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Tag: DevOps trends

  • Best of 2022: 6 Top Technology Trends to Watch

    Best of 2022: 6 Top Technology Trends to Watch

    As we close out 2022, we at staging-devopsy.kinsta.cloud wanted to highlight the most popular articles of the year. Following is the latest in our series of the Best of 2022.

    Technology trends simply cannot be ignored in today’s dynamic environment. As the world enters a post-digital era, organizations are exploring disruptive technology trends. Artificial intelligence, machine learning, intelligent process automation, advanced analytics, and robotic process automation (RPA) are among the top DevOps technology trends to watch. According to a survey by Accenture, 94% of IT and business experts reported that emerging technologies have accelerated firms’ innovation pace in the last three years. 

    Winning This Decade in the Context of the Next Normal

    This decade continues to witness further development in technology, as innovation transforms industries such as insurance, banking, and healthcare. It is crucial to look beyond seeking operational efficiency and decreasing cost — particularly as customer expectations, regulatory requirements and business landscapes are rapidly evolving. 

    Take a look at six vital technology trends that have gained momentum in today’s hyper-digital world: 

    Trend 1: Artificial Intelligence (AI), Machine Learning (ML) – Integrating Intelligence 

    Technologies like AI and ML have existed for some time, but only now have they started maturing from their perception of “interesting” to “organizationally effective.” Artificial intelligence and machine learning are digital transformation catalysts, and some of the capabilities that fall under this technology include pattern recognition, natural language processing and sentiment analysis. Artificial intelligence has become increasingly important because it provides companies with a competitive edge, is cost-effective and allows them to future-proof themselves. 

    Trend 2: Predictive Analytics – Harnessing New Opportunities 

    In this digital-savvy environment, it has become crucial for organizations to employ tools like predictive analytics.  An advanced form of data analytics, it’s used to extract information and predict trends, behavior and activities with precision. It is increasingly being introduced across industries as it optimizes productivity, helps understand customers better, identifies revenue opportunities and areas of attrition. Financial institutions have already begun to deploy predictive analytics, helping them customize the product offerings, detect fraud and identify new opportunities for cross-selling.

    Trend 3: Low code – Accelerating Innovation 

    Gartner forecasts that low code application platforms will be responsible for 65% of application development activity by 2024. Organizations are rapidly leveraging low code to cater to the growing needs of the digital-first customers, deliver mission-critical and enterprise-grade applications quickly and respond rapidly to unforeseen scenarios. 

    Trend 4: Intelligent Process Automation (IPA): Unifying Processes

    Another technology trend that has paved its path in the digital world is intelligent process automation (IPA). IPA is a group of technologies that work together to organize and digitize the end-to-end business processes and the customer journey. The key drivers of IPA are the need for continuous improvement, retaining data consistency and bridging existing silos.  The technology offers several benefits to an organization, such as reducing error rates, unifying operations, and accelerating processes, but more importantly, assisting in streamlining employee and customer-facing journeys.  

    Trend 5: Contextual Content Services – ECM: Leveraging Context in Content

    Contextual content services, an evolution of traditional enterprise content management (ECM), play a key role in powering automation initiatives. The right tools in ECM like intelligent taxonomy and cognition can bring context to business processes. Empowering users to make smart decisions with relevant information ensures accurate data extraction, and mitigates risks by managing the overall content lifecycle. 

    Trend 6: Robotic Process Automation (RPA): Improving Productivity 

    Robotic process automation is rapidly becoming an integral cog in the wheel of the end-to-end digital journey of organizations. RPA can be the last mile of process automation — or in between — but is key to streamlining and optimizing customer-facing and back-office tasks. The use of bots helps automate repetitive tasks and liberate knowledge workers to focus on other value-added tasks.  The capabilities of RPA offer several benefits, including faster turnaround times, process optimization, sustaining consistency and increasing employee productivity.  

    Future-proofing the Enterprise

    A close eye on the different technology trends can help enterprises leverage the right tools to optimize their business processes and adapt to the changing market needs. Technologies like low code platforms, advanced analytics, AI, and ECM are already being implemented by some organizations to stay ahead of the competition. Ensure your enterprise is tapping into these technology trends to thrive in the next normal.

  • DevOps Trends to Watch in 2021

    DevOps Trends to Watch in 2021

    Engineers at fast-paced startups often wear both Dev and DevOps hats. Early in my career, as a developer at a sentiment-analysis startup, I was tasked with writing scripts to spin up servers, configure them, install necessary software and deploy the code we wrote. My custom shell scripts, Python scripts and cron jobs did all the heavy lifting so that we could reliably deploy software and scale resources on AWS. Fast forward to today, and that problem of confidently building and securely deploying software has become an even more enormous challenge at scale.

