PLEASANTON, Calif., June 7, 2021 /PRNewswire/ — SpringML, Inc. (“SpringML”), a leader in machine learning and advanced data analytics services, is proud to announce that it has achieved the Security Specialization in Google Cloud Partner Advantage. By earning the Partner Specialization, SpringML has proven their expertise and success in building customer solutions in the Security field using Google Cloud technology.

Partners with this specialization have demonstrated significant success securing customer infrastructure and workflows to Google Cloud. Areas of expertise for SpringML include data encryption management, identifying and masking confidential data, role based access control and network security access to data for internal and external users – all critical components to help lay a secure foundation for data driven transformation.

“We’re pleased that SpringML has achieved the Security Specialization in the Google Cloud Partner Advantage Program,” said Nina Harding, Chief of Global Partner Programs & Strategy at Google Cloud. “Acquiring this specialization is evidence of SpringML’s expertise in building security infrastructure solutions and their continued commitment to helping our customers throughout their digital transformations.”

“SpringML is an agent of change to help enterprises rapidly deploy data-driven digital transformation projects,” said Girish Reddy, CTO of SpringML. “Using Google Cloud tools, we can ensure that security and privacy compliance are maintained at the highest levels throughout a project implementation.”

As enterprises look for new ways to engage with customers, providing creative digital experiences is key. SpringML’s portfolio showcases its ability to deliver data-driven digital transformation projects having built applications for enterprises with an agile, iterative, and incremental delivery model.

Spring ML, Inc.

SpringML delivers data-driven digital transformation outcomes with an experimentation and design thinking mindset. We provide consulting and implementation services and industry-specific analytics solutions that deliver high-impact business value from data. SpringML is a premier Google Cloud partner with capabilities to plan, assess, deploy, and manage data-driven engagements. We have been awarded Google Cloud specializations based on our expertise and customer portfolio for Security, Data Management, Application Development, Data Analytics, Machine Learning, and Marketing Analytics.

Remote Work as a Security Wake-Up Call

The COVID-19 pandemic pushed development teams into remote work almost overnight. For many organizations, this sudden shift exposed structural weaknesses that had been quietly accumulating for years: poor visibility into what development teams were actually doing, over-reliance on centralized security controls, and a persistent gap between security teams and developers.

Guy Podjarny, founder and president of Snyk — a security company focused on helping developers find vulnerabilities in open source code and Kubernetes-based applications — put it bluntly: “Security has historically been very much about control, and that was already being challenged around visibility into what development teams are doing. So at a high level, you need to shake off this illusion that you can centrally control what development is doing. That wasn’t true before. You’ve been kind of walking on this thin ice. And then the virus shattered it.”

The Case for Bug Bounty Programs During Downtime

One of Podjarny’s more actionable suggestions during the pandemic period: launch a bug bounty program. With many developers working from home and looking for productive outlets, sign-ups to bug bounty communities increased noticeably. Podjarny noted that this kind of distributed, community-driven security testing aligns naturally with remote-first workflows.

The idea received broader industry support as well. Bugcrowd announced it would provide free, fully managed bug bounty programs — including attack surface analysis — to emergency response teams, hospitals, and other healthcare organizations responding to the pandemic. This illustrated a broader industry recognition that security capacity could be scaled through community programs at relatively low cost.

Snyk’s Approach: Continuous Security Integration

Snyk’s core product is built around the idea that security should happen during development, not after deployment. The platform helps developers identify vulnerable dependencies as they write code, leveraging a continuously updated database that monitors open source libraries and container images for newly disclosed vulnerabilities.

When vulnerabilities are found post-deployment, Snyk can also apply patches or suggest dependency upgrades. This dual capability — shift-left detection and post-deployment remediation — positions the tool as relevant across the full software development lifecycle (SDLC).

In response to the pandemic, Snyk offered its security services free for six months to organizations in industries hit hardest: healthcare, hospitality, travel, and entertainment.

How to Secure Code When the Dev Team Is Fully Remote

Podjarny outlined three core components for empowering remote developers to write secure code:

1. Document Best Practices and Decision Frameworks

It is not enough to publish a list of secure coding rules. Documentation should walk developers through the decision-making process: what questions to ask themselves as they make architectural and implementation choices, and what steps to take when they encounter an ambiguous situation. The goal is to make good security decisions the path of least resistance.

2. Use Supportive Tools, Not Enforcement Tools

Podjarny makes an important distinction between tools that block developers and tools that inform them. Rather than breaking the entire build when a security violation is detected, he advocates for failing pull requests instead — a targeted intervention that flags only the new changes in question. This approach preserves developer autonomy and keeps the feedback loop tight and actionable.

“Don’t break the build. But rather, surface it to developers. They need to play a role. It’s not just the security team that needs to change.”

— Guy Podjarny, Founder and President, Snyk

This philosophy reflects a broader DevSecOps principle: security tooling should feel like a helpful colleague, not a gatekeeper.

3. Invest in Security Visibility

Even well-intentioned teams make mistakes, and not every developer will have the same level of security awareness. Visibility tooling — instrumenting builds to capture dependency manifests, surfacing discovered vulnerabilities in the build output, and creating team-level dashboards or leaderboards that track how security issues are handled — creates accountability and encourages healthy competition around security hygiene.

Celebrating security achievements publicly within an organization reinforces the message that security is a shared engineering responsibility, not just a compliance checkbox.

Why This Accelerates DevSecOps Adoption

CI/CD pipelines had already been accelerating the pace of software delivery well before the pandemic. But as deployment velocity increased, security teams were frequently left out of the loop — reduced to naysayers who slowed releases down, rather than enablers who helped teams ship safely.

Podjarny argues that the pandemic forced a reckoning with this dysfunction. Organizations could no longer rely on proximity, informal hallway conversations, or the assumption that a centralized security team could review everything. The only scalable answer is DevSecOps: embedding security controls, tooling, and culture directly into the DevOps workflow.

“DevSecOps is about equipping teams that practice DevOps with the ability and empowerment to make security decisions,” Podjarny said. “It is fundamentally the right approach.”

Key Takeaways for DevOps Teams

• Centralized security control does not scale in a distributed, remote-first development environment. Accept this and plan accordingly.
• Shift security left by integrating vulnerability scanning into the IDE, version control, and CI pipeline — not just at the point of deployment.
• Fail pull requests, not builds, to keep developer feedback loops tight and maintain autonomy while enforcing security standards.
• Make security visible through dashboards, dependency audits, and team-level metrics. What gets measured gets managed.
• Consider a bug bounty program as a cost-effective way to augment internal security capacity, particularly when developer availability is high.
• Security documentation should be decision-oriented, not just rule-oriented. Help developers reason through security tradeoffs, not just memorize restrictions.

The pandemic did not create the problems that DevSecOps solves. It simply made them impossible to ignore.

Retail consumers today demand personalized services and delivery, from online, to mobile, to in-store – a joined-up, omnichannel experience delivered in a cycle of swipes, clicks, chat and in-stores contact. These behaviors reflect just how much shoppers are today ‘in charge’ of their purchases and demand interactions that are responsive to their personal preferences. For many retailers, it’s a challenge to keep pace with these demands.

In its 2018 B2C Innovation Survey, IDC asserted that retailers have just “two to three years of competitive advantage, or be a laggard.” This illustrates how short is the window of opportunity – action is needed now. There is an increasing polarization between good and poor retail performance based on a host of different metrics. Retailers can end up as zero or hero very quickly.

For traditional retailers with a plethora of legacy systems, a large asset portfolio and significant human capital, it can be tough to think holistically about digital. The e-commerce channel is often siloed, consigned to a completely different division with different operations. CIOs must work out how to unify digital and traditional retail to truly deliver omnichannel in a profitable way. This is what is keeping most retail CEOs up at night and the CIO will play a critical role in supporting the success of the business, working hand in hand with the CEO, and supported by key staff and employees.

Based on our work with customers in the retail industry, we believe there are three ingredients for success in the quest to adapt to an omnichannel world:

Organization – in particular adoption of DevOps
Technology – specifically a suitable Rapid Application Development (RAD) platform with support for cloud and API-based integration
Process – adopting agile and lean methodologies to support experimentation, curiosity, collaboration and courageous learning.
Product-centric DevOps
We are finding many companies, large and small, embracing the product-centric approach. Instead of planning projects to make changes to their applications, they are constantly evolving their software in the same way as an Independent Software Vendor (ISV), regardless of whether the “customer” is the enterprise’s paying customer or an internal user of the application. Product roadmaps chart the course of new capabilities to be added and their priority, replacing project management skills with the sprints and scrums of continuous agile development. This is the approach taken at Johnson & Johnson, as Aravinda Boyapati, Senior Manager and UX and Advanced Tech Incubation Leader, explains:

You don’t have a year-long time to deploy a project. Our approach is to do it in an agile way. You come up with those minimum viable products which you can put into production in less than ten weeks … You can actually improve it on top, building more capabilities and then help your business to evolve them in a faster pace.

In this product-centric world, CIOs are challenged every day to deliver new solutions, products and services either to enable operational improvements or customer experience improvements. Hence, having a single, adaptable and fast-moving development platform is an essential capability.

Choosing a RAD platform
Using a Rapid Application Development (RAD) platform supports the speed, agility and rapid delivery of a DevOps approach. It helps CIOs to simplify their development landscape, reduces the need for long development cycles, and underpins the DevOps organization with a tool to support continuous development, rapid testing and industrialization. The right platform for innovation is essential, says Geir Nøstdahl, Head of IT Digital Services at consumer retail group Elkjop Nordic AS:

By having microservices and utilizing the cloud technologies … we were able to do a lot of things and to help the customer have a more seamless experience than what we would have been able to do if we didn’t have this capability internally.

Choose a RAD platform that can align with your cloud strategy and harness API integration. It should bring together low-cost software development and emerging technologies utilizing microservices, continuous integration and continuous deployment tools.

Agile processes
This should be combined with agile development methodologies and cross-LOB teams who input throughout the development process.

Many organizations are claiming to adopt agile working, but this is often still consigned to project thinking rather than being product-centric. Often, new products or services are launched without deep consultation with users, without a continuous development cycle of product improvement over time, and without a deep understanding of how the products or services are actually being used. It’s important to change the culture, says Nøstdahl:

We needed to embrace a culture of change, a culture of ‘fail fast’ – because you need to dare to change. You need to challenge yourself into learning a lot of new technologies. It’s scary, but we work in technology. No customers of ours would ever shop with pen and paper. We always need to be on alert of the new changing customer expectations, so we have a pressure of constantly innovating.

