The Future of DevOps
The Future of DevOps
The future holds more focus on security and DevSecOps, using AI/ML to improve processes, and more automation.
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To understand the current and future state of DevOps, we spoke to 40 IT executives from 37 organizations. We asked them, "What’s the future of DevOps from your perspective, where do the greatest opportunities lie?" Here’s what they said:
- Security is huge. The more we automate the more we can automate problems. How to integrate security into DevSecOps. More connected = more exposure. What happens when next wave of tools comes out. What is going to be the driving tools after containers?
- DevSecOps - security and privacy. Don’t need to know the details. Know the kind of information that is private. Common security things and privacy things you need to worry about. As a group working with educators here’s what you need to teach on security and privacy in addition technical. Human aspect cannot be ignored. More teaching on the socio part in addition to the technical side. See more collaboration. There will be a huge backlash with more disclosures coming out.
- More people are taking the leap from DevOps to data ops. As streaming architectures become more popular people will want and need more data ops and data logistics in their organizations because of ML adoption. Maturation of DevOps with the data becomes more pervasive. Simplifying the toolsets to pick up GPUs and run ML is becoming more pervasive and less scary. Use cases deliver high value. This is creating the launching pad for practical applications of big data in business.
- I see a lot of opportunities related to artificial intelligence (AI) and machine learning (ML) as it relates to DevOps. Anticipating how changes I make to the application affect the overall user experience. What do I need to test? What is the impact of a change? There is a lot of opportunities to help companies decide what to do next with their product as well as verify the impact of those changes, so they can confidently release high-quality software targeted at their end users’ needs. For example, with AI and ML, I can analyze hundreds of thousands of test results on my platform and determine those changes to a particular portion of the code base result in, on average, a 20% failure rate. The actions I can then take care to ensure that, whenever changes occur in this part of the code, we test much more thoroughly. An even better approach would be to refactor this portion of the code base. AI and ML can allow organizations to make better decisions faster by providing unique analysis correlating, code, test results, user behavior, and production quality and performance. This type of capability can have a dramatic effect on increasing the velocity and quality of DevOps pipelines.
- For DevOps to work, developers must be willing to expand their expertise and responsibilities. DevOps relies on a “many hands make light work” ethos which requires everyone to handle a piece of the development, operations, and security pie. This shouldn’t be viewed as a burden. DevOps is an opportunity for developers. DevOps enables companies to clearly define roles and responsibilities for developers which gives them autonomy and responsibility for the development of their project.
- Today there are a lot of independent Lego blocks. K8s independent of cloud, CI is independent of CD, security, all are important and all are tiny dots. Will see a more holistic, integrated POV, from doing it yourself to integrated and validated as part of the flow. Simplification with alliances, acquisitions to make it easier.
- The biggest opportunity for DevOps is to drive into tech stacks and organizations that think that a move to DevOps requires a complete re-architecture of their application or adoption of replacement technologies. While those sorts of changes may be excellent opportunities to introduce culture change as well, teams running existing business-critical apps in “monolithic” architectures can take advantage of DevOps as well, if they choose the right tools.
- I think containers continue to provide interesting opportunities as the tooling around them improves. Depending on how they’re used, they can confer a lot of benefits or a lot of downsides within a system. There are a lot of untapped or just-scratched ideas on how to use them to convey significant benefits, though. For a few examples of what I mean: using containers to sandbox applications for security and resource reasons. It seems basic on its face, but we mostly see containers used for stateless (and some stateful) server-side applications that are otherwise operated in traditional manners within those containers. The container is treated more like a packaging system than a runtime. What if you generated an application container per user or per user session? It provides a limitless array of opportunities for improving system security, user security, and adding additional analytics around user behavior or preventing resource contention between users of your application. As the tooling improves, containers become even lower-cost to deploy. I think there’s many yet to be tapped opportunities in how containers could be used by both technically-focused and traditional industry verticals to improve their business.
- Platform-as-a-service is definitely a growing field. In the long-run, an application developer should be able to simply define a couple of entry points in their application package and that should be sufficient for the application to be deployed. I think this is very much aligned with what we have been doing in developing an abstraction between Applications and Technology Infrastructure. Another example is our Data Science Platform as a Service, where the DevOps of the platform itself is fully encapsulated away from the application developers who use this platform to develop machine learning powered applications.
- Kubernetes (K8s) and Docker seem worthwhile to learn about for any DevOps team. Another one is scaling of machine learning. Not everyone is doing this and it’s new so there’s a lot to learn there. We’ve been diving into what it’s like to scale TensorFlow serving and I think that this is going to be a big part of DevOps in the future.