    The Cloud-Native Application Stack

    A simple looking e-commerce app today can easily be a big set of coordinating microservices that are powered by containers, orchestrated using Kubernetes and hosted on multi-cloud/hybrid cloud. This combination enables faster delivery of code, cheaper hosting bills and more efficient use of server resources.

    While the application components certainly became smaller over time, organizations today are tasked with managing tens of thousands of containers in a production environment. In a 2020 Cloud Native Computing Foundation (CNCF) survey, 92% of respondents said they use containers in production, a 300% increase from just 23% in the first survey from March 2016. Such coordination complexity at scale is being managed by software delivery pipelines. Just as Ford revolutionized the automobile industry with its assembly line, these integrated DevOps products are massively impacting verticals like finance and insurance.

    DevOps Trends

    Trend: Developers are now Decision Makers and Influencers

    We are excited to see the increasing impact and influence of developers when it comes to the selection of tools. The top-down sales motion largely doesn’t work in the cloud-native world, and needs to be supplemented by bottom-up community adoption. The best developers pick peer-recommended tools to solve these tech challenges. Developers today leverage large communities, self-organize and pick the best open source tools to kickstart small projects that have potential to impact the growth curve of any organization. For vendors, developer evangelism becomes an important strategy for customer acquisition.

    Trend: Integrated Software Delivery Pipelines

    CIOs typically pick category-leading products for each category of the DevOps toolchain, like CI, CD, SAST and SCA. Historically speaking, integrating these tools to maintain an effective CI/CD pipeline has been painful and requires stitching together tools using custom scripts. We hear the frustration when we speak with maintainers of such scripts. To address this challenge, we think end-to-end integrated DevOps platforms will become the norm. Such platforms will offer a few best-in-class components with built-in automation, and will enable organizations to integrate other DevOps tools in a plug-and-play manner.

    Trend: Shifting Left with Code

    The shift left phenomena, which enables fixing of bugs/security issues early in the software development life cycle when it is cheapest to do so, is accelerating. By implementing a shared security responsibility model and enabling developers to take more ownership related to infrastructure, security and other critical production issues with code, organizations are shipping code faster and cheaper with less bugs.

    Trend: Low-Code DevOps Automation

    Many workflows are pretty standard in the DevOps world. Go look at an alert in production; prioritize it, look at the knowledge base for a fix, create incident, apply fix, resolve ticket and then repeat. This example is an oversimplification, but there are many repeat use cases that can be automatically managed. These workflows can and should be automated, which can make DevOps teams more effective. We think that customizable low-code automation interfaces will evolve to act as secret weapons for effective DevOps engineers.

    Trend: Security at the Speed of Dev

    Software release cycles a decade ago used to be four to six months long — if not longer — but today, the software delivery life cycle and release cadence is accelerating. The 2019 State of DevOps report suggests that the top performers deploy software 1,460 times per year, which is effectively four deployments per day. Security, compliance, vulnerability management, authentication and authorization all have to move at this speed, otherwise delivery of secure code is just not possible. We see the most innovative CISOs adopt a shared ownership model of security that enables application component owners to detect and fix vulnerabilities. In this shared model, a good balance between agility (so that development is not blocked) and security (so that risk can be managed) needs to be struck.

    Are You Building the Next Big Thing?

    Cloud infrastructure is an enormous opportunity that Gartner expects to grow to $412 billion by 2024 and many enterprises are just getting started. If you haven’t been paying attention, Satya Nadella now runs Microsoft and Andy Jassy will soon run Amazon. Both these leaders are pioneers in cloud, having led Azure at Microsoft and AWS at Amazon, respectively. This shows how much the largest companies, as measured by global market cap, believe in the opportunity related to the cloud. We couldn’t agree more.

  • 7 Trends Influencing DevOps and DevSecOps Adoption

    7 Trends Influencing DevOps and DevSecOps Adoption

    Enterprise technology adoption trends can be broadly categorized under three pillars: infrastructure, architecture and technology. Let’s explore what these trends are and how they influence DevOps and DevSecOps adoption in tech corporations worldwide.

    Trends in Architecture

    In computing, architecture is the collection of protocols encompassing the utility, structure and execution of software applications. Architecture defines how an application works and determines the function of each component, such as data storage and computing capability, for example. Architecture trends directly influence how technology is developed, which also makes it influential on the field of DevOps.

