Tag: DevOps

  • Choosing the Right Programming Language

    Choosing the Right Programming Language

    In software engineering, selecting a programming language is a pivotal decision for any organization. The appropriate language contributes to a streamlined development workflow and yields a resilient software solution that facilitates swift and efficient debugging, documentation, enhancement and maintenance. On the other hand, selecting an unsuitable language can set a project on a course for failure from the onset. Given the multitude of available options and the magnitude of this decision, how can programmers ensure they make the most judicious language choice?

    A comprehensive and effective decision-making process begins with a thorough understanding of the current landscape, historical context and the evolutionary trajectory of programming languages. It is also imperative to critically appreciate the benchmarks employed by computer scientists and the industry’s best practices in language selection. Programmers who invest time to enhance their expertise in these domains will mitigate the risk of selecting a language deficient in essential features or one poised for obsolescence, thereby safeguarding the resulting product’s longevity, relevance and success.

    The evolution of programming languages

    Charles Babbage is credited with creating the first computer between 1833 and 1871. He spent nearly 40 years perfecting his invention, which ultimately could perform simple calculations. While Babbage is recognized for building the initial computer, in 1883, Ada Lovelace played a pivotal role by devising the programming language for Babbage’s invention. Today, 140 years later, more than 500 distinct programming languages facilitate human-computer interaction. The continuous adaptation and innovation of programming languages have paralleled the dynamic evolution of technology.

    Contemporary programming languages, such as Swift, Kotlin and Go, exhibit more user-friendly syntax and enhanced ease of use than their predecessors, including SQL, MATLAB and C. Recent languages often integrate features that were unavailable just a few years ago. As such, programmers have the ability to select from a diverse array of languages, identifying the most suitable for their project that facilitates the efficient completion of tasks. For example, Swift is used in Mac and iOS platforms, C# is tailored for the .NET Framework and JavaScript, renowned for constructing the internet, is a testament to the varied roles these languages play in diverse technological domains.

    Selecting the right programming language

    With vast options of programming languages available, one of the greatest challenges is determining which is best suited for a particular project. Comparing the various features and syntaxes of languages can be complicated. Based on recent industry trends, Table 1 details the key programming languages in popular areas/domains.

    Area/Domain

    Programming Languages

    Web development

    JavaScript, Java, HTML5, Ruby, TypeScript

    Mobile development

    Kotlin, Swift, Objective-C, ++, C#

    Data science

    Python, R, SQL, Scala, Julia, MATLAB

    Artificial intelligence and machine learning

    Python, R, Java, Lisp, Prolog, JavaScript

    Cloud computing

    Java, Python, JavaScript, Golang, .Net Core

    Cybersecurity

    Perl, C++, SQL, Ruby, Rust, Assembly

    Table 1: Key programming languages by domain

    Scientifically benchmarking programming languages

    One of the most efficient and effective ways to differentiate programming languages is through benchmarking, a process through which computer scientists analyze and compare programming languages. For instance, they may compare the performance of different programming languages directly against each other. They may also assess performance differences between different compilers or runtimes of the same language with the same source code. In addition, they may assess real server environments such as hosted cloud VMs or Docker/Podman and their effects on the performance of various programming languages.

    Numerous renowned computer scientists have begun benchmarking programming languages, making it easier to tell them apart and select the best language for a particular task. The Computer Language Benchmarks Game is a popular free software project that compares how popular programming languages implement simple algorithms. Programmers can view results and better comprehend each language, its speed and optimal use cases. Additionally, several reputable websites, such as Programming Language and compiler-Benchmarks and Kostya-benchmark, provide computer scientists with side-by-side comparisons of different programming languages, taking into account various performance issues.

    Ranking of programming languages by popularity (2023)

    When selecting a programming language, some of the factors for programmers to consider include the targeted platform, production time, language elasticity, community/support, the programmer’s experience with the language, and the language’s current popularity. Popularity is an important factor because even though more than 500 programming languages are available, most of the industry relies on just 10, making those the most likely to remain in use. Table 2 highlights the 10 most popular programming languages based on the PYPL Index, which assesses how frequently language tutorials are searched for on Google and Stack Overflow’s Developer Survey 2023.

    Ranking

    PYPL Ranking

    September 2023

    Stack Overflow’s

    Developer Survey 2023

    #1

    Python

    JavaScript

    #2

    Java

    HTML/CSS

    #3

    JavaScript

    Python

    #4

    C#

    SQL

    #5

    C/C++

    TypeScript

    #6

    PHP

    Bash/Shell

    #7

    R

    Java

    #8

    TypeScript

    C#

    #9

    Swift

    C++

    #10

    Objective-C

    C

    Table 2: 2023 10 most popular programming languages

    When making the final programming language selection, it is essential to remember that no programming language will be perfect for any particular project. As such, programmers need to allow for some flexibility in the selection process. Determine what is most important, such as speed, performance or fewer lines of code, and then sacrifice other areas to ensure a solution that best fits the identified priorities.