Following Jeff Bezos’ two pizza rule, ‘If a team couldn’t be fed with two pizza’s it was too big,’ we recommend forming small teams drawn from across line-of-business (LOB) functions to deliver an agile process within the DevOps environment. These small teams should include representation from both users and IT. As described above, it is essential for the IT participants to be armed with not only the right process methodologies but the right tools which allow them to keep pace with the thinking and development of the business.

Kubernetes, the open-source container orchestration platform originally developed at Google and now governed by the Cloud Native Computing Foundation (CNCF), addresses those problems directly. It automates deployment, scaling, self-healing, and rolling updates for containerized workloads. Since its public release, it has become the de facto standard for container orchestration across the industry.

Below are four well-known companies that have publicly discussed their Kubernetes journeys — what drove adoption, what they deployed, and what they gained. Details are drawn from publicly available case study material; specific figures are reproduced as reported and should be independently verified before citation.

Pinterest

Pinterest operates a visual discovery engine serving more than 250 million monthly active users, generating upward of 10 billion recommendations. The platform manages a large server fleet and more than 300 petabytes of data, and it has run on cloud infrastructure since its earliest days.

As Pinterest scaled, the overhead of managing individual virtual machines became a significant drag on engineering productivity. After evaluating several orchestration platforms, the team selected Kubernetes. Key outcomes reported by Pinterest include:

• Faster time-to-market: Deployment cycles that previously took much longer were reduced to minutes.
• Machine learning at scale: Kubernetes became the foundation for building, deploying, and iterating the ML models that power Pinterest’s recommendation engine.
• Reduced operational toil: Kubernetes automated a range of previously manual tasks, freeing engineers to focus on product development rather than infrastructure management.

Spotify

Spotify is one of the world’s most widely used music streaming platforms, with more than 200 million monthly active users. The company adopted a microservices architecture early in its history and built its own internal container orchestration system called Helios to manage containerized services on VMs.

Rather than replacing Helios outright, Spotify treated Kubernetes as a complementary platform — adopting it incrementally to cover capabilities that Helios did not provide. The migration began with a small subset of workloads, allowing engineering teams to validate behavior before expanding coverage. Reported outcomes include:

• Dramatically faster service provisioning: Services that previously took an hour to spin up in production were reduced to minutes or seconds.
• High-throughput workloads: The largest service running on Kubernetes was reported to handle approximately 10 million requests per second.
• Improved CPU utilization: Kubernetes bin-packing led to measurable improvements in resource efficiency.
• Parallel operation: Kubernetes and Helios continue to run side by side, demonstrating that Kubernetes can coexist with existing orchestration tooling rather than requiring a hard cutover.

Bose

Bose, best known for audio hardware, has an extensive portfolio of connected (IoT) devices. As connected device adoption grew, Bose needed to build a scalable, vendor-neutral backend platform capable of supporting millions of devices in production.

After evaluating alternatives, Bose chose Kubernetes running on AWS as the foundation for its IoT platform-as-a-service. Prometheus was deployed alongside Kubernetes for monitoring. Publicly reported details of the platform include:

• Supports approximately 3 million connected devices
• One live production cluster contains over 1,800 namespaces and 340 worker nodes
• Approximately 100 engineers work on the platform
• The platform enables around 30,000 non-production deployments per year
• A new service can move from concept to production in roughly 2.5 weeks

The Bose case is a useful reference for teams exploring Kubernetes for IoT or device-management workloads, where high connection counts and namespace isolation are important operational concerns.

Adidas

Adidas, a global sports apparel and footwear brand, faced a common enterprise problem: VM provisioning that could take days or even weeks. That lead time directly blocked developers and slowed product delivery across the engineering organization.

Kubernetes addressed both the provisioning problem and what the Adidas team refers to as Day 2 operations — the ongoing maintenance tasks (health checks, restarts, rollbacks, resource tuning) that consume engineering time after a service is initially deployed. With Kubernetes handling Day 2 work, developers could redirect effort toward building new features. Reported outcomes:

• Deployment frequency: Releases that previously took four to five days can now happen four to five times per day.
• Scale: Adidas runs more than 4,000 pods on Kubernetes in production.

Common Themes Across These Adoptions

Looking across Pinterest, Spotify, Bose, and Adidas, several patterns emerge that are relevant to any team evaluating Kubernetes:

• Incremental adoption works. Spotify’s parallel Helios/Kubernetes approach shows that a full-replacement migration is not required. Kubernetes can be introduced alongside existing systems.
• Day 2 operations are often the real motivation. Initial container deployments are not particularly difficult; it is ongoing management — health monitoring, scaling, rollback, scheduling — where Kubernetes delivers the most sustained value.
• Machine learning and IoT are first-class use cases. Pinterest and Bose illustrate that Kubernetes handles workload types well beyond standard web services.
• Vendor neutrality matters at scale. Both Bose and Adidas cited avoiding vendor lock-in as an explicit adoption criterion.
• Community and ecosystem reduce risk. All four companies benefit from the CNCF ecosystem — tools like Prometheus, Helm, and managed Kubernetes offerings from major cloud providers reduce the cost of adoption and ongoing operations.

Is Kubernetes Right for Your Organization?

Kubernetes is not universally the right choice for every team or every workload. It introduces real operational complexity, particularly for smaller engineering teams without dedicated platform infrastructure capability. But for organizations already running containerized microservices at meaningful scale, the case studies above show a consistent pattern: Kubernetes reduces provisioning time, improves resource utilization, decreases manual operational burden, and enables higher deployment frequency.

For teams evaluating adoption, reviewing the CNCF case study library is a useful starting point — it collects first-party accounts from a broad range of organizations across industries and scale points.

Self-service platforms and automation are now central to accelerating application delivery. But the real driver behind modern DevOps adoption is something less technical and more cultural: the democratisation of capabilities that were once locked away in specialised silos.

What Does Democratising DevOps Actually Mean?

Democratisation of technology means giving everyday practitioners access to tools, workflows, and capabilities that were previously the exclusive domain of specialists. In tennis, the shift from heavy wooden rackets to lightweight graphite composites meant amateur players could benefit from the same equipment as professionals. The same principle applies to DevOps: infrastructure provisioning, deployment pipelines, test data management, and security scanning no longer need to be gatekept by a single team.

From agile to DevOps, the broader software delivery movement has always been about making application development more accessible—reducing friction, breaking down silos, and empowering each practitioner to contribute more meaningfully to the full delivery lifecycle.

Goodbye, Request Tickets

If you worked on a large software project before CI/CD pipelines became standard, you will recognise this workflow: write code, open a ticket, wait. A separate team provisioned the test environment. Another team integrated the code and built the deployment artefact. By the time you received feedback on a bug, six or more working days had elapsed—assuming nobody was on leave and nothing went wrong in the queue.

Modern CI/CD pipelines collapse that feedback loop dramatically. A developer today can write code, commit it, trigger an automated integration build, provision a test environment on demand, inject test data, execute a test suite, and receive results—all within hours or even minutes. The specialist roles have not disappeared; they have been elevated. Database administrators, security engineers, and infrastructure teams now encode their expertise into reusable, self-service capabilities consumed by the rest of the delivery team.

This is the essence of true DevOps adoption: fostering a culture of collaboration and trust where specialists multiply their impact rather than becoming bottlenecks.

The Four Core Tenets of Democratised DevOps

For democratisation to work at enterprise scale, four foundational elements must be in place simultaneously. Missing any one of them will cause the model to break down.

1. Self-Service Driven Empowerment

The "shift left" principle places capabilities earlier in the delivery lifecycle so that practitioners can access them when they need them, not when a ticket is eventually resolved. Self-service is only meaningful if the service is accessible in the practitioner’s existing workflow—ideally via an API or command-line interface (CLI) that integrates cleanly with their toolchain of choice. A capability that requires switching context, raising a request, or waiting for manual approval is not truly self-service.

Practical examples include:

• Developers spinning up isolated test environments via a CLI or IaC template without involving the infrastructure team directly.
• Testers provisioning masked, compliant test data snapshots on demand rather than waiting for a DBA to export and sanitise data.
• Security scans integrated into the CI pipeline so developers receive feedback at commit time rather than in a separate audit phase.

2. Permission to Act and Experiment

Granting access to a capability but then requiring manual approval before every action defeats the purpose. Democratisation requires genuine permission to act and—critically—to experiment. Developers and testers need the freedom to try things, fail safely, learn, and iterate without raising a ticket at each step.

This cultural permission is often harder to earn than the technical capability itself. It requires leadership to explicitly endorse a model where practitioners own outcomes, not just tasks. Without that endorsement, self-service tools sit unused while teams default to the familiar safety of waiting for specialist approval.

3. Guardrails to Protect Individuals and the Organisation

Freedom without boundaries is not a sustainable engineering practice. Every self-service capability must have role- and policy-based access controls built in from the start—not retrofitted as an afterthought. These guardrails serve two purposes: they protect the organisation from security and compliance violations, and they protect the individual from inadvertently causing harm.

Effective guardrails are automatic, not advisory. If a developer does not have permission to deploy to a production environment in a specific data centre, the service should refuse that action silently rather than relying on the developer to remember a policy document. If the deployment is allowed, it should be impossible for it to violate a defined compliance boundary. The guardrails must be embedded in the automation of the service itself.

The era of "move fast and break things" has given way to "move fast within safe boundaries." Guardrails are how organisations reconcile speed with governance.

4. An Environment of Trust

Trust flows in multiple directions in a democratised model:

• Practitioners trust the service: results returned by automated pipelines—test outcomes, security scan results, deployment statuses—must be accurate and reliable. If practitioners cannot trust automated output, they will seek manual verification, negating the value of self-service.
• Specialists trust practitioners: a database administrator must be confident that developers using self-service data tooling are not misusing access or circumventing data governance rules.
• Leadership trusts the model: executives must view democratisation as a productivity multiplier rather than a cost-reduction exercise. Measuring success purely by headcount reduction misses the point and will undermine the cultural change required.

Trust is both a prerequisite for democratisation and a product of it. Each successful interaction—where a developer acts autonomously, the guardrails hold, and the specialist is not called in to clean up—builds confidence in the model over time.

Adopting a Democratised DevOps Model

Democratisation requires change from both directions simultaneously.