    Trend #1: Monolithic to Microservice Architecture

    The shift from massive, inflexible systems that limit compatibility to lightweight, nimble software has increased DevOps and DevSecOps adoption. As architectures such as containers become mainstream, it is easier than ever for teams to code, debug and deploy software quickly, and work collaboratively. Microservice architecture offers the benefit of quickly being able to install, run and maintain systems, and is a catalyst to greater DevOps adoption, while containers spur DevSecOps adoption.

    Trend #2: Cloud-First Trend

    Mobile-first, cloud-first development has revolutionized data transportation, security and collaboration, and made development efficient, safe and transparent. This approach leads many organizations to adopt DevOps/DevSecOps.

    Trends in Infrastructure

    In IT, infrastructure refers to software, hardware, networking resources, systems and tools that allow companies to operate and manage their production processes.

    Infrastructure trends also heavily influence both the hardware and software tools companies use for tracking and manufacturing their products, and therefore directly impact their willingness to adopt DevOps and DevSecOps.

    Trend #3: Kubernetes Plus/Minus DevOps

    A significant infrastructure trend that affects DevOps is the increasing adoption of Kubernetes by global technology companies. Kubernetes is open-source software designed to orchestrate the management, deployment and scaling of containers, and  has seen widespread adoption in the tech industry. Kubernetes empowers organizations to be more productive, with an emphasis on security and collaboration. Containers in Kubernetes seamlessly allow multiple teams to develop a project simultaneously. Resource management and transparency are significantly enhanced, bugs can be fixed more quickly and work cycles can be shortened.

    Kubernetes and container management systems also reduce the need for human interaction and facilitate a fully automated, ‘NoOps’ scenario. Kubernetes uses a pipeline architecture approach that makes it easier for developers to use AI and ML tools to analyze, predict, and automate data and work processes.

    Furthermore, Kubernetes sets an infrastructure backed on logging, continuous detection, consistency, and self-healing, making it extremely easy and considerably faster for computers to make sense of the data, potentially eliminating human interaction. The infrastructure established by the trendy Kubernetes technology is both advancing and restraining DevOps adoption in companies worldwide, depending on how the companies implement Kubernetes into their work cycle.

    Trend #4: The Agile Infrastructure Trend

    Agile infrastructure for software development has become the standard for software development, and is perhaps the most influential trend impacting DevOps adoption. Agile, like DevOps, focuses on collaboration, organization, diversifying skill sets to achieve resilience, rapid iteration and self-improving work processes. Adapting to an Agile methodology means each team has members skilled in various disciplines that work in concert. Each product delivered is followed by a feedback cycle. Agile teams in an enterprise are transparent, collaborative and resilient to internal and external conditions.

    Trends in Technology

    Advancements in technology enable new ways of thinking and new best practices. As tech trends change, the work cycles, processes and mindsets also evolve.

    Trend #5: Release & Deployment Automation

    Manually moving software between testing and production environments is an inefficient, painful task that must be repeated every time updates are added, new versions are released or new features are built. But with the rise of release and deployment automation, software companies can entirely automate this process, saving time while boosting reliability. With the click of a button, test code can be automatically converted to production-ready work and deployed to version controls or hosting services. With this use of automation, DevOps adoption skyrockets.

    Trend #6: Site Reliability Engineers

    Site Reliability Engineers (SREs) are in huge demand as organizations strive to create highly scalable and secure software. SREs build robust, reliable software that leverages automation, create self-healing systems that focus on operations and align closely with business imperatives.

    Trend #7: Shift Toward AI/ML

    The evolution of artificial intelligence and machine learning is changing our entire perspective on the development and use of technology. With increasing automation, ML systems can process large amounts of data, make reliable predictions and provide extremely accurate analyses. AI can make use of this data and these predictions to determine what to do next, and create truly intelligent machines that can function with minimal human interaction.

    While this makes systems faster, more autonomous and reliable—which aligns with what DevOps promotes—it can eventually create a NoOps cycle that doesn’t require human intervention. In the short-term, this trend does influence DevOps adoption, but, in the long term, AI/ML could ultimately eliminate DevOps altogether.

  • DevOps Trends for 2020 and Beyond

    DevOps Trends for 2020 and Beyond

    Since the ideation of DevOps, technologists have made great strides to benefit from it. Despite advancements and the success that teams have seen within pockets, many of its core challenges are still prevalent: scaling across the enterprise due to manual processes, poor visibility between dev and ops teams, and tool sprawl that hurts agility. Luckily, there’s light at the end of the tunnel for organizations hoping to get the most from DevOps.

    In order to succeed in DevOps and foster agility in this next decade, the secret is pretty simple: Get the right work to the right team at the right time. Here are three ways development and operations teams will get closer to this formula over the coming years. 

