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Category: Doin’ DevOps

  • Implementing Data Analytics in Multi-Cloud Environments

    Implementing Data Analytics in Multi-Cloud Environments

    Multi-cloud data analytics is more intricate, demanding a nuanced approach to ensure seamless operations and accurate insights. Coordinating analytics across multiple cloud providers introduces various issues, requiring careful consideration of implementation strategy. From cost management to performance variability and data orchestration, we will explore each challenge in detail and provide actionable insights on how to effectively navigate these hurdles, ensuring fool-proof data analytics infrastructure in multi-cloud solutions.

    Interoperability and Standards

    Interoperability challenges arise in multi-cloud data analytics due to varying APIs and standards across cloud providers. The lack of uniformity in data formats and communication protocols hinders seamless collaboration and data exchange. To address this, organizations should prioritize cloud services adhering to open standards, and advocate for industry-wide interoperability initiatives. Additionally, create an abstraction layer or middleware that acts as a bridge between different cloud providers. This abstraction layer helps standardize interactions, facilitating seamless data exchange and reducing dependencies on specific provider APIs. Thus, you enhance the flexibility of your multi-cloud environment, making it easier to integrate and switch between different cloud platforms.

    Vendor Lock-In

    Organizations risk becoming overly dependent on a single vendor, limiting flexibility and making migration complex. Utilizing specialized features or tools from one provider may face difficulties transitioning to another, which restricts the ability to choose more cost-effective or suitable solutions and may result in increased switching costs. To mitigate this, a multi-cloud strategy should be adopted, favoring standardized technologies and open APIs, allowing organizations to distribute workloads across multiple cloud providers and avoid being tied to a specific vendor’s ecosystem, enhancing flexibility and strategic decision-making. Design architectures that leverage abstraction layers and avoid tightly coupling with provider-specific services. This approach ensures flexibility, allowing your organization to choose and switch between cloud providers based on performance, cost and strategic considerations while minimizing the risk of vendor lock-in.

    Cost Management

    Each provider has unique pricing models and billing mechanisms, making it difficult for organizations to accurately estimate and compare costs. The dynamic nature of data analytics workloads, coupled with data transfer expenses between clouds, contributes to the complexity of budgeting. To cope with that, implement a centralized cloud cost management tool to monitor expenses across providers. Also, regularly review and optimize resource utilization to identify underutilized or over-provisioned instances. You may leverage reserved instances or spot instances for cost-effective computing and implement automation for scaling based on demand, ensuring resources are allocated efficiently. Lastly, negotiate pricing with cloud providers and explore discount options to secure favorable terms for long-term commitments.

    Performance Variability

    As providers have unique infrastructure, network configurations and service levels, inconsistent performance levels may occur and impact the reliability and predictability of analytics results. To mitigate these consequences, conduct thorough performance testing across different cloud environments, optimize queries for specific platforms, and consider workload distribution based on provider strengths. Utilizing performance monitoring tools helps track and analyze variations, enabling timely adjustments. Choosing cloud providers based on specific performance requirements and establishing performance service level agreements (SLAs) also mitigates the impact of performance variability in multi-cloud data analytics.

    Resource Orchestration

    Due to differences in orchestration tools, APIs, and resource management mechanisms cloud providers employ, compatibility issues often take place. The heterogeneity makes it complex to ensure seamless deployment, scaling, and management of analytics workloads. Organizations may consider adopting cloud-agnostic orchestration tools and frameworks to abstract the underlying differences among cloud providers, allowing for consistent resource management. Embracing infrastructure as code (IaC) practices and automation also streamlines resource orchestration, enabling organizations to deploy and manage analytics workloads more efficiently across various cloud environments.

    Data Movement and Latency

    Varied network architectures, geographical distances, and differing data transfer mechanisms contribute to increased latency and potential performance issues. Transferring significant datasets across clouds incurs additional costs and can lead to operational inefficiencies. To avoid that, organizations should optimize data movement processes by leveraging efficient transfer protocols, considering edge computing for proximity to data sources, and strategically distributing workloads based on data location. Implementing caching strategies and utilizing content delivery networks (CDNs) can help mitigate latency challenges, ensuring a more responsive and cost-effective multi-cloud data analytics environment.

    As a Footnote

    In conclusion, the journey of implementing data analytics in multi-cloud environments presents its unique set of challenges. Embracing open standards, adopting cloud-agnostic tools and fostering a culture of continuous improvement are essential components of overcoming them. Continuous monitoring, regular updates to governance frameworks, and strategic decision-making aligned with your business specifics and objectives are keys to reaping maximum benefits from multi-cloud data analytics. You may also take advantage of cloud computing consulting to get a tailored strategy from experts.