Bottom-up: Individual practitioners need to see democratisation as a path to elevating their skills, not as a threat to their roles. When repetitive, rote tasks are automated and accessible to others, specialists are freed to work at a higher level—designing better guardrails, improving service reliability, solving harder problems. This is a genuine career opportunity, not a demotion.

Top-down: Senior leaders must actively invest in the transformation. Organisational silos and entrenched fiefdoms are the most common obstacles to DevOps democratisation in large enterprises—often more so than any technical challenge. Command-and-control management structures that have calcified over decades do not dissolve without executive sponsorship. Leadership must be fully and visibly committed; a half-hearted endorsement will stall the transformation at the pilot stage.

Beyond the Full-Stack Superhero Myth

One important outcome of a well-implemented democratised DevOps model is that it removes the organisational dependency on mythical "full-stack engineers"—individuals expected to master every layer of the stack from infrastructure to front-end UI. This expectation may be workable in a small startup but does not scale in a large enterprise with complex compliance requirements, diverse technology estates, and global teams.

The goal is not to create generalists who do everything passably. It is to create a network of specialists who understand and respect each other’s craft, contribute their expertise as reusable services, and collaborate within a framework of shared trust and clear boundaries. That combination—deep specialism, accessible services, and mutual trust—is what allows large organisations to move at speed without sacrificing quality or compliance.

Key Takeaways

• Democratising DevOps means making specialist capabilities available to all practitioners via self-service, APIs, and automation—not eliminating specialisation.
• Effective democratisation requires four elements working together: self-service empowerment, permission to act, automated guardrails, and mutual trust.
• Guardrails must be embedded in automation—not in policy documents—to be effective at scale.
• Both bottom-up practitioner buy-in and top-down executive commitment are required; either alone is insufficient.
• The model reduces reliance on mythical full-stack superheroes and replaces it with a collaborative network of trusted specialists delivering services to each other.

Edge Computing Is Making a Comeback — and the Linux Foundation Is Leading the Charge

At his keynote address at the Open Networking Summit in Belgium, Arpit Joshipura, The Linux Foundation’s general manager of networking, made a bold prediction: edge computing will overtake cloud computing by 2026. That’s a striking claim given the scale of the cloud industry, but Joshipura’s reasoning is grounded in real technical and market forces that DevOps engineers are already navigating.

The story of computing has always been about where compute power lives. In the mainframe era, it was literally down the hall. The cloud era moved it hundreds of miles away — milliseconds in network terms, but far enough to introduce latency that modern workloads increasingly cannot tolerate. Now, driven by IoT, 5G, autonomous systems, and real-time analytics demands, the pendulum is swinging back toward local compute. That’s edge computing.

What Joshipura Means by “Edge Computing”

Joshipura defines edge computing specifically as compute and storage resources that are five to twenty milliseconds away from the end device or data source. This latency target is the key differentiator from cloud computing and drives most of the architectural choices in edge deployments.

Equally important to Joshipura is the nature of that edge infrastructure. He argues it must be:

• Open and interoperable — not locked to any single vendor’s ecosystem
• Hardware-agnostic — independent of specific silicon or server vendors
• Cloud-agnostic — not tied to AWS, Azure, GCP, or any single cloud provider
• OS-agnostic — capable of running on diverse operating environments
• Universal across use cases — supporting IoT edge, telecom edge, cloud edge, and enterprise edge within a single framework

His stated goal: “Our goal here is to unify all of these.”

LF Edge: The Linux Foundation’s Unifying Edge Initiative

The vehicle for this unification effort is LF Edge, a Linux Foundation umbrella organization designed to consolidate the fragmented edge computing landscape under a common open-source software stack. LF Edge’s purpose is explicitly to prevent the edge ecosystem from splintering into incompatible proprietary silos — the same problem that plagued early cloud infrastructure before projects like OpenStack, Kubernetes, and the CNCF ecosystem emerged.

At the summit, Joshipura announced two new projects joining the LF Edge umbrella: Baetyl and Fledge.

Baetyl (formerly Baidu OpenEdge)

Baetyl is an intelligent edge computing framework originally developed by Baidu and donated to LF Edge. Its core purpose is to extend cloud computing capabilities — data processing, AI inference, and cloud services — down to edge devices, enabling developers to build lightweight, secure, and scalable edge applications without requiring full cloud connectivity at runtime.

Its primary target audience is IoT edge device developers who need cloud-class capabilities closer to the data source. Watson Yin, a Baidu VP, explained the donation decision: “[Baidu] decided to donate Baetyl, the intelligent edge computing framework, to the community, hoping to reciprocate the open-source community while continuously contributing cutting-edge technologies to the global technology ecosystem.”

The motivation follows a pattern familiar in enterprise open source: companies contribute strategically to shared infrastructure projects because a healthy ecosystem accelerates their own commercial products built on top of it.

Fledge (formerly FogLAMP)

Fledge is an open-source framework focused on the industrial edge — factories, plants, and mines where latency, reliability, and safety are critical. Its key use cases include:

• Critical operations monitoring
• Predictive maintenance
• Situational awareness
• Safety systems

Fledge is engineered to bridge Industrial Internet of Things (IIoT) sensors and modern machines with legacy industrial “brown field” systems — equipment that uses proprietary protocols and data formats that predate modern networking standards. It does this through a common set of administration and application APIs shared across both legacy and cloud environments.

Tom Arthur, CEO and co-founder of Fledge’s creator Dianomic Systems, explained the industrial problem clearly: “The LF Edge’s efforts for an open, interoperable framework for the edge is especially needed for the industrial factory, plant, and mine — where almost every brown field system, piece of equipment, or sensor uses its own proprietary protocols and data definitions.”

Why Edge Could Realistically Challenge Cloud Dominance

The cloud is not going away. At the time of Joshipura’s keynote, Gartner had estimated the 2019 public cloud market at approximately $214.3 billion, growing at roughly 17.5% annually. That is an enormous base to overtake. So why does Joshipura believe edge will eventually be larger?

The answer lies in the scale of edge use cases and the environments they span. LF Edge’s vision encompasses industrial, enterprise, and consumer scenarios across complex environments spanning multiple edges and domains — effectively everywhere that compute happens outside a centralized data center. The killer applications driving this shift include:

• Video content delivery — reducing latency for streaming and real-time video
• Autonomous vehicles — requiring sub-millisecond decision loops that cloud round-trips cannot support
• Augmented and virtual reality — demanding low-latency rendering at the edge
• 5G infrastructure — where multi-access edge computing (MEC) is a core architectural component
• Cloud gaming — where input lag directly determines product viability

Two Reasons Edge Computing Has Been Underestimated

Joshipura identified two structural reasons edge computing has not yet received the attention its potential warrants:

1. Fragmentation has worked against it. Edge computing projects have historically operated at cross-purposes, with incompatible standards, competing frameworks, and vendor lock-in preventing the kind of ecosystem momentum that cloud computing achieved. LF Edge exists specifically to solve this problem by creating shared, open infrastructure that the whole industry can build on.

2. The killer apps aren’t fully here yet. Autonomous vehicles, pervasive AR/VR, and 5G-native applications were still emerging in 2019. The mass-market use cases that will drive edge deployment at scale were still maturing. As these technologies reach production readiness, the demand for edge infrastructure should accelerate significantly.

What This Means for DevOps Engineers

For DevOps and platform engineering teams, the rise of edge computing introduces new architectural complexity that pure cloud-native tooling does not yet fully address. Key considerations include:

• Orchestration at the edge: Kubernetes distributions designed for resource-constrained environments (such as K3s and MicroK8s) are gaining traction as the operational model for edge deployments.
• Observability across distributed edges: Monitoring pipelines must extend beyond centralized cloud infrastructure to heterogeneous edge nodes with intermittent connectivity.
• Security posture: Edge devices expand the attack surface significantly; zero-trust network models and hardware-rooted trust become more critical.
• Open standards matter: LF Edge’s push for interoperable frameworks reduces the risk of expensive vendor lock-in at the infrastructure layer.

Whether or not edge computing literally overtakes cloud computing by 2026, the direction of travel is clear: compute is moving closer to the data source, and the open-source infrastructure to support that shift is being built now.

Mphasis Stelligent Earns AWS Security Competency Designation

Mphasis Stelligent, a technology services company specializing in DevOps automation on Amazon Web Services (AWS), announced in July 2019 that it had achieved AWS Security Competency status. The designation recognizes the company’s demonstrated technical proficiency and proven customer success in Security Operations and Automation—particularly in the area of DevSecOps.

The AWS Security Competency differentiates Mphasis Stelligent within the AWS Partner Network (APN) as a consulting partner capable of helping enterprises adopt, develop, and deploy complex security projects on AWS. To receive the designation, APN Partners must demonstrate deep AWS expertise and the ability to deliver solutions seamlessly on the platform.

Security as a Foundation for DevOps Automation

Paul Duvall, founder and CTO of Mphasis Stelligent, emphasized the company’s philosophy on security in DevOps engagements:

“A strong security posture is absolutely essential to effective DevOps automation. With our exclusive focus on Amazon Web Services, we’re able to put security at the foundation of our AWS customer solutions. Our team takes an ‘automate everything’ approach to each engagement by automating security controls and governance through the Continuous Delivery (CD) pipeline. This allows customers to develop on AWS with more speed, consistency, visibility, and confidence, knowing security is built-in.”

— Paul Duvall, Founder and CTO, Mphasis Stelligent

Four-Phase Approach to AWS Security Automation

Mphasis Stelligent uses a structured, four-phase methodology to help enterprises automate security on AWS:

1. Assessment – Profile the existing environment and identify areas for improvement in security posture and compliance gaps.
2. Foundations – Determine the security strategy and lay the architectural groundwork for security and compliance controls on AWS.
3. Extended Controls – Implement additional controls designed to mitigate and reduce business risk across cloud workloads.
4. DevSecOps – Automate security testing and infrastructure security inspections directly within the Continuous Delivery pipeline, embedding security as a first-class concern rather than an afterthought.

Notable Customers

At the time of the announcement, Mphasis Stelligent was working with a range of enterprise customers—including 3M Health Information Systems, Verizon, and The Washington Post—to strengthen their cloud security posture while streamlining development workflows on AWS.

About the AWS Competency Program

The AWS Competency Program was established by Amazon Web Services to help customers identify APN Consulting and Technology Partners with validated experience and expertise in specific domains—including security, machine learning, migration, and more. Achieving a competency designation requires partners to pass a technical validation review and demonstrate proven customer success.