    Change Automation Based on Policies, Data and Automation Will Take Hold

    Despite large investments to double down on DevOps initiatives, the reality is most organizations have only made progress on development processes, while downstream gates and processes such as change management are still manual and time consuming. Too often are developers having to log in, create change requests and fill out forms manually–a frustrating, drawn out and overall unnecessary process. The reality is, manual processes in DevOps simply cannot continue in the next decade. They create unnecessary friction, disrupt the flow between dev and ops, and get in the way of other essential work.

    However, the tide is starting to turn as organizations look to automate tasks like change management by leveraging dynamic controls through data and policy automation to achieve true CI/CD. As automation becomes more commonplace, developers will spend more time actually developing and will be able to deploy apps faster and with greater efficiency. 

    Organizations Will Rely on AI/ML and Analytics to Drive Efficiency and Greater Visibility

    Despite all the advancements in DevOps over the past 10 years, one hurdle hasn’t been addressed: the limited visibility across all the pipelines from development, and the connection to how those deliverables run in the real world–i.e., knowing what changed, how it’s been tested, understanding the impact and assessing the risk, how something performs in production etc. The lack of visibility presents challenges of its own (distrust among teams, slower approvals, bottlenecks and inefficient processes). 

    Organizations are starting to recognize and address this challenge, realizing the gains from local optimization within departments along the way. As this continues, they will increasingly rely on analytics to drive efficiency and end-to-end visibility across processes. What’s more, analytical tools will draw planning, repository, test, deployment and performance data from across the DevOps lifecycle to provide insights into reducing cycle times, risk and improving project outcomes. AI and ML will play a role in leveraging that information to better direct work, to adjust stage gates and policies based on prior performance and more.

    At its core, analytics enables IT teams to make changes at the pace of DevOps, while unprecedented visibility assisted by ML puts IT at the center of developers’ culture and mindset.

    Tool Sprawl Will Continue to Be a Problem but DevOps Management Platforms Will Emerge

    With continued adoption of cloud and microservices architectures, new tools will continue to emerge–different teams within an organization often use different tools for planning, version control, build automation, testing, deployment, etc. But, a new trend is emerging as the industry moves toward platforms that streamline management across various tools and teams to improve development speed and agility. Platforms also enable IT teams to quickly accommodate the rapid addition of features to software, while automating the approval process–making product development faster and more efficient, while also breaking down silos.

    The Bottom Line

    Despite advancements from the last 10 years, there are still many ways to deliver on the value of DevOps and grow adoption. The vast number of tools used by developers and IT makes it difficult for leaders to get a holistic view of how each team is performing. Lack of visibility paired with manual processes slows the behavioral and cultural change needed to succeed with DevOps.

    But, as we enter the next 10 years of DevOps, the combination of automation, analytics and workflow platforms will arm teams for success.

  • Don’t Let ‘Hindsight 2020’ Be Your DevOps Strategy

    Don’t Let ‘Hindsight 2020’ Be Your DevOps Strategy

    The idea of DevOps originated a little over a decade ago with a desire to ensure efficiency in the overall software development and delivery process. Constant operational hurdles that have been hampering flawless and frequent delivery of applications have led organizations to think beyond and to come up with continuous improvements in their processes and tooling. Thus, DevOps has evolved into a solid practice to aid enterprises with an end-to-end feedback enabled framework that spans across various functions and stakeholders to design, develop and deliver quality software.

    The IT landscape over the last few years has changed significantly. Companies are building secure applications and large volumes of workloads are being transformed to the public cloud. Nowadays, firms are actively applying AI technologies to transform their businesses more than they were just a few years ago. Some companies are still investing heavily on building their own complex AI platforms involving expensive infrastructure, but most companies have realized the value in leveraging AI as a service offered by leading cloud providers.

    Digital initiatives and IoT have further increased the use of cloud-based services and the need for automated deployment and delivery of applications and services. Seamless integration of the CI/CD pipeline for on-premise or deployment to the cloud is possible only with a solid DevOps practice. For organizations that are ambitious and striving to get ahead of the game in order to accelerate the growth of their businesses, DevOps is indispensable.

    To successfully implement some of the current major trends in IT, organizations are required to have a mature DevOps practice in place.  In October 2019, Gartner published its predictions on technology trends for 2020. We’ll pick a few from those to discuss the crucial role that DevOps is expected to play in them, and the critical success factors for companies to be advanced forward.  