- Containerization opens up a lot of possibilities in terms of revamped architecture. The surrounding architecture is isolated from everything else. This provides deployment mobility and flexibility in terms of what libraries are in use and what resources they need which is exciting. Another one is dealing with machine learning and IoT data. There’s a massive influx of data from devices and so we have to think about how we store it in a usable format and also the global distribution of it. People need to be able to get data from a variety of locations which brings politics into the mix. There are rules in development from countries all over the world that make determinations about how data is stored. It’s becoming kind of tricky to have international customers because data has to be stored differently in various physical locations. Finally, the move towards the SRE model. Operators will no longer be a separate team but work on each project with developers as they go. Part of this will be because of containers because you’ll be working together to build at the development phase. Containers couple the development process with the deployment process.
- In today’s DevOps environments, many technology professionals have mastered operating containerized workloads at scale, and leveraging containers in production. DevOps teams that have mastered containers, and have begun deploying microservices, are swiftly moving into the future of DevOps. The future of DevOps will include increased deployment of microservices and implementation of a service mesh architecture to manage and monitor said microservices. Moving forward, tech pros will increasingly need to develop a good understanding of the breadth/depth of service mesh necessary for their environment and choose the right tool to fit their needs. The future of DevOps also lies in focusing on serverless computing and determining the use cases where serverless computing is appropriate for certain distributed environment and use cases. With this acquired skill, DevOps teams will be able to lead their organizations to strategic decision-making and ultimately into the age of digital transformation.
- The future of DevOps is bright. I believe there are great opportunities to deliver purpose-built pipelines that would connect to common repositories and perform a set of necessary steps to reliably deliver working applications to known environments. All cloud-based of course.
- Containers will continue to grow prevalence and influence within the modern technology landscape. DevOps has always been about automation and this technology allows for companies to more easily build and deploy infrastructure. More companies will adopt containers in an effort to improve automation capabilities and velocity. Organizations will also continue to seek out technologies that integrate into frameworks like K8s as work to further improve automation and velocity.
Here's who shared their insights with us:
- Tim Curless, Senior Technical Architect, AHEAD
- Will Hurley, Vice President of Software Lifecycle Services, Astadia
- Lei Zhang, Head of Developer Experience (DevX), Bloomberg
- Ashok Reddy, Group General Manager, CA Technology
- Sacha Labourey, CEO, CloudBees
- Logan Daigle, Director DevOps Strategy and Delivery, CollabNet
- Sanjay Challa, Senior Product Marketing Manager, Datical
- Colin Britton, CSO, Devo
- OJ Ngo, CTO, DH2i
- Andreas Grabner, DevOps Activist, Dynatrace
- Anders Wallgren, CTO, Electric Cloud
- Armon Dadgar, founder and co-CTO, HashiCorp
- Tamar Eilam, IBM Fellow, Next Generation Cloud and DevOps, IBM Research
- Mathivanan Venkatachalam, Vice President, ManageEngine
- Jim Scott, V.P., Enterprise Architecture, MapR
- Mark Levy, Director of Strategy, Micro Focus
- Glenn Grant, President - U.S. East, Mission
- Jonathan Lewis, VP of Product Marketing, NS1
- Zeev Avidan, Chief Product Officer, OpenLegacy
- Tyler Duzan, Product Manager, Percona
- Bradbury Hart, Vice President and Chief Evangelist, Perfecto
- Damien Tournoud, Founder and CTO, Platform.sh
- Bob Davis, Chief Marketing Officer and Jeff Keyes, Director of Product Marketing, Plutora
- Brad Micklea, Senior Director and Lead, Developer Business Unit, and Burr Sutter, Director, Developer Experience, Red Hat
- Dave Nielsen, Head of Ecosystem Programs, Redis Labs
- Brad Adelberg, Vice President of Engineering, Sauce Labs
- Adam Casella, Co-founder and Glenn Sullivan, Co-founder, SnapRoute
- Dave Blakey, CEO, Snapt
- Keith Kuchler, Vice President of Engineering, SolarWinds
- Justin Rodenbostel, Vice President of Open Source Applications, SPR
- Jennifer Kotzen, Senior Product Marketing Manager, SUSE
- Oded Moshe, VP of Products, SysAid
- Loris Degioanni, CTO and Founder, Sysdig
- Jeffrey Froman, Director of DevOps and Aaron Jennings, Engineer, Temboo
- Pan Chhum, Infrastructure Engineer, Threat Stack
- John Morello, CTO, Twistlock
- Madhup Mishra, Vice President of Product Marketing, VoltDB
- Joseph Feiman, Chief Strategy Officer, WhiteHat Security
- Andreas Prins, Vice President of Product Development, XebiaLabs
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