About Mphasis Stelligent

Stelligent is part of Mphasis, a global IT solutions provider specializing in cloud and cognitive services. Operating within the Mphasis Digital Business Unit, Mphasis Stelligent delivers DevOps automation professional services exclusively on AWS. The company focuses on building fundamentally secure infrastructure-as-code, deployment pipelines, and feedback mechanisms that enable engineering teams to ship software faster and more consistently—without sacrificing security or compliance.

Why Agile Still Matters

Agile has earned its place in the business lexicon. For almost two decades it has helped companies of all sizes break free from the inflexibility of traditional top-down hierarchies and rigid long-term planning. You will hear talk of “squads”, “chapters” and “tribes”, and although different flavours of Agile exist, certain patterns recur: teams work in short sprints, they are empowered to make decisions, and success is measured by responsiveness to customer demands rather than adherence to a fixed plan.

Justin Rodenbostel, Vice President at digital transformation agency SPR, argues there is no “after Agile”. He sees adjacent movements such as DevOps, Continuous Delivery, and Lean as direct extensions of Agile thinking, and believes organisations will continue drawing on the original Agile Manifesto to evolve how they work and respond to customers.

But Agile is also demanding. It requires intense, sustained commitment and depends heavily on organisational culture to succeed. According to the annual State of Agile survey, the top three challenges are general resistance to change, inadequate management support, and a culture at odds with Agile values. Below, practitioners share the most common pitfalls — and how to navigate them.

Pitfall 1: Short Sprints Can Obscure Long-Term Vision

One of Agile’s biggest draws is that annual planning is replaced by shorter-term milestones. Teams focus on delivering value now rather than executing a fixed multi-year roadmap. That responsiveness is powerful, but it carries a risk: teams can lose sight of where the organisation is ultimately heading.

Ian McClarty, CEO of global IT service provider PhoenixNAP, puts it plainly: “When teams are so heavily focused on the current sprint, communication about long-term vision may be put aside or ignored, leaving the team with a feeling that there is no grand plan they are supporting.”

The most straightforward remedy is deliberate, regular communication of larger business objectives — whether that is doubling headcount within two years or reaching a specific revenue milestone. McClarty also notes that Agile itself can reshape those goals by helping organisations fail faster and learn from mistakes sooner. He explains: “The ability to carve a project into many smaller pieces helps us create a sense of continued progress and motivation within teams, and also helps us create cleaner stop points — dropping features out of iterations to allow for faster time to market to see if the product will be successful before investing too much.”

Practical steps

• Share business-level OKRs or strategic goals at each sprint planning session so teams understand context.
• Run quarterly “big picture” reviews that sit above the sprint cycle and revisit organisational direction.
• Use product roadmaps — even lightweight ones — to communicate intent without locking in rigid plans.

Pitfall 2: The Agile Mindset Requires Constant Reinforcement

Christopher McFarlane, Agile Project Manager for Walmart Canada, identifies two distinct hurdles in building an Agile mindset: convincing internal team members of its value, and educating external customers and suppliers on how to engage with Agile teams.

“Encouraging team members and partners to understand the benefits of this new way of working — fast failure, constant communication, transparency — is the first hurdle,” McFarlane explains. “The second is a technical understanding of the components of an Agile methodology and educating stakeholders on how to engage with the Agile team, which is different from other areas of the business.”

Dr Matt Poepsel, Senior Vice President of Product at Predictive Index, found that a successful mindset shift required coaching individuals in the soft skills Agile demands: flexibility, collaboration, discipline, and a focus on results. He notes that any business transformation demands a simultaneous talent transformation: “We coached each individual by examining where their natural behaviours and approach were well-suited to some of these activities and where they needed to stretch to succeed.”

Chris Steel, Head of Transformation for North America at PA Consulting, emphasises the importance of early wins. “Where we have seen organisations struggle is where they approach it in a half-hearted way without the right sponsorship. The change to culture and ways of working required to achieve the best results is too significant without full engagement.”

Practical steps

• Identify and publicise early successes to build internal credibility for Agile practices.
• Invest in soft-skills coaching alongside technical Agile training.
• Secure visible sponsorship from senior leadership — Agile transformations stall without it.
• Create onboarding materials specifically for external stakeholders explaining how to collaborate with Agile teams.

Pitfall 3: Teams Slide Back into Traditional Management Styles

Adopting Agile is not a one-time event. Without sustained attention, teams can revert to the top-down, hierarchical structures they were accustomed to — particularly under delivery pressure.

David Fort, Managing Director at Haines Watts Manchester, describes it this way: “Being an Agile business isn’t a start-stop scenario; it’s a constant shift in culture and balance that has to be regularly revisited. If you stop running as an Agile business, you’re likely to seize up. The real challenge is ensuring the agility is fresh and team members remain focused on being Agile.”

Fort also advises factoring Agile compatibility into hiring decisions — prioritising candidates who demonstrate adaptability and comfort with ambiguity alongside technical skills and academic credentials.

Practical steps

• Schedule regular retrospectives that explicitly examine whether Agile principles are still being followed — not just whether delivery targets are being met.
• Include Agile values and behaviours in performance reviews and hiring criteria.
• Rotate Agile coaching and facilitation responsibilities to avoid over-dependence on a single champion.

Pitfall 4: Agile Does Not Scale Automatically

Rodenbostel identifies what he considers the most common mistake: assuming that success achieved by a small Agile team can be replicated at scale simply by applying the same practices to larger groups. “What works for small groups often doesn’t work for larger or different groups,” he warns. “Customers, business stakeholders, software assets, and government or industry regulation can all have an impact on what works and what does not. We see a tendency when scaling to standardise in a way that is often too prescribed.”

He also notes that the Agile move away from big upfront annual planning is particularly challenging in publicly traded companies, where investor expectations can push leadership back toward rigid, predictable roadmaps. The goal, he says, is to determine the appropriate balance rather than insisting on Agile purity in every context.

Practical steps

• Treat scaling frameworks (SAFe, LeSS, Nexus, etc.) as starting points, not prescriptions — adapt them to your specific constraints.
• Run a deliberate pilot with a small team before scaling Agile practices organisation-wide, and document what worked and what did not.
• Acknowledge regulatory and investor-relations constraints explicitly and design your Agile practice around them rather than ignoring them.
• Avoid standardising processes across teams prematurely — let teams develop practices that suit their context before rolling out a uniform model.

The Bottom Line

Agile is not a silver bullet, but it is a durable and well-evidenced approach to building adaptive, customer-focused teams. Its failures are rarely about methodology — they are almost always about culture, commitment, and communication. The organisations that get the most out of Agile are those that treat it as an ongoing practice rather than a project to be completed, invest in the human side of the transformation, and are honest about the contexts where a more structured approach remains necessary.

As Rodenbostel puts it, the concepts in the original Agile Manifesto are not going away. The challenge is applying them with the discipline and self-awareness they demand.

The Security Friction Problem in DevOps

As DevOps matures from an experimental practice into an organizational standard, it brings with it a familiar tension: the need to ship fast versus the need to stay secure. Survey data cited by Qualia suggests that as DevOps becomes mainstream, both budgets and headcount are increasingly tied to measurable business outcomes—which means speed and throughput are on the scorecard. Security checkpoints that slow delivery are felt directly.

The collision between delivery velocity and security compliance is predictable. DevOps teams optimize relentlessly for automation and flow. Security and audit teams introduce gates and approval steps. When those two forces meet without a coherent strategy, the result is usually one of two failure modes: security slows delivery to an unacceptable crawl, or developers route around security controls entirely.

DevOpstacles: When Security Becomes an Obstacle

John Willis, VP of DevOps and Digital Practice at SJ Technologies and co-author of The DevOps Handbook, frames the core challenge clearly:

“Most modern-day DevOps teams are all about continuously removing obstacles and are maniacal about streamlining automation—as it should be. However, that’s not to diminish the fact that there is a very legitimate need to incorporate security into DevOps, so much so that the term DevOps has expanded to DevSecOps (for security). To be successful, businesses need to incorporate security into DevOps during the development and planning stage, rather than treating security as an afterthought.”

— John Willis, co-author, The DevOps Handbook

The key phrase here is afterthought. When security is bolted on at the end of a delivery pipeline rather than built into the process from the start, every security control feels like an interruption. Over time, engineers under delivery pressure will find workarounds—not out of malice, but because the system design incentivizes speed and punishes delay, regardless of cause.

A Concrete Example: SSH Key Vaulting

Privileged access management—specifically, the vaulting of SSH keys—illustrates the friction problem well. In principle, a key vault enforces accountability: every access credential is registered, checked out before use, and checked back in afterward. In practice, the workflow looks like this:

1. An engineer needs to access a target server.
2. They must locate the correct key in the vault and formally check it out.
3. They perform their task.
4. They return the key to the vault.

Each step adds latency to an otherwise automated process. In a fast-moving CI/CD pipeline where dozens of deployments may happen daily across many target systems, this overhead compounds quickly. The predictable outcome: engineers begin placing new SSH keys directly on target systems, bypassing the vault entirely. What looked like a security control on paper becomes, in practice, an unaudited proliferation of unmanaged credentials—arguably worse than no control at all.

The Better Approach: Embed Security Into the Flow

The alternative is not to lower security standards—it is to redesign security controls so that they work with the DevOps workflow rather than against it. Several patterns make this possible:

Ephemeral, Short-Lived Credentials

Instead of storing long-lived private keys on endpoints or in vaults, issue short-lived SSH certificates that expire automatically after a defined window (minutes to hours, depending on the use case). A certificate that expires cannot be exfiltrated and reused later. This approach eliminates the key-management lifecycle problem—there is nothing to rotate, nothing to audit for staleness, and nothing to accidentally leave on a compromised host.

Role-Based Access Control (RBAC) in Real Time

Rather than granting individuals access to specific keys, grant roles access to classes of systems. When an engineer authenticates, the system evaluates their current role, checks policy, and issues a certificate scoped to exactly what that role is permitted to reach. Access is granted just-in-time and revoked automatically. No ticket required, no manual approval chain for routine access.

Inventory-Driven Access

DevOps teams should not be burdened with maintaining their own asset inventory or credential stores. A well-implemented privileged access solution presents engineers with an authoritative, always-current list of the systems they are permitted to reach. A single click—or a single CLI command—initiates a properly authenticated, fully audited session. The security machinery runs invisibly in the background.