  • Businesses Must Bridge the Skills Gap to Succeed With Quantum Computing

    Businesses Must Bridge the Skills Gap to Succeed With Quantum Computing

    Quantum computing is poised to transform the way companies solve business problems. The technology needed to power this revolution is becoming a reality, with large developers like IBM making excellent progress toward building quantum-centric supercomputers. But while the technological barrier to deploying quantum is falling away, another obstacle remains: The lack of quantum skills in the labor market.

    Last year, Classiq commissioned a survey of over 500 professionals familiar with quantum technology, which highlighted a range of possible benefits that quantum computing will unlock, including cost savings, new revenue streams and competitive advantages for companies that successfully deploy the technology. But the survey also highlighted that the shortage of qualified engineers and scientists who can program quantum computers remains one of the biggest barriers to increased quantum deployments.

    This skills gap will not only prevent companies from leveraging the power of quantum computing but also lead to expensive competition for talent as businesses try to recruit the few quantum professionals available in the workforce. Why has this skills gap grown, and what can companies do about it?

    Coding for the Quantum Computing Era

    First, we need to understand what skills are missing that are contributing to a skills gap, as some readers may ask why existing programmers and computer engineers cannot meet this demand. Besides lacking the purely technical skills needed to code for quantum computing, such as creating quantum circuits and software, there is also a knowledge gap around how to solve business problems using a quantum computer.

    For instance, while many programmers trained in classical computing could figure out a way to design a program for portfolio optimization, they likely have no idea how to do the same using a quantum computer. They are unlikely to know where to start, how to check their results or even how to run their code on a quantum device.

    Coding for quantum computers is also more complicated than for classical computing. As each “qubit” can be one and zero simultaneously instead of either/or, writing the quantum code is more complex. Developers need to understand quantum concepts such as superpositions, so working in quantum requires new ways of thinking about problems and necessitates new ways of working.

    This last issue is compounded by a technology gap, as quantum programming has not been abstracted away as it has for classical programming – it is mostly still at the assembly language level. At this level, writing a few dozen lines of quantum programming is achievable but time-consuming, so creating complex quantum programs of hundreds or thousands of lines of code is almost impossible.

    Solving the Skills Gap

    The first instinct for companies wishing to leverage quantum technology is to hire their way out of this problem, but there are simply not enough Ph.D.-trained quantum experts in the market, and there is likely to be stiff competition for the few quantum-trained graduates coming out of university. Given the shortage, companies must instead consider how to train and upskill their staff.

    Convincing existing staff to take part in this training should not be an issue. Engineers and programmers are keen to learn – nearly 95% of respondents to our survey said they would like to be trained in quantum. Why is there such high demand? Not only is this new technology exciting, but many workers may hope to future-proof their jobs or increase their earning potential through quantum skills.

    Upskilling your workforce does not have to be expensive. There is already a wide range of open source material, such as the Qiskit development kit, designed to help people explore this new technology. For companies, a straightforward solution is to give their engineers the time and space to learn about quantum for themselves. Simply giving your existing coders permission and resources to learn about this technology and how it can benefit your business could prove to be a profitable long-term investment.

    The added advantage of upskilling your staff, rather than recruiting quantum Ph.D. graduates, is your existing workforce understands the sorts of problems your business needs to solve.

    Plus, new products are emerging that can help solve the technology gap and make it easier to code for the quantum era. Sophisticated low-code software can abstract away some of the complexity involved in writing quantum coding, which in turn will speed up the process of learning.

    But upskilling is only one thing to consider. Companies also need to invest in the resources and infrastructure around their emerging quantum talent. They need to build a team, identify use cases that can benefit from quantum technology and locate the right vendors and cloud providers that will allow them to test and simulate their quantum programs.

    The quantum revolution remains several years away, but companies need to make sure they have the skills and infrastructure ready to take advantage of this technology or risk being left behind by quantum-prepared competitors.

  • 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.

  • 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.

  • 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.

  • 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:

    Indeed.com

    MedStudy
    Remote
    Senior DevOps Engineer
    $125,000 to $165,000

    SimplyHired.com

    Raytheon
    Tewksbury, Massachusetts
    Technical Fellow – DevSecOps/Cloud Native
    $122,000 to $155,000

    CareerBuilder.com

    State Street Corp.
    Quincy, Massachusetts
    Azure DevOps Engineer
    $120,000 to $202,500

    Dice.com

    USEReady
    New York, New York
    Senior Azure DevOps Engineer
    $120,000 to $160,000

    LinkedIn

    Northrop Grumman
    Dulles, Virginia
    Senior Principal DevOps Engineer
    $107,300 to $199,600