    Strategic Trends in IT and the Role of DevOps

    Hyperautomation

    Gartner’s analysis on strategic technology trends for 2020 has hyperautomation as the number one trend. Hyperautomation, which enables organizations to function as autonomous digital enterprises, will be leveraging latest technologies such as 5G networks, intelligent edge devices, advanced data analytics, expanding RPA with AI, etc. Firms aspiring to implement AI and ML on a grand scale will want to take advantage of the diverse range of complex machine learning algorithms implemented on scalable cloud infrastructure and offered as services.

    Instead of recreating everything from scratch, organizations will simply upload their data to the cloud and train algorithms based on their business processes and needs. For hyperautomation initiatives to be successful, organizations need to implement end-to-end fully automated DevOps processes with the relevant tooling to specifically address the varied needs from several perspectives. Speed, agility and ensuring an error-free repeatable process will be basic requirements for hyperautomation.

    On-demand deployment of transient infrastructure on the cloud, driven by infrastructure as code and configuration as code will be a primary requirement for rapid development and testing of artifacts that form components of hyperautomation. Fully automated pipelines for the creation and deployment of containers will become a core requirement not only for hyperautomation but in general for most applications. The involvement of DevOps to manage data virtualization, obfuscation and uploading data to the cloud will become more significant to increase the overall efficiency of the automated CI/CD pipelines for hyperautomation projects. 

    Democratization of IT

    Democratization of technology is about making technology more accessible to common people. Gartner has predicted this to be another major trend for 2020. Smartphones and the internet have already played a major role on this front and have enabled people with the technology, tools and data to help make better decisions. Democratization of technology in the IT world is about enabling the IT community and it gets more and more interesting day by day.

    With container technologies such as Docker Desktop that can run on a user’s laptop, a developer now has access to pre-configured container images of various operating systems and other platforms that can be pulled from container registries within seconds. Cloud services such as IaaS, SaaS and PaaS add to this by providing more independence to the development teams apart from making the entire process agile and faster. Democratization at these levels also leads to setting a different level of expectations on the overall velocity of development and delivery of applications. All these services in isolation, i.e. when not connected together through a strong DevOps process and toolset, will not produce the expected level of agility and speed.

    An end-to-end DevOps process works all the way: from triggering the build of a container image as the code is built, deploying an environment on the cloud, integrating with other components, testing artifacts with automated testing and finally to producing a production-worthy artifact. When IT teams get more democratic, they will run their domain the way they perceive beneficial and produce the best possible outcome with quality and speed to make their teams and the organization successful. DevOps will be the means to achieving that goal.

    Internet of Things

    Edge devices are getting smarter by the day. Mass manufacturing of intelligent controller boards, such as the ESP 8266 and ESP 32, is making it extremely cheap to build edge devices with data storage and processing capabilities locally within the device itself instead of communicating with a cloud based central system for command and control. However, such intelligent devices need to be kept up-to-date on security patches, bug-fixes, firmware updates, etc. A solid DevOps framework that integrates testing of patches/fixes, and deploys tested artifacts to remote edge devices is an absolutely essential component of an organization’s IoT ecosystem.

    Some controller boards run light-weight versions of Linux and have the capability to additionally run a Docker engine. This helps ensure consistency through deployment of updated containers via a DevOps pipeline to the edge devices. Convergence of cloud and digital transformation initiatives, especially in IoT over the last few years, has widened the scope of DevOps. Major cloud providers are already offering several services to integrate edge devices with a full-fledged CI/CD pipeline leveraging DevOps processes and tools. 

    Blockchain

    Application developers and IT managers spend a great deal of time either fixing bugs or recovering systems from breakdowns caused by bugs. If best practices, processes and tools are put to proper use, this wasted effort can be greatly reduced and the entire development and delivery process could be made more efficient. Blockchain demands building a strong foundation in IT and raises the need for higher quality in software development, delivery and IT operations on the whole.

    Organizations have to take a closer look at their current capabilities and process maturity before embarking on a journey involving blockchain. Firms that have faced failures with blockchain can certainly vouch for the fact that the failure wasn’t caused by the concept of blockchain or the algorithms that drive it. Rather they had resulted because the rest of the IT systems weren’t reliable enough to keep up with the strict demands of the blockchain systems. DevOps and automation will certainly help improve the security, quality, reliability and repeatable deployment of applications. When the systems that feed the blockchain application go through such increments of improvement, the net effect is that the organization is getting closer to implementing a solid blockchain solution.

    Success Factors

    Firms are moving fast toward the cloud for IaaS and PaaS services. As DevOps, CloudOps and SecOps are converging, CIOs are becoming conscious about building organizations that encapsulate these functions under one leadership in order to set shared goals and ensure synergies across these functions. Organizations with strict compliance requirements and governance would need to involve their operations teams to lay the rules regarding using cloud services.