Eliminate Shared Passwords from Server Access

Shared credentials for sysadmin or cloud-console access are a persistent risk: they cannot be attributed to an individual, they are rarely rotated promptly after personnel changes, and they are frequently reused across environments. Replacing shared passwords with individual, certificate-based authentication—issued dynamically and tied to a verified identity—closes this gap without adding manual overhead.

The Cultural Dimension: DevSecOps as Organizational Shift

Technical controls alone are not sufficient. The survey data referenced in the original source points to enterprises treating culture change as a foundational element of a DevOps rollout—not an afterthought. When executives sponsor DevSecOps as a strategic initiative, the organizational dynamics shift: security teams move from gatekeepers to enablers, and development teams stop viewing security reviews as hostile interruptions.

This culture shift requires deliberate effort on both sides. Security practitioners must learn the pipelines, toolchains, and delivery cadences that DevOps teams operate under. Development and operations engineers must accept that some controls are non-negotiable and work with security to implement them in ways that fit the workflow. The goal is shared ownership of risk, not security-as-a-separate-department.

Key Takeaways

• Security bolted on at the end of the pipeline creates friction that engineers will route around. The resulting shadow IT and unmanaged credentials are often riskier than the problem the controls were meant to solve.
• Ephemeral, certificate-based SSH access removes the key-management lifecycle problem without sacrificing auditability or accountability.
• Just-in-time, role-based access control aligns security policy with how DevOps teams actually work—dynamically, at scale, and across many target systems simultaneously.
• Automation is the shared language of DevOps and modern security. Controls that can be enforced automatically—certificate issuance, expiry, session recording, access revocation—are far more reliable than controls that depend on manual compliance.
• Culture matters. DevSecOps succeeds when security and engineering teams collaborate from the planning stage, not when security audits a completed product.

The path forward for organizations trying to reconcile velocity with security is not to choose one over the other. It is to redesign the security architecture so that the two reinforce each other—where doing the secure thing is also the fastest, easiest path for the engineer.

What Is the OverOps Platform?

OverOps launched a platform that applies machine learning algorithms to detect potential software issues before a DevOps team promotes an application into production. The platform is distinctive because it does not rely primarily on log data. Instead, it combines static and dynamic analysis of code as it executes, capturing richer context about errors and exceptions at the moment they occur.

According to OverOps CTO Tal Weiss, this approach captures machine data about every error and exception in real time, including:

• The value of all variables across the full execution stack at the moment of failure
• The frequency and failure rate of each error
• Classification of errors as new or reintroduced (regression)
• The associated release version number for each event
• Uncaught and swallowed exceptions that would never appear in a standard log file

Why Log-Based Analysis Falls Short at Scale

Traditional operations monitoring depends heavily on log files. DevOps teams spend significant time combing through log data in search of anomalies—but logs have a fundamental limitation: they only capture what a developer explicitly chose to log. Uncaught exceptions, swallowed errors, and missing variable state are invisible in log output.

Scalability compounds the problem. As distributed systems grow, the volume of log data can reach millions or even billions of entries. Storing, shipping, and querying that data becomes expensive and slow. Root-cause analysis through log correlation is inherently difficult and time-consuming.

OverOps addresses this by analyzing events before a log file is created, reducing dependence on log storage infrastructure while providing deeper context than logs typically contain.

Platform Architecture and Integrations

The OverOps Platform is designed to fit into existing DevOps toolchains without requiring every tool in the pipeline to natively support machine learning. Key architectural points include:

• Open API: Insights and event data can be shared with other tools in the DevOps pipeline via an open API, making integration with existing monitoring, alerting, and CI/CD systems straightforward.
• Grafana-based dashboards: The included dashboards are built on Grafana, the widely used open-source observability visualization platform, giving teams a familiar interface.
• Serverless function support: Teams can extend the platform using an AWS Lambda-based framework or a separate on-premises serverless computing framework to build custom functions and workflows.

Machine Learning in the CI/CD Gate

One of the more operationally significant use cases is using OverOps as a quality gate in the CI/CD pipeline. By classifying errors as new, regressed, or known before a release is promoted, teams can make more confident deployment decisions based on objective code-level evidence rather than subjective test coverage assumptions.

This matters particularly in microservices architectures built on containers, where the rate of deployments is high and the operational complexity introduced by many small, independently deployable services can be significant. Automated, ML-driven error classification helps teams keep pace with deployment velocity without sacrificing reliability.

The Broader Case for AI in DevOps Operations

Resistance to AI and machine learning in production operations is understandable—incorrect automated decisions can cause outages or mask real problems. However, the scale at which modern distributed systems operate increasingly makes purely manual approaches unworkable.

The OverOps approach represents a pragmatic middle ground: use ML to surface and classify issues, present that information to engineers through dashboards and APIs, and let humans make the final call on deployment. This keeps engineers in the decision loop while dramatically reducing the manual effort required to identify what needs attention.

It is worth noting that ML-assisted code analysis is a tool for augmenting DevOps teams, not replacing them. Deep knowledge of system behavior, business context, and acceptable risk remains squarely in human hands.

Key Takeaways

• OverOps uses static and dynamic code analysis—not log parsing—to detect errors at runtime.
• The platform captures full execution-stack context (variable values, error frequency, release version) at the moment an error occurs.
• Grafana dashboards and an open API enable integration with existing DevOps toolchains.
• AWS Lambda and on-premises serverless support allow teams to build custom workflows on top of the platform.
• ML-based error classification can serve as a CI/CD quality gate before production promotion.
• The approach reduces—but does not eliminate—the need for skilled DevOps engineers to interpret results and make deployment decisions.

DevOps Is Not Just for Developers

One of the most persistent misconceptions about DevOps is that it belongs exclusively to software engineers writing code. It does not. Business leaders, infrastructure teams, cloud architects, and operations staff all have a stake in DevOps — and all need to understand what it is and what it can deliver for an organization.

DevOps touches every layer of a modern technology stack: from custom application development and API integration to data pipelines, cloud infrastructure, and the cultural processes that tie them together. If your team thinks DevOps is just a group of developers doing “some kind of development operation,” that misunderstanding itself becomes a barrier to adoption.

The Problem with Legacy

Before defining DevOps, it is worth defining what DevOps is trying to replace: legacy. At a practical level, legacy refers to any process, codebase, or infrastructure that demands significant manual intervention and generates complex, reactive support burdens. This includes:

• Monolithic codebases that are difficult to modify or test in isolation
• Manual deployment pipelines with error-prone handoffs between teams
• On-premises infrastructure that cannot scale dynamically
• IT processes designed around infrequent, high-risk releases

As organizations move toward data-driven operations and distributed architectures — spanning on-premises data centers, public cloud providers, and third-party hosting — legacy practices increasingly become a competitive liability. The digital business demands agility and delivery velocity that classical architectures simply were not designed to provide.

What DevOps Actually Is

DevOps is not a single tool, a job title, or a two-word portmanteau. It is a multidimensional discipline that spans culture, process, tooling, and philosophy. Specifically, DevOps is:

• A cultural shift in how code, processes, and technology are designed and delivered across teams
• A philosophy of continuous development and integration that responds to users, business needs, and market dynamics
• A practice that continuously evolves as technology and organizational needs change
• A toolchain that helps teams deliver services and applications at market speed
• A process that enables organizations to innovate faster than legacy software and infrastructure could ever allow

Jez Humble, co-author of The DevOps Handbook, defines DevOps as “a cross-disciplinary community of practice dedicated to the study of building, evolving, and operating rapidly-changing resilient systems at scale.” That definition is worth sitting with. DevOps is not about releasing software faster for its own sake — it is about building systems that are resilient, scalable, and continuously improving.

Five Steps to Reach a State of DevOps

Whether you are working in a global enterprise or a mid-market organization, the journey toward DevOps maturity tends to follow a recognizable path. Here are five critical steps based on common patterns in organizations making the transition.

1. Have an Honest Reckoning with Your Current Development Process

The first step is acknowledging that your current development process cannot scale to meet market demands. This is harder than it sounds. Teams that have built custom systems and written millions of lines of code often have strong institutional attachment to their existing approaches. But if your release cadence is slowing your competitive response, or if your competition is shipping features faster than you can, that is a signal worth heeding.

Conduct an honest ecosystem evaluation: Where are the bottlenecks? Where does the delivery pipeline break down? Which systems are being maintained out of habit rather than necessity? These questions are uncomfortable but they are the foundation of any meaningful DevOps strategy.

2. Map Your Systems, Dependencies, and Delivery Pipelines

Before you can modernize, you need a detailed picture of what you are working with. This means documenting your applications, the libraries and frameworks they depend on, your build and compilation processes, data repository locations, and the APIs your services consume and expose.

Applications are complex systems, and application data is often the hardest asset to migrate or modernize. Skipping this discovery phase is one of the most common reasons DevOps transformations stall mid-journey. Invest the time upfront.

3. Define a Future-State Architecture

A core principle of DevOps planning is designing for what your organization needs to be, not just what it is today. This means making deliberate decisions about cloud adoption strategy, system migration timelines, the phased retirement of legacy infrastructure, and the application capabilities your business will require in the future.

Documenting a future-state target gives business leaders and engineering teams a shared reference point. It makes abstract conversations about “continuous integration” and “innovation velocity” concrete and actionable.

4. Include Infrastructure and Cloud Teams from the Start

DevOps transformations frequently fail when they are treated as a developer-only initiative. Infrastructure engineers, data center teams, network architects, and cloud platform specialists all need a seat at the planning table.

The reason is straightforward: the cultural shift that DevOps requires has to happen at multiple organizational levels simultaneously. Cloud platforms in particular offer a rich ecosystem of DevOps tooling — from managed CI/CD pipelines to infrastructure-as-code services — that infrastructure teams are best positioned to evaluate and implement. Leaving them out of the conversation guarantees friction later.

5. Work with a Partner Who Has Done This Before

DevOps is designed to simplify how software is delivered — but getting there from a complex, heterogeneous environment is genuinely difficult. Organizations with deeply entrenched legacy systems often find the transformation discouraging without external guidance.

Working with an experienced partner — whether a consultancy, a systems integrator, or a cloud provider’s professional services team — can accelerate the initial stages of the journey. A good partner helps you understand your ecosystem clearly, avoid common pitfalls, and build the internal competency to sustain DevOps practices independently over time.

The DevOps Delivery Cycle

The ultimate goal of a DevOps practice is to move away from infrequent, scheduled release windows toward continuous delivery. In a mature DevOps organization, application updates are deployed multiple times per day rather than on a weekly or monthly schedule.