    The future of programming languages

    Today’s programming world is centered around artificial intelligence (AI) and machine learning (ML). As such, languages like Python, currently a driving force behind AI, will likely grow in popularity. Moving forward, programmers can expect an increase in the popularity of JavaScript, which dominates web browsers; Java, which is prevalent in the business world; Kotlin, which is widely used in apps and C++, which is used frequently in system-level programming and game development.

    As technology evolves, programming languages—both existing and yet-to-be-developed—will play a pivotal role in shaping the future of a digital landscape. Their role in incorporating new tech features, expanding ML, augmenting AI and furthering the internet-of-things (IoT) will become increasingly critical.

    As new languages appear and evolve, old languages will fade away. To that end, Analytics Insight recently published a list of 10 programming languages, including currently well-used languages like Visual Basic and COBOL, which it expects to dramatically decrease in popularity and use by 2030. For this reason, it is essential for developers and decision-makers to stay up to date on the ever-evolving world of programming languages. As existing languages are retired and new ones enter the marketplace, it is imperative that professionals learn promising new languages and continually upskill. Those who commit to ongoing training and education will simplify the programming language selection process, increase their chances of running a successful project, and solidify their position in an increasingly competitive marketplace. Those who don’t risk going extinct alongside once-popular programming languages.

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.

    Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.

    Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.

    The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.

    We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:

    CareerBuilder.com

    Belcan Federal
    Anaheim, California
    Lead DevOps Engineer
    $140,000 to $170,000

    Dice.com

    Trinity IT Services
    Remote
    DevOps Lead
    $115,000 to $165,000

    SimplyHired.com

    EMMES Corp
    Rockville, Maryland
    DevOps Engineer
    $115,000 to $145,000

    Indeed.com

    VyncaCare
    San Francisco, California
    DevOps Engineer
    $110,000 to $140,000

    LinkedIn

    L3Harris Technologies
    Colorado Springs, Colorado
    Senior DevOps/AWS Specialist
    $91,500 to $170,500

  • From Machine Learning to DevSecOps: Six DevOps Trends for 2024

    From Machine Learning to DevSecOps: Six DevOps Trends for 2024

    The DevOps market is set to grow by 25% annually between 2024 and 2032, fueled by the growing demand for faster software delivery and higher-quality output. Emerging technologies like AI-driven development and serverless architectures have introduced new products in the space, while traditional processes like version control and CI/CD will be further refined. But amongst this innovation, security and quality should remain as organizations’ highest concerns. Looking into the coming year, it’s hard not to see where the DevOps industry is going based on the past year. As such, we have endeavored to provide six distinct DevOps trends we think will be particularly influential in the new year.

    The Integration of AI and Machine Learning

    One of the most notable trends in the future of DevOps is the increasing incorporation of artificial intelligence (AI) and machine learning (ML) into the software development life cycle. The explosion of AI technology in 2023 has set the stage for more predictive analytics in code development, automated testing and intelligent monitoring in the operations phase.

    As Google’s 2023 State of DevOps Report highlighted, DevOps teams feel that AI will contribute most to analyzing data and security, alongside identifying bugs. However, a solid use case has yet to materialize for most teams, and the report shows that early adopters of AI have experienced a minor decrease in their software delivery performance and a significant decrease in their operational performance. So, it’s possible that at this stage teams are implementing AI without considering what problems they’re trying to solve, or without paying full attention to the security and quality implications.

    Emphasis on Increased Productivity

    While AI adoption will be driven by the underlying push for increased productivity in DevOps tools and practices, DevOps teams should focus on drawing maximum value from the existing tools that automate mundane and repetitive tasks, allowing them to focus on more critical aspects of development and operations. This will be made possible by DevOps vendors that provide enhancements to collaborative tools, simplified interfaces and more intuitive workflows, reducing the cognitive load on developers and operations professionals.

    Culturally, the continuing trend towards remote and hybrid work environments is influencing productivity strategies in DevOps, with tools and practices that support remote collaboration and communication becoming vital. Cloud-based DevOps tools are facilitating seamless collaboration, allowing teams to work efficiently irrespective of their physical locations.

    Alignment to Existing Cloud and Microservices Architecture

    We can expect the new alignment of DevOps with cloud and microservices architectures to continue into 2024. This integration is about more than just adopting new technologies — it’s about creating efficiencies that enhance agility and revenue growth.

    The combination of DevOps practices with cloud and microservices enables organizations to leverage the inherent benefits of these platforms. Cloud infrastructure is scalable and flexible, reducing overhead costs associated with physical servers. Microservices allow for rapid development, testing and deployment of independent components without disrupting the whole system. Together with DevOps automation and continuous practices, these architectures accelerate innovation and time-to-market.