    Application development teams and users are still dependent on several operations teams to design and implement secure cloud connectivity and to establish the rules around using cloud services. While a strong governance is absolutely essential, there is a high possibility for firms to lose their agility and get caught once again in a mess if these operational processes are not smoothen and streamlined. For the strategic initiatives on implementing the new trends to succeed, it is vital for DevOps to be able to operate without any hurdles.

    Below are a few critical success factors worth considering.

    Dev and Ops in a Box

    Organizational structure plays a key role in determining the success of DevOps. Keeping applications development, DevOps and infrastructure operations very closely aligned with overlapping goals, KPIs and roadmaps is a key to success. Companies that operate these functions independently without proper alignment fail to establish an organization-wide DevOps culture and rarely reap the full benefits of DevOps.

    Full-Stack Engineers for DevOps

    It is important to develop a DevOps culture across the company. At the same time, it is equally important to bring in experts who can evangelize DevOps and take a completely fresh look at how things are done. Experienced DevOps professionals can pin-point areas for improvement, and additionally help accelerate the adoption rate of DevOps. Settling for high-quality resources that have full-stack experience is absolutely essential if you are in for a long run with DevOps. Global companies should seriously consider placing such experienced resources close to the development and operations teams. While it may be cost-effective to recruit resources offshore, that may not be such a good idea if your active development and infrastructure teams are onshore. 

    Trained Internal Resources

    Training internal resources are extremely important in order to carry out the recommendations from experts and to make DevOps a habit. Trained resources have to be deployed immediately to work on DevOps initiatives alongside the experts. To establish synergy and increase collaboration, it is a good idea to send development and operations resources to some common training courses and sessions.

    Wrapping Things Up

    Research organizations are closely observing the direction in which most organizations are moving toward in order to improve their speed, efficiency, agility and their overall ability to provide services that are secure and reliable. Business strategy and technology feed each other and as a result, some key trends in technology emerge from time to time.

    We looked at some of the trends that are going to be prominent in 2020 and the role that DevOps would have in implementing these technologies. Regardless of any specific technology, it is important to automate the integration and delivery of applications. DevOps processes and tooling will help a great deal in accelerating the pace of implementing new technologies and leveraging them in business applications.

    The organization of IT teams and a focus on recruiting the right talents will determine the overall success of DevOps. Training internal staff would help sustain the culture of DevOps in companies and fuel further growth in this space. DevOps will not be an optional function any more. It will be an essential component of the culture, and firms that adapt themselves sooner to this culture will be the ones most successful in implementing the current and future trends in technology.

    — Sridhar Asvathanarayanan

  • 5 DevOps Trends Worth Knowing in 2019

    5 DevOps Trends Worth Knowing in 2019

    DevOps today is more than the buzzword it was 10 years ago. Back then, the idea of combining development with production in your IT infrastructure was not common; the logistics were nonexistent and most of the time businesses decided it was far too much effort.

    However, if the last nine years has taught us anything, it’s that DevOps is worth every penny businesses invest. It helps companies streamline production, keep customers happy and provide agile services to attract new audiences.

    But as with anything in the IT field, there is a paradigm of change. This article discusses some of the top DevOps trends happening now and how to implement them in your own system.

    Automation Will Be a Bigger Focus

    Automation has always been the way for businesses. Whether it’s speeding up the pipeline with machines, providing better response times for angry customers with chatbots or better inventory management with tracking and logging system, automation is behind it all.

    DevOps is no exception.

    But as with everything, there can be too much automation. The best thing to do is break down processes and find simple, redundant tasks that can save time if automated and apply the changes.

    The most important aspect of automating DevOps is to understand the six pillars in the DevOps cycle, which are:

    • Continuous business planning
    • Collaborative development
    • Continuous testing
    • Continuous release and deployment
    • Continuous monitoring
    • Collaborative customer feedback and optimization

    DevOps Assembly Lines: The Next Big Thing

     

    To track the development of your application, we have pipelines. These visually represent the product from its initial stage right through until final development and release.

    However, continuous integration (CI) is no longer the priority focus when it comes to effective monitoring and implementation. Continuous delivery and automation are a major part that businesses have already started delving into in 2019.

    This is where DevOps assembly lines enter the picture.

    Assembly lines are focused on bridging the gap between manual and automated tasks, ensuring there is no redundancy while providing several tools for teams to implement, to ensure apps are hitting the production lines as reasonably quickly as possible.