The standard DevOps lifecycle phases are:

1. Plan — Define features, requirements, and priorities collaboratively across development, operations, and business teams
2. Create — Write and commit code using version control and collaborative branching strategies
3. Verify — Run automated tests (unit, integration, security, performance) in a continuous integration pipeline
4. Package — Build deployable artifacts, container images, or packages from verified code
5. Release — Deploy to production through an automated, auditable release pipeline
6. Configure — Manage infrastructure and application configuration as code, ensuring consistency across environments
7. Monitor — Observe system behavior, collect metrics and logs, and feed insights back into the next planning cycle

This is a continuous loop, not a linear sequence. Each monitoring cycle informs the next planning cycle, which makes the entire system self-improving over time.

Why Continuous Innovation Requires Continuous Action

Once a DevOps practice is established, integrating new tools, processes, and even entire business units becomes significantly easier. Continuous integration and delivery keeps an organization genuinely agile — able to respond to market shifts, customer feedback, and competitive pressure in near-real time.

The risk of delay is compounding. Every month that legacy development practices remain in place, the codebase and the operational debt around it grows larger and harder to address. Organizations that start the DevOps journey today build an innovation engine that becomes a durable competitive advantage. Those that wait find the problem getting harder, not easier.

DevOps is not a destination — it is a discipline. Start the process now, build incrementally, and let the feedback loop do its work.

What the Global DevOps Tool Market Looked Like in 2018

By mid-2018, the DevOps tools market had matured from a niche engineering practice into a mainstream software-delivery discipline. Enterprises across industries were consolidating around toolchains that span planning, source control, continuous integration, configuration management, containerisation, and monitoring. Analyst reports from the period consistently pointed to double-digit compound annual growth rates driven by cloud adoption, agile transformation programmes, and pressure to shorten release cycles.

Key Vendors in the DevOps Tool Landscape

The market research referenced in the original post identified the following companies as significant players in the global DevOps tools space at the time of publication:

• Puppet Labs – infrastructure automation and configuration management
• Chef – policy-based infrastructure automation
• Docker Inc. – container runtime and image registry
• Red Hat – enterprise Linux, OpenShift container platform, and Ansible automation
• Atlassian – Jira, Confluence, Bitbucket, and Bamboo for planning and CI/CD
• SaltStack – event-driven automation and configuration management
• CA Technologies – release automation and agile management tooling
• Rackspace – managed cloud services with DevOps consulting
• XebiaLabs – release orchestration and deployment automation
• VersionOne – agile and scaled-agile planning
• Cisco – network automation and infrastructure tooling
• CollabNet – ALM and DevOps value-stream management
• HP (Micro Focus) – quality management, testing, and release tooling
• IBM – UrbanCode Deploy, cloud-native toolchains
• Microsoft – Azure DevOps (then Visual Studio Team Services), GitHub
• Spirent Communications – network test and assurance
• VMware – virtualisation platform and emerging Kubernetes tooling
• DBmaestro – database DevOps and release automation

This list reflects both pure-play DevOps vendors and large enterprise software companies that had built or acquired DevOps capabilities to defend their existing customer bases.

Market Segments and Product Categories

DevOps tools are rarely sold as a single product. Market research from this era typically segmented the landscape by function:

• Source control and collaboration – Git hosting, code review, and inner-source platforms
• Continuous integration and continuous delivery (CI/CD) – pipeline orchestration, build automation, and artefact management
• Configuration management and infrastructure-as-code – tools that declare desired system state and enforce it at scale
• Containerisation and orchestration – container runtimes, image registries, and Kubernetes distributions
• Monitoring, logging, and observability – metrics collection, log aggregation, distributed tracing, and alerting
• Test automation – unit, integration, performance, and security testing integrated into pipelines
• Release and deployment automation – environment-aware deployment orchestration and rollback
• Security and compliance (DevSecOps) – static analysis, dependency scanning, and policy enforcement

Regional Market Dynamics

In 2018, North America led DevOps tool adoption due to the concentration of technology-native companies and early enterprise adopters. Europe followed, with regulated industries such as financial services and healthcare driving demand for auditable, compliant CI/CD pipelines. The Asia-Pacific region was identified as the fastest-growing geography, fuelled by rapid cloud infrastructure build-out in China, India, and South-East Asia. Latin America and the Middle East and Africa were at earlier stages of adoption but were expected to accelerate as cloud availability improved.

Factors Driving Market Growth

Several converging forces were pushing organisations to invest in DevOps tooling during this period:

• Cloud-first strategies – public cloud adoption created demand for tools that provision and configure infrastructure programmatically.
• Microservices architecture – decomposing monoliths into independently deployable services multiplied the number of pipelines teams needed to manage.
• Container adoption – Docker and Kubernetes dramatically changed how artefacts were packaged and how deployments were orchestrated.
• Regulatory pressure – industries such as banking and healthcare needed auditable deployment records, driving demand for release-automation tooling.
• Talent economics – automation reduced the manual toil associated with operations, allowing smaller teams to manage larger infrastructure footprints.
• Competitive pressure – faster delivery cycles became a direct competitive advantage, incentivising investment in toolchain improvement.

Forecast to 2026: What the 2018 Outlook Anticipated

Market research published around 2018 generally projected that the global DevOps tools market would sustain strong growth through 2026, driven by the factors above. Key themes expected to shape the market over that horizon included:

• Consolidation – large platform vendors were expected to acquire specialist tools, reducing the number of independent point solutions.
• AI and ML integration – predictive analytics for pipeline failures, anomaly detection in production, and automated remediation were emerging areas of R&D investment.
• Value-stream management – organisations were beginning to measure DevOps not just by deployment frequency but by business outcomes, creating demand for end-to-end visibility tooling.
• GitOps – Git as the single source of truth for both application and infrastructure state was gaining traction and was expected to become a dominant deployment pattern.
• Security shift-left – integrating security scanning directly into developer workflows, rather than as a gate at release time, was forecast to become standard practice.

Evaluating DevOps Tool Vendors: What to Look For

Whether you are benchmarking vendors in 2018 terms or evaluating the market today, a rigorous vendor assessment should examine:

• Integration breadth – how easily does the tool connect to the rest of your existing toolchain?
• Scalability – can the tool handle your pipeline volume, concurrency, and artefact storage requirements at scale?
• Security posture – does the vendor offer role-based access control, audit logging, and compliance certifications relevant to your industry?
• Community and ecosystem – active open-source communities and large plugin ecosystems reduce lock-in and accelerate problem resolution.
• Total cost of ownership – licensing, hosting, staffing, and training costs all factor into the real cost of a toolchain decision.
• Vendor viability – acquisition history, funding, and customer retention rates signal whether a vendor will continue to invest in the product.

Summary

The global DevOps tools market in 2018 was a dynamic, rapidly expanding space characterised by strong venture investment, active M&A, and accelerating enterprise adoption. The vendor list from that era includes names that remain dominant today alongside others that have since been acquired or repositioned. For engineering teams evaluating toolchain investments, understanding this historical context helps explain how the current landscape of consolidated platforms and open-source ecosystems came to be shaped.

Original market research source: theperfectinvestor.com (2018). The underlying paid report is not publicly available; specific figures and methodology are not reproduced here.

Every Company Is Now a Software Company

In today’s digital economy, the ability to build and deliver software quickly is no longer exclusive to technology firms. Organisations across every sector — retail, financial services, manufacturing, government — compete on software. Those that can transform rapidly and seize emerging opportunities ahead of rivals hold a decisive advantage.

This shift has driven demand for application development to record levels, according to the State of Application Development 2018 study published by OutSystems. Yet most organisations struggle to match that demand with their existing development capacity and tooling.

The same report found a growing emphasis on building customer-facing and partner-facing applications over internal tooling — a signal that software is now directly tied to business revenue, brand reputation, and competitive standing.

Why Speed to Market Is the Core Challenge

Getting to market first requires both technical velocity and organisational courage. Rapid experimentation — releasing minimal viable products, testing assumptions with real users, and iterating quickly — demands a development approach that is far faster than traditional hand-coded workflows.

Low-code platforms have emerged as a practical answer to this pressure. By replacing large amounts of manually written code with visual modelling, pre-built components, and automated scaffolding, they compress development cycles significantly and open contribution to a broader pool of developers.

Four Priorities Low-Code Platforms Support in Digital Transformation

According to OutSystems, the right low-code platform should move the needle on four interconnected priorities that IT organisations typically focus on during digital transformation programmes:

1. Enable Greater Innovation

Low-code development can be substantially faster than hand-coding for many application types. Visual prototyping supports design-thinking workshops, while fast creation of minimal viable products (MVPs) and iterative “test and learn” cycles bring developers and end users into close, productive collaboration earlier in the process.

2. Improve Agile Adoption

Siloed front-end, back-end, and mobile specialisms can slow agile teams down. Low-code platforms can reduce this fragmentation by giving developers a single integrated development environment (IDE) and a unified skill set, effectively turning specialised developers into full-stack contributors and making cross-functional agile squads more practical.

3. Architect Flexible Integration

Legacy systems rarely disappear overnight. A capable low-code platform should support REST and SOAP APIs, allow visual modelling of back-end services, and encourage a layered service architecture with composable applications, decoupled front-ends, and loosely coupled services. This approach preserves existing investments in slow-evolving core systems while enabling new digital experiences to be built on top of them.

4. Achieve DevOps Success

Low-code and DevOps are complementary, not competing, approaches. A well-designed low-code platform should provide comprehensive version control, support collaborative development across multi-developer and multi-team organisations, and offer proactive application performance monitoring — all of which underpin reliable, high-frequency software delivery.

Five Practical Ways to Accelerate Your Digital Transformation

Miguel Baltazar, Director of Transformation Services at OutSystems, identified five structured approaches that organisations can use to build momentum in their digital transformation programmes:

1. Design Customer Journey Maps

Customer journey maps are visual representations of how a customer interacts with your organisation across time and channels — from initial awareness through purchase, support, and ongoing engagement. Mapping these journeys helps teams empathise with customer motivations and pain points, set project goals grounded in real user needs, and define the right scope before a single line of code is written.

2. Create Clickable Prototypes

Interactive prototypes — even simple ones — give stakeholders a tangible sense of how an application will look and behave. They accelerate feedback loops, surface misaligned expectations early, and reduce the risk of building the wrong thing. Prototypes can be tested with real users, iterated rapidly, and validated against project goals before significant engineering investment is committed.