    This alignment of DevOps with cloud and microservices will lead to enhanced scalability, resilience, collaboration and customer experiences. By being able to quickly scale, update, and respond to demands, organizations can better satisfy customers and meet market needs, and this, in turn, will drive revenue growth and productivity.

    The Drive for Higher Quality

    While increased productivity and revenue generation will be a key priority in 2024, it’ll be matched by an unwavering commitment to quality. With the rising complexity of software and the increasing dependency of businesses on digital platforms, the margin for error is slimming. Quality assurance practices will become more integrated into the DevOps workflow, ensuring that each release meets the highest standards of performance, security, and user experience.

    Continuous integration/continuous deployment (CI/CD) practices are evolving to include more rigorous and automated testing protocols. These protocols are designed not just to catch bugs but to assess performance under diverse and real-world scenarios. Real-time monitoring tools are being integrated more deeply into DevOps workflows, providing ongoing insights into application performance and user experience. This continuous feedback loop ensures that any issues are identified and addressed promptly, thereby maintaining high standards of quality post-deployment.

    DevSecOps Maturity

    In the same vein, cybersecurity will continue to be a primary concern in 2024, with core DevOps tooling and processes stepping up by incorporating advanced security features. Version control systems will take the lead here by introducing robust access controls, encryption of code repositories and integration with security testing tools to ensure that the code is not only efficient but also secure from vulnerabilities.

    Security integrations in version control will become more advanced, possibly incorporating real-time scanning for security vulnerabilities, tighter integrations with identity management platforms and pattern analysis to predict and prevent potential security breaches in code.

    And as AI is pioneered throughout DevOps tooling, organizations will need to consider the security implications. The Open Worldwide Application Security Project (OWASP) has predicted that data leaks, permission issues and insecure plugins could pose serious security vulnerabilities for software. Teams that can harness growth with AI in 2024 while building sufficient security through DevSecOps practices like backup and monitoring will future-proof themselves.

    Speed and Security

    The speed of DevOps growth, innovation and adoption is only set to increase in 2024. We’ll see teams pioneer new technologies to meet business priorities and revenue targets. In doing so, it’s very easy to jump on the AI or serverless bandwagon and assume that sprinkling it on every technical discipline will make for a bright and shiny future. While some key opportunities in the DevOps space will benefit from the acceleration and tighter integration that new technology brings, organizations looking for lasting growth will need to prioritize quality and security practices.

    The key trend we’ll see in 2024 is teams getting smarter and more focused on how they apply new technology to their DevOps trajectory, delivering faster, more accurate releases that free up time to deliver ever greater value to their customers and business. So, the DevOps winners of 2024 will be the organizations that manage to balance the speed of new technology adoption with robust security.

  • Opsera Leverages AI to Simplify DevOps Platform Migrations

    Opsera Leverages AI to Simplify DevOps Platform Migrations

    Opsera is leveraging a generative artificial intelligence (AI) model it built to enable DevOps teams to migrate from one platform to another.

    Gregg Holtzrichter, chief marketing officer for Opsera, said the Hummingbird AI model makes it simpler to migrate source code from, for example, a Bitbucket on-premises continuous integration/continuous development (CI/CD) platform to GitHub.

    Opsera originally developed Hummingbird as an orchestration engine that integrates DevOps workflows spanning multiple tools and platforms. The primary goal is to make it possible for DevOps teams to unify their workflows, said Holtzrichter.

    At the same time, Opsera is now making it possible to leverage a generative AI model to reverse engineer the source code a DevOps team may have developed to extend the capabilities of a specific platform. That new code then makes it possible to significantly reduce the total cost of switching DevOps platforms, said Holtzrichter. As part of such an effort, Opsera is also now providing support for organizations looking to specifically migrate to GitHub Copilot and GitHub Actions.

    It’s not clear how many DevOps teams might be looking to switch platforms, but with the rise of generative AI capabilities for writing code, interest in platforms such as GitHub has increased, noted Holtzrichter. In addition, support for platforms such as the on-premises edition of Bitbucket is being sunsetted by Atlassian as part of an effort to drive more customers toward the cloud edition of the CI/CD platform.

    Regardless of the underlying platforms involved, generative AI promises to reduce the ability of any vendor to lock customers into a specific platform. As DevOps teams extended DevOps platforms over the past decade, many of them have discovered that the integrations they created and maintained made it all but impossible to switch platforms. With the ability to refactor much of that code based on the insights surfaced by the Opsera platform using generative AI, the time and effort required to switch platforms has been substantially reduced, noted Holtzrichter.