    Microservices Architecture Adoption Taking Off

     

    Microservices architecture may as well be that new kid in school trying to fit in. These smaller services compound into agile, implementable tools that DevOps teams can use to scale and test software over the course of weeks—not years.

    As you can imagine, this can help some businesses streamline their development and production lines, setting them way ahead of their original production estimates—which is never a bad thing.

    Implementing microservices into your DevOps team can also help companies focus on the runtime and efficient delivery of new development sources, without the hassle of creating dependency errors when something decides to bug out for no apparent reason.

    Security Needs to Step Up

    Cybercrime is on the rise—we all know that’s old news. But as a business, cybercrime can either make or break your sales and weigh down your DevOps team.

    That’s why more companies are hopping onto enterprise security options. Although there are third-party programmers for almost every situation you need, none of them have the same security levels as the tools from the enterprise version provides. Safeguarding both your company and your clientele.

    Riding the Artificial Intelligence Wave

    Finally, we have artificial intelligence (AI) and machine learning (ML). When you think about it, these two elements are literally the sum of everything we want in the DevOps community—the ability to crunch out hours upon hours of menial labor and saving your teams hours to focus on higher-priority tasks.

    The combination of AI, used to predict algorithmic influencers, and ML’s ability to adapt to new situations will be key for DevOps teams to get to the next level in 2019.

    — Gil Artmoore

  • Top Trends in DevOps for 2019

    Top Trends in DevOps for 2019

    DevOps is poised for healthy growth over the next several years, with analysts large and small weighing in with forecasts. For instance, IDC predicts the worldwide DevOps software market will reach $8 billion by 2022, up from $3.9 billion in 2017. Similarly, Grand View Research estimates the global DevOps market will hit $12.85 billion by 2025, registering a CAGR of 18.6 percent.

    Below are some of the most influential trends driving growth in 2019.

    DevOps Instrumentation in AI and Machine Learning will Increase Productivity Gains

    Web-based forms are the nemesis of process automation. Everyone dreads manual forms, but that’s what organizations use when they want to capture data from customers, employees or partners.

    AI and machine learning technologies are mature enough to reliably process voice, video, text and images. That means data-enriched forms could be filled out simply by making a phone call, taking a video, taking a picture or some other natural activity. The analog inputs from the external world are captured digitally and automatically.

    Of course, the error rates in such conversions would depend on how well the AI models have been trained and how well we use the real-world input to enhance the model. DevOps-level instrumentation needs to be introduced in the pipeline. That way, either the customer or the person in-loop will be able to take these errors as inputs for enhancing the accuracy of the models.

    Key Management is Key for DevOps

    In 2019 and beyond, data usage will be closely monitored as GDPR becomes a reality and similar privacy laws are passed across the globe. This is when data tagging becomes critical. Therefore, tools related to data tagging and master data management will become crucial for tracking the origin of data usage.

    Sensitive data will need to be encrypted in rest and in motion. Along with data encryption comes the important responsibility of managing encryption keys. Not only should the keys be stored securely, they also may need to be rotated. Key management services that offer such functionality will become commonplace in the DevOps world.

    Container Adoption Increases with Data Locality Demands

    Data locality is driven by countries that mandate data must reside within their geographical boundaries. Enterprises using SaaS or PaaS will use country-specific public clouds or even private clouds. As a result, critical data and applications will have to be deployed across geographies. Deployments will become more complex due to geographical expansion as well as agile development methodologies.

    In response, container-based development, release and deployment models will become commonplace within the enterprise. While containers offer many advantages by creating siloed workloads, they also increase the demand for sophisticated monitoring tools and technologies that help consolidate the view of these applications.

    The Compiler is the New King of the Data Center

    The demise of Moore’s Law is not helping the CPU keep up with increasing workloads in the data center. To bridge the performance gap, modern hardware such as GPUs, FPGAs and ASICs will infiltrate the data center.

    Developers will start using higher-level languages such as OpenCL and CUDA, and the compilers will generate code to run on the various hardware, depending on deployment-level input including workload, hardware configuration and data size. DevOps practices will have to start befriending compilers so that code can be generated just in time, very close to the point of deployment.

    Technological advancements, security requirements and legislative changes will impact the professionals within this sector, which is growing at a rapid pace that could see it double within five years. As developers prepare to manage a higher volume of sophisticated data, an increasing amount of DevOps solutions will enter the market to further automate processes, accelerate deployments and address the above trends.