3. Build Multi-Disciplinary Teams

Effective digital delivery typically draws on six areas of expertise: business analysis, demand management and governance, software development, solution architecture, user experience design, and operations. Keeping individual teams small improves communication, preserves autonomy, and encourages innovation. Cross-functional squads that span these disciplines reduce handoff delays and build shared ownership of outcomes.

4. Organise a Hackathon

Hackathons — intensive collaborative sessions ranging from a few hours to several days — act as focused innovation labs. They work across industries: financial services, retail, government, and e-commerce organisations have all used them to rapidly prototype new ideas, surface hidden talent, and build internal momentum for transformation. OutSystems has published a hackathon planning guide that covers how to structure and run one effectively.

5. Adopt a Structured Digital Transformation Playbook

Ad hoc transformation efforts often stall without a repeatable methodology. A structured playbook — covering how to initiate, govern, and scale digital projects — gives teams a shared language and a step-by-step framework. The OutSystems Digital Transformation Playbook introduces what they term the “low-code digital factory” model, designed to guide organisations from initial projects through to scaled, continuous delivery.

Start Small, Then Scale

Organisations that succeed with low-code-driven transformation tend to follow a common pattern: start with a contained, high-visibility project; deliver a working application faster than stakeholders expect; then use that proof point to secure support for larger initiatives.

Logitech’s experience illustrates this well. Steven Schmidt, Enterprise Collaboration Manager at Logitech, used low-code development to build and iteratively modify a mission-critical product launch application. The speed of delivery and ease of modification were, in his words, what allowed the team to simply “do more.”

Starting small reduces political risk, builds internal confidence, and establishes repeatable delivery patterns that larger programmes can inherit. A project that goes live in a few weeks — rather than months — demonstrates the feasibility of a new approach far more effectively than any strategy document.

Key Takeaways

• Digital transformation is fundamentally a software delivery challenge: organisations that ship relevant applications faster win.
• Low-code platforms can significantly reduce development cycle times and broaden who can contribute to application delivery.
• Four platform capabilities matter most: enabling innovation, improving agile adoption, supporting flexible integration, and enabling DevOps practices.
• Structured approaches — journey mapping, prototyping, multi-disciplinary teams, hackathons, and a delivery playbook — provide repeatable frameworks for accelerating transformation.
• Starting small and delivering quickly builds the organisational trust and momentum needed to scale transformation programmes.

The Problem: Network Security Is the Last Manual Bottleneck

Modern DevOps practices have automated nearly every step of the software delivery lifecycle. Virtualization, public and private cloud platforms, and software-defined networking (SDN) mean that spinning up servers, provisioning storage, or launching entire environments can take minutes or even seconds. CI/CD pipelines handle builds, tests, and deployments with no human intervention.

Yet one step has stubbornly resisted automation: changing network connectivity and firewall rules. When a new application or service needs a firewall rule opened or a network flow provisioned, developers typically must stop, open a manual change request, wait for approval from a network security team, and then wait again for implementation. This single step can turn a sub-hour deployment into a multi-day process.

The Application Development–Security Disconnect

The friction is not just procedural—it is also a knowledge gap. Change request forms typically ask developers to specify firewall names, network zones, subnets, and IP addresses. Application developers often do not have this information and should not need it: their job is to describe what the application needs to communicate with, not to understand the underlying network topology.

Conversely, the network security team reviewing the request rarely has full context about why the application needs a particular connection, what business process it supports, or what the impact of a delay or denial will be. The result is back-and-forth communication that makes the process slow, error-prone, and frustrating for both sides.

The core challenge is balancing two legitimate needs:

• Developer agility: changes should flow through the pipeline automatically, without waiting on manual approvals for routine, low-risk connectivity.
• Security governance: not every connectivity change is routine or low-risk, and a fast pipeline that skips security checks simply moves the risk rather than eliminating it.

The Solution: Connectivity as Code

Connectivity as code is a methodology that brings network connectivity provisioning into the CI/CD pipeline as a first-class, automated step. Instead of opening a manual change request, developers declare their application’s connectivity requirements in a machine-readable file that lives alongside the application code in version control. The pipeline then handles connectivity provisioning, validation, and compliance checking automatically—just like it handles unit tests or infrastructure provisioning.

The key architectural idea is an abstraction layer that translates between the developer’s logical view of the application and the network security team’s physical view of the infrastructure. Developers work in terms of application components and logical flows; the abstraction layer maps those to the actual firewalls, zones, IP ranges, and security policies that the network team manages.

How Connectivity as Code Works in Practice

Step 1: Declare Connectivity Requirements

The developer creates a connectivity requirements file—a simple, human-readable, machine-parseable text file (YAML and JSON are common choices, though the methodology is format-agnostic) that lists all the logical flows the application needs. Each flow entry typically includes:

• Source: the component or service initiating the connection (e.g., app-server, frontend)
• Destination: the component or service accepting the connection (e.g., database, payment-api)
• Service/port: the protocol and port required (e.g., tcp/5432, https)
• Environment: which deployment environment the flow applies to (development, staging, production)

Critically, the developer does not need to know IP addresses, subnet ranges, firewall names, or whether a firewall even exists between the two components. That translation is handled downstream by the automation tooling.

# Example connectivity requirements file (illustrative YAML)
flows:
  - name: app-to-database
    source: app-server
    destination: postgres-db
    service: tcp/5432
    environments: [staging, production]

  - name: app-to-payment-gateway
    source: app-server
    destination: payment-api
    service: https
    environments: [production]

Step 2: The Connectivity Phase in the Pipeline

Once the requirements file exists, a dedicated connectivity phase is inserted into the CI/CD pipeline—typically between infrastructure provisioning and application deployment. This phase is handled by a Network Security Policy Management (NSPM) solution that reads the connectivity requirements file and takes one of three actions:

1. No change required: If connectivity requirements have not changed since the last run, the NSPM solution verifies that all required flows are currently active and reachable. It returns a pass/fail signal to the pipeline, giving developers confidence that connectivity-related failures will not occur in production.
2. Compliant new flow required: If a new or modified flow is needed and it complies with the organization’s pre-approved security policy, industry regulations, and security best practices, the NSPM solution automatically designs and pushes the required firewall rule changes to the relevant network devices—without any manual intervention.
3. Non-compliant change required: If a requested flow does not comply with policy, the change is automatically escalated for human approval. Once approved, it is implemented automatically. The developer does not need to open a separate out-of-band change request.

Step 3: Ongoing Lifecycle Management

Because the connectivity requirements file lives in source control alongside the application code, it evolves with the application. When a new version of the application introduces new dependencies or removes old ones, the developer updates the file, and the connectivity phase on the next pipeline run handles provisioning and de-provisioning automatically. This means the connectivity state of the application is always documented, current, and reproducible—a significant improvement over firewall rules that accumulate over years with no clear ownership.

Benefits of Connectivity as Code

Faster, More Predictable Delivery

By automating the connectivity provisioning step, organizations remove one of the most common causes of deployment delay. Routine, policy-compliant connectivity changes no longer require waiting on a separate team. The pipeline moves forward automatically, and developers get clear, immediate feedback if a connectivity issue is anticipated before it reaches production.

Continuous Compliance and Auditability

Every connectivity change made through the pipeline is automatically checked against security policy and industry regulations before implementation. All changes are logged with full context—which application triggered the change, which pipeline run, which version of the connectivity requirements file. This creates a complete, timestamped audit trail that satisfies compliance requirements without any additional documentation effort from either the development or security team.

Business Continuity During Infrastructure Changes

Because connectivity requirements are explicitly documented and version-controlled, they can be used to restore or verify application connectivity after network architecture changes, cloud migrations, or server replacements. Instead of relying on institutional knowledge about which firewall rules support which applications, operations teams can re-run the connectivity phase against the current requirements file to ensure the application’s connectivity is intact.

Reduced Friction Between Development and Security

Perhaps most importantly, connectivity as code shifts the relationship between developers and network security teams. Developers no longer need to understand network topology to get their applications deployed, and security teams no longer receive context-free change requests that require back-and-forth clarification. The abstraction layer handles translation automatically, and the NSPM solution enforces policy consistently—giving security teams confidence that the pipeline is not bypassing their controls.

Where Connectivity as Code Fits in the Pipeline

A typical pipeline integration looks like this:

1. Code commit triggers the pipeline.
2. Build and unit test phase runs as normal.
3. Infrastructure provisioning (Terraform, Ansible, CloudFormation, etc.) creates or updates compute and storage resources.
4. Connectivity phase reads the connectivity requirements file, validates or provisions network flows, and returns pass/fail to the pipeline.
5. Application deployment proceeds only if the connectivity phase passes.
6. Integration and smoke tests validate end-to-end behavior, including connectivity.

This positioning ensures that connectivity is always validated before the application is deployed, eliminating the class of production failures caused by missing or misconfigured firewall rules.

Conclusion

Connectivity as code applies the same principles that have made infrastructure as code successful—declarative definitions, version control, automation, and auditability—to the domain of network security policy. By treating connectivity requirements as code rather than as manual change requests, organizations can eliminate one of the last significant manual bottlenecks in the CI/CD pipeline without weakening security controls or compliance posture.

The result is a genuine win for both sides of a historically tense relationship: development teams get the speed and autonomy they need, and security teams get consistent policy enforcement, full auditability, and confidence that risk checks are built into the delivery process rather than bypassed by it.

The Headline Finding: Containers Are Moving Faster Than DevOps

A survey of 601 IT decision-makers, conducted by ClearPath Strategies on behalf of the Cloud Foundry Foundation, revealed a notable shift in enterprise technology adoption priorities: container adoption is on pace to overtake DevOps adoption within two years.

At the time of the survey, 32 percent of respondents had already adopted both containers and DevOps processes. Looking forward 12 months, 25 percent planned to adopt or evaluate containers, compared to only 17 percent planning the same for DevOps. Projected out to a two-year horizon, 72 percent of respondents said they already use or expect to use containers, versus 66 percent who said the same about DevOps.

What remains less clear from the data is whether widespread container adoption will push organizations toward DevOps as a cultural and operational practice — or whether the two trends will continue to evolve independently.

Where Container Adoption Actually Stands

The survey breaks container adoption into three tiers:

• 18 percent have broadly adopted containers across their organization.
• 40 percent have deployed containers on a limited basis.
• 40 percent are still in the early stages of evaluation or pilot.

