impractical to continue to use local machines to build applications, said Whiteley.
Regardless of the motivation for adopting CDEs, significant cultural changes are coming to DevOps workflows that need to evolve as application development continues to become more distributed. In the post-COVID-19 era, it’s now not uncommon for application development teams to consist of IT professionals working in different time zones. That creates a pressing need to leverage cloud services to provide the level of collaboration those teams need to achieve.
It’s not clear how many CDEs have been deployed, but as the pace at which applications are being built and deployed continues to accelerate, the days when organizations could allow each developer to maintain their own development environment are coming to an end. Organizations need to determine as quickly as possible whether the code being created will actually run in a production environment. The more time spent trying to reconcile differences between development environments and the production environment where the application runs, the longer it will take to complete any given project.
Unfortunately, time is the one thing that most DevOps teams no longer have the luxury of spending on application development initiatives that, in addition to multiplying, are also becoming much more complex to manage with each passing day.
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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:
Indeed.com
Western Climate Initiative
Sacramento, California
DevOps Engineering Manager
$175,000 to $200,000CareerBuilder.com
Leidos
Herndon, Virginia
Senior DevOps Engineer
$122,200 to $220,900SimplyHired.com
Hallmark
Remote
DevOps Engineering Manager
$120,000 to $160,000Dice.com
LTIMindtree
Atlanta, Georgia
Azure DevOps Lead
$100,000 to $130,000LinkedIn
Nintendo
Redmond, Washington
Associate Engineer – DevOps
$84,800 to $127,200 -
Edge Computing Requires DevOps at Scale
The rise of edge computing is about to drive a long-overdue convergence of DevOps, data engineering, security, networking, operational technology (OT) and machine learning operations (MLOps) best practices, which will ultimately make IT teams more responsive than ever to the needs of the business.
Historically, each of these IT disciplines has tended to work in isolation, with deliberate handoffs being made between the various teams employed to provision and maintain various services. The challenge is that as more applications are deployed at the network edge, the need for IT teams to work hand-in-glove is crucial. Applications not only need to be deployed across a much more distributed computing environment, but they also need to be regularly updated and patched.
At the same time, increasing amounts of data are being processed and analyzed at the point where it is collected and consumed. More stateful applications running at the network edge are passing aggregated analytics data back to applications running in the cloud or on-premises IT environment. That shift requires a fundamentally different approach to how storage is managed across multiple edge computing environments and the various federated backend systems spanning the enterprise.
Organizations will typically need to store files locally on edge computing platforms while also storing copies of those files in the cloud using an S3-compatible storage service that provides access to inexpensive object-based storage to enable everything from data protection to driving artificial intelligence (AI) applications. That multi-protocol capability creates an opportunity to not just build new stateful distributed applications but also modernize existing applications regardless of what protocols are being used to store data.
Most edge computing platforms today are managed by OT teams that report directly to the leaders of specific business units. However, as more of these platforms are connected to networks, the greater the imperative to centrally manage them as part of any effort to lower the total cost of IT. After all, the most expensive element of IT remains the cost of the labor still required to manage it.
Naturally, sharing data in near real-time across a distributed computing environment puts more pressure on network operations (NetOps) teams. Instead of updating applications in batch mode once a day, data continuously streaming from thousands of edge computing platforms needs to be synchronized with multiple applications to surface the latest, most accurate insights.
More challenging still is that much of that data is being used to train AI models that ultimately require the associated inference engine created to be deployed at the edge. To process terabytes of data, the underlying platform required to support those inference engines also will need to be measured in terabytes. Over time, AI models tend to drift or, in the case of generative AI, outright hallucinate, over time. Data scientists will need to work closely with DevOps teams to replace those AI models because, unlike a traditional application, they can’t be updated via a patch. The entire AI model needs to be replaced with one that is not just more reliable but also—and just as importantly—safer.
Finally, the need to secure edge computing platform is nothing short of critical. Cybercriminals tend to view these platforms as gateways to the rest of the enterprise. Each new edge computing platform deployed extends the size of the attack surface that needs to be defended. Unless cybersecurity teams are deeply involved in building and deploying edge computing applications and platforms, it’s only a question of when, rather than if, a breach will occur.
It may be a while before these various IT fiefdoms converge, but at this point, it is all but inevitable. In fact, it will soon be difficult to distinguish between edge computing and the rest of the IT environment as event-driven applications spanning multiple platforms become more the norm than the exception.
DevOps teams, as part of their relentless commitment to automation, should naturally be at the forefront of these efforts. Rather than confining DevOps principles to how applications are built and deployed, the time has clearly come to automate IT workflows on an end-to-end basis. The only way to programmatically manage edge computing at scale is to also apply DevOps principles at scale. Also known as platform engineering, that approach enables organizations to manage distributed IT environments at scale without requiring organizations to hire a small army of IT teams to manage it all. There will always be a need for specialized expertise, but as IT continues to evolve the siloes that slow down the pace of innovation will need to come down.
Not every member of an IT organization needs to know how to expertly manage every task, but they should be familiar with the inherent dependencies that exist between applications, networking, storage and cybersecurity to advance the goals of businesses that have never been more dependent on IT.
Each organization needs to determine the pace of convergence that best suits its needs, but the faster it occurs, the greater the return on the investment in edge computing will become. The challenge and the opportunity, as always, will be managing yet another major IT transition, where the issues that need to be resolved are as much cultural as they are technical.