    At its core, Opsera makes it possible to apply analytics to DevOps workflows as they become more complex. The number of dependencies that exist between various application modules, for example, makes it exceedingly difficult for DevOps teams to manually identify the impact a delay in one project might have on any number of other downstream projects.

    The Opsera platform surfaces that intelligence by scanning all repositories and branches, including commit history and generating alerts and notifications whenever KPI thresholds or compliance requirements are exceeded. DevOps teams can also use the platform to assess their overall DevOps maturity using, for example, the DevOps Research and Assessment (DORA) metrics defined by Google. In addition, many organizations are keen to discover what best practices should be adopted by other application development teams.

    Now that AI is being applied to that analytics capability, it’s become possible to extend the reach of the Ospera platform beyond analytics to include source code migration. Each organization will still need to determine whether such an effort is worth the remaining time and effort still required, but at the very least, most DevOps teams can take comfort in the fact they can now keep their options a lot more open.

  • What Will DevOps Look Like in 2024?

    What Will DevOps Look Like in 2024?

    The last two years have been marked by significant downsizing in technology companies. As large layoffs at companies like Amazon, Meta and Google make major headlines, there is a general shift away from the “spending cash and growing fast” mindset that free capital and strong growth during the pandemic launched in the tech industry. The name of the game is to be more conservative with spending, and that mindset will drive a lot of investments in the next year. While these cost pressures may feel challenging to technology organizations, there are a few key opportunities as well. Many trends are starting, sustaining and ending, so let’s focus on the top six likely to drive changes in the industry in 2024 and beyond.

    1. AI—Specifically Generative AI—Move Right on the Hype Cycle

    While AI is not new, OpenAI and ChatGPT have created an amazingly accessible and powerful tool that is positioned to drive a lot of value. The hype cycle is still in full swing as dozens of new uses of the technology hit the market daily, and if you’re not trying to build a generative AI-enabled product, you’re getting asked when you will start. In 2024, we will likely see LLMs move into the trough of disillusionment as inflated expectations and projects don’t all play out. However, those organizations with generative AI products that truly deliver value to users should expect to see massive growth. Technology organizations will need to make hard decisions about AI products. You will either find traction in the next year and see huge growth or quickly find it doesn’t work and shift focus back to non-AI products.

    2. Developer Experience Gains Momentum

    While DX has been talked about for years, we’re seeing new investments in tools that empower developers. This trend is partially born out of a need to reduce tool and system sprawl for engineering teams, to empower teams and allow them to move faster and to improve efficiency. While you may not build or buy a DX platform, you will likely start a DX team or committee that better represents the folks building your software. This will lead to some great internal changes and key efficiencies getting attention that may have been previously overlooked. The wonderful thing about this trend is that it both brings efficiencies to the organization and makes developers’ lives easier. It’s a win-win!

    3. Increased Pressure to Reduce Costs

    Even for organizations that have avoided layoffs, most are seeing pressure to shift into a leaner existence. This often means critically looking at all sources of spending and taking on new projects to optimize them. Maybe a cloud re-platform can reduce hosting spend by 20%, or maybe you start looking to remove or combine SaaS tools to reduce seats. You may even see hiring freezes for critical teams. Organizations will have to embrace other trends to increase the efficiencies of their teams to meet business demands here. Don’t be surprised to hear “do more with less” become a mantra around the office.

    4. Increased Accountability Through Data

    While data platforms for technology organizations continue to grow, teams are finding that it’s not enough to just measure a few metrics. Organizations will be required increasingly to use the data they have to show they are delivering value. Partially due to increased pressures mentioned above, teams will need to become more predictable, and better at identifying and handling risk at every stage of development. It’s no longer enough to just build a great development engine, teams must ensure that the engine can power them to their destination.

    5. ESG in Technology

    Focus on DEI in technology continues to require significant investment, however, organizations are starting to consider a broader umbrella of social responsibility. This can range from considering the environmental impacts or carbon production of your servers to changing your software supply chain to avoid supporting particular organizations. These types of requirements are appearing sooner and more overtly in the software development process, and may significantly impact how teams choose to build or implement software.

    6. DevSecOps Continues to Grow

    There has been a renewed focus on security in recent years after some critical attacks exposed vulnerabilities in the software supply chain. It’s not surprising that security is now written into the fabric of how we build and deliver software. Teams are now expected to consider security at all stages of development from product planning to operation in production, and require additional experts, training and resources to both monitor and ensure compliance. Requirements for SaaS products continue to evolve as the last remnants of on-premises solutions evaporate, and teams should be prepared to move quickly as new threats are discovered and need to be patched.

    As the technology industry prepares for 2024, it’s clear that innovation and adaptation will be the keys to success. From the continued evolution of AI and the growing focus on the developer experience to the imperative to reduce costs, increase accountability through data, embrace ESG practices  and fortify security through DevSecOps, technology organizations face an exciting future.