    — Shailesh Kumar Davey

  • DevOptimism: The Future of DevOps is Becoming Clearer

    DevOptimism: The Future of DevOps is Becoming Clearer

    DevOps has quickly matured from a fairy tale to a bestseller at most large enterprises. However, we are still at the beginning of the DevOps story, with many exciting chapters still to be written. This article looks at several emerging technologies shaping DevOps today and a handful of developing trends that will shape the ecosystem in the years to come.

    Today’s IT organizations increasingly are moving away from monolithic applications deployed through grand-slam releases with security slapped on after the fact. We are seeing many enterprises transitioning to more atomic applications released in continuous deployments with application security baked in from the beginning. Fueling today’s emerging DevOps is a bevy of new products focused on architectures including microservices, service mesh and function-as-a-service (FaaS); advancements in pipeline automation; and more adaptable security paradigms such as interactive application security testing (IAST) and runtime application self-protection (RASP). While these solutions herald a great step forward, they do demand that legacy applications be refactored into smaller granularity services, layered onto newer architectures and migrated to processes that allow greater automation.

    OK, that’s where we sit today: Great progress creating great opportunities and great challenges. But let’s head a little further down the road to understand where today’s advancements are driving DevOps tomorrow.

    There are five trends consistently appearing on the whiteboards of venture firms and forward-thinking startups that will shape DevOps in the upcoming years. At a macro-level, DevOps must become wider and smarter. As we dig layer deeper, we see five distinct patterns appear in which emerging technologies will make the release pipeline more insightful and proactive while allowing it to handle a wider spectrum of pipeline artifacts beyond just applications:

    • AI for DevOps: Artificial intelligence (AI) is making inroads into all facets of IT and DevOps is no exception. AI techniques soon will make the DevOps pipeline smarter, able to predict the impact and risk of deployments, spot procedural bottlenecks and identify automation shortcuts. Advancements in robotic process automation (RPA) lend themselves directly to optimizing the various handoff and automation points of the release pipeline. AI-based predictive analytics also will allow for higher fidelity operational capacity planning and pre-deployment fault prediction.
    • DevOps for AI: Today we are seeing a handful of new tools, commonly called MLOps, emerging on the market that allow developers and modelers to deploy new AI models rapidly and repeatably. However, these products have yet to collaborate closely with the Ops side of the enterprise. They are generally a development aid, not an operational aid—or said another way, they are all “ML” and very little “Ops.” This space will mature rapidly, however, allowing release of AI models via the DevOps ecosystem so as to include operational aspects. Conversely, tomorrow’s DevOps pipeline will also allow AI models that have been learning from real-world data to be extracted from production and moved back into the development environment.
    • DBOps: Today, new databases, schemas, indexes, triggers and stored procedures typically are deployed manually by DBAs outside the DevOps pipeline, with their operational impact being provided by best-guess capacity planning. This glaring gap soon will be closed by products that allow databases and their associated artifacts to be released through the deployment pipeline along with the applications with which they are coupled. This will not just encompass traditional relational databases, of course, but also support non-structured databases and object stores. The prime objective being that applications and their supporting data repositories will be developed, tested and deployed via the same automated pipeline as applications without the need for specialized database handholding.
    • DataOps: When new apps are deployed, the associated analytics and visualizations are typically brought online by data scientists after the fact through manual deployments. Looking forward, expect data analytics to be introduced into the DevOps pipeline soon after the requirements for the release are defined. Much like DBOps above, the goal is to eliminate specialized manual promotion. But beyond just synchronous deployment, DataOps will allow data governance, data loss prevention (DLP), data lifecycle management and copy data management features to be integrated during each new deployment.
    • Blockchain for DevOps: Today’s and tomorrow’s DevOps evolutions will increase the velocity, variety and volume of pipeline traffic dramatically. While this sounds promising, it undoubtedly will be a nightmare to audit, verify compliance and ultimately control and manage. One technology being eyed to untangle this spaghetti is a distributed consensus mechanism such as blockchain. Stop rolling your eyes—we’re not talking about Bitcoin or ICOs. We are describing the need for a centralized, permissioned, secure, distributed ledger to record, track, authenticate and audit the torrent of pipeline transactions. Blockchain seems to fit the bill nicely and also has the added benefit of enabling digital contracts at each pipeline stage that can be verified at runtime to trigger artifact promotion and also audited long after the fact to ensure that all rules were followed.

    DevOps has already become the standard deployment paradigm in many enterprise IT shops and its adoption will continue to become more pervasive. But this growth requires DevOps products and processes to become smarter, more secure and more controllable as the release pipeline incorporates a wider set of deployment artifacts beyond just applications. This is a daunting challenge to be sure, but there are exciting new products that will emerge to help write the next chapters in the DevOps story.

    — Mark Campbell