This distribution is a useful reminder that headline adoption figures often mask significant variation in deployment maturity. An organization running containers in a single team’s CI pipeline is counted alongside one running Kubernetes clusters in production at scale.

Platform Pluralism: PaaS, Containers, and Serverless Together

One of the more telling findings is that IT organizations are no longer picking a single application delivery model. Instead, many are running multiple paradigms in parallel:

• 64 percent report using both PaaS and containers.
• 43 percent report using both PaaS and serverless.
• 42 percent report using both containers and serverless.
• 39 percent report using all three: PaaS, containers, and serverless simultaneously.

For engineering teams, this platform pluralism has real operational implications. Infrastructure and platform teams must now support toolchains, security policies, networking models, and observability pipelines that span fundamentally different runtime environments. Standardizing on a common control plane — whether that is Kubernetes, a managed cloud platform, or a PaaS layer — becomes critical as the number of runtime models grows.

Serverless: Evaluations Outpace Production Use

Serverless computing shows a striking gap between interest and production deployment. Only 11 percent of respondents were actively using serverless frameworks at the time of the survey, yet 35 percent were evaluating at least one. This mirrors the early trajectory of containers themselves — broad curiosity, limited production footprint.

For DevOps practitioners, this signals that serverless will increasingly appear in team backlogs and architecture discussions even if production usage remains concentrated in early adopters. Understanding the operational tradeoffs of serverless — cold starts, vendor lock-in, observability gaps, per-invocation cost modeling — is becoming baseline knowledge rather than specialist knowledge.

Cloud-Native Is the Direction, Not Just the Destination

CFF CTO Chip Childers, commenting on the survey results, drew a distinction that matters for how engineering leaders frame their modernization strategies: the benefits of cloud technologies are only fully realized when applications are either purpose-built for or genuinely modernized for cloud platforms. A simple lift-and-shift — moving an existing application to a VM in the cloud without architectural changes — captures very little of the operational or economic value.

The survey data supports this framing. More than half of IT decision-makers (57 percent) reported that their organizations are both building new cloud-native applications and refactoring existing ones. Only 13 percent are relying on refactoring alone.

PaaS vs. CaaS: A Division That Is Already Blurring

One question the survey raises but does not definitively answer is how the market will divide between Platform-as-a-Service (PaaS) environments and Container-as-a-Service (CaaS) environments. At the time this data was published, PaaS platforms were already absorbing container orchestration technologies — Kubernetes in particular.

Childers noted that SUSE had already released a distribution allowing Cloud Foundry to run on top of Kubernetes, and that work was underway to allow organizations to replace the CFF’s own container orchestration engine with Kubernetes. This points toward a broader industry dynamic: Kubernetes is becoming the common substrate beneath multiple higher-level platform abstractions rather than a competing alternative to them.

For engineering teams evaluating their platform strategy, the practical implication is that a bet on Kubernetes as a foundational layer does not preclude using a PaaS abstraction on top of it — and may in fact enable one.

Key Takeaways for DevOps Teams

• Container adoption breadth does not equal container adoption depth. Plan your platform engineering and support capacity for the 40 percent in early stages, not just the 18 percent who have broadly deployed.
• Expect to support multiple runtime models simultaneously. Platform teams that assume a single delivery model will cause friction as serverless and PaaS usage expands alongside containers.
• DevOps practices and container adoption are correlated but not automatically linked. Organizations adopting containers without investing in corresponding cultural and process changes risk compounding operational complexity rather than reducing it.
• Kubernetes is becoming infrastructure, not a differentiator. Evaluate which higher-level abstractions your teams need on top of it, rather than treating Kubernetes itself as the end goal.
• Serverless is worth evaluating now. With 35 percent of organizations actively evaluating serverless frameworks, waiting until it is mainstream risks falling behind on tooling and operational patterns.

Survey conducted by ClearPath Strategies on behalf of the Cloud Foundry Foundation; sample size of 601 IT decision-makers. Original reporting via containerjournal.com, published 2018.

DevOps Has Been the Standard — But Is It Enough?

For years, DevOps has been the dominant methodology guiding cloud adoption in both the private sector and government agencies. By breaking down silos between development (Dev) and operations (Ops) teams, organizations have been able to embrace agile development, continuous integration, and iterative testing at scale. Federal agencies in particular have built DevOps practices into their IT modernization playbooks, and the model has even extended to include security (DevSecOps) and acquisition professionals.

But at the General Services Administration’s Cloud Reverse Industry Day, industry stakeholders raised a pointed question: is DevOps really where the future lies, or is a newer model — NoOps — about to take center stage?

What Is NoOps?

NoOps — short for “No Operations” — is a cloud delivery model in which the cloud service provider takes on full responsibility for infrastructure management tasks that would traditionally fall to an organization’s internal IT or operations team. This includes patch management, lifecycle management, capacity scaling, and underlying infrastructure maintenance.

Dan Prieto, former White House Director of Cybersecurity Policy and strategic adviser for Google Cloud Public Sector at the time of the event, described the concept this way:

“Basically Google and other cloud providers are increasingly offering cloud [services] where you really don’t have to worry about the infrastructure or updating it or running code to automate patch management, lifecycle management or all of the above. It’s all handled in the background by a cloud service provider.”

— Dan Prieto, speaking at the GSA Cloud Reverse Industry Day

In practical terms, NoOps is built on on-demand, fully managed cloud infrastructure. Developers request the resources they need, and the platform handles provisioning, scaling, and maintenance automatically. The intended result is that engineering staff spend more time building and shipping software and less time managing the environment it runs in.

Where NoOps Delivers the Most Value

According to Prieto, NoOps is especially well-suited to certain workload categories:

• Application development: Developers can focus purely on writing and shipping code without provisioning or configuring servers.
• Databases: Managed database services handle patching, replication, failover, and backups automatically.
• Analytics: On-demand compute resources can scale up dynamically during intensive processing jobs and scale back down afterward.

“It allows an app developer to just code, and when they need more capacity, it automatically spins up. The environment has the tools to allow them to just focus on the coding.”

— Dan Prieto

The core promise of NoOps is eliminating the cognitive and operational overhead that slows development teams down — offloading routine infrastructure work to automation, machine learning tools, or the vendor itself.

NoOps Is Not a New Idea

The concept of NoOps was already being discussed in the industry as far back as 2011, when Forrester Research published a report exploring how it could augment DevOps practices rather than replace them outright. The framing at the time positioned NoOps as a natural evolution: as automation matured and cloud platforms became more capable, operations work would be increasingly abstracted away from internal teams.

What has changed since 2011 is the breadth and capability of managed cloud services. Serverless computing platforms, managed Kubernetes services, fully managed data pipelines, and AI-driven infrastructure monitoring have all brought the NoOps vision measurably closer to reality.

The Trade-offs: Vendor Dependence and Security Considerations

NoOps is not without significant trade-offs, particularly for government agencies operating under strict security and compliance requirements.

Handing infrastructure management — and potentially some security operations — to a cloud vendor deepens an organization’s dependence on that provider. Prieto acknowledged this directly, noting that the model creates greater reliance on vendor services, but argued the potential cost and productivity gains justify the shift in many cases.

For federal agencies, this raises specific questions about:

• Data sovereignty: Who controls where data is stored and processed?
• Security visibility: Can agencies maintain adequate security oversight when infrastructure is abstracted away?
• Vendor lock-in: How portable are workloads if an agency needs to switch providers or bring operations back in-house?
• Compliance: Can fully managed services be configured to meet federal security frameworks such as FedRAMP?

The Cost Management Challenge

Cloud adoption — whether DevOps, NoOps, or any point in between — fundamentally changes an organization’s cost model from capital expenditure to operational expenditure based on consumption. That shift places new demands on contracting officers, CIOs, and financial staff.

Jonathan Alboum, former CIO of the Department of Agriculture, offered a frank warning about the risk of unexpected cloud bills:

“[Data] overages are going to happen. I think the question is what can the vendor do to prevent it? And that’s a question you have to ask. This happened to me on the government side in multiple agencies. You get a bill from a vendor with an unexpected dollar amount that you have to pay because you used the service and suddenly that affects your plans across the enterprise to do other things. That’s the last thing you want as an operator of IT, a CIO or program manager.”

— Jonathan Alboum, former USDA CIO

The takeaway for procurement teams is clear: understanding cloud cost drivers must be a first-class concern at the contracting stage, not an afterthought discovered at invoice time.

Active Cloud Management: A Lesson from SBA

Maria Roat, CIO of the Small Business Administration, described how her agency managed a migration to a hybrid cloud model. Her experience reinforced a practical truth: even in a more automated cloud environment, governance and active monitoring remain essential.

“We’re not passively monitoring it, we’re actively monitoring and managing the environments that we have to be able to manage our spend. We have to watch what we are doing. Turn things off as it’s not being used. That’s the beauty of the cloud, you turn it off.”

— Maria Roat, SBA CIO

Roat’s approach also highlighted that a successful cloud operating model requires collaboration across organizational functions — specifically, between the CIO, the Chief Financial Officer, and acquisition staff — to align on data storage needs, budget flexibility, and the ability to scale resources up or down based on actual demand.

NoOps vs. DevOps: Evolution, Not Replacement

It is worth being precise about what NoOps does and does not mean. NoOps does not imply zero operational skill or oversight within an organization. Rather, it describes a model where the routine, repetitive, and automatable parts of infrastructure operations are delegated to the cloud platform, freeing internal teams to focus on higher-value work.

In this framing, NoOps and DevOps are not mutually exclusive. DevOps culture — emphasizing collaboration, automation, shared ownership, and continuous improvement — remains highly relevant even when many operational tasks are offloaded to a managed platform. The difference is that the operational surface area shrinks, and the skills required shift from server administration toward cloud architecture, cost governance, and platform engineering.

Organizations considering a move toward NoOps should evaluate:

• Which workloads are genuinely suited to fully managed, serverless, or PaaS-style deployment
• What internal operational capabilities they still need to retain for compliance, security, or resilience reasons
• How they will govern cloud costs in a consumption-based billing model
• What vendor lock-in risk is acceptable given their long-term architecture strategy

Key Takeaways

• NoOps is a cloud model in which the provider handles infrastructure management, freeing developers to focus on code rather than operations.
• It is best suited to application development, managed databases, and analytics workloads where on-demand scaling is most valuable.
• The concept has been discussed since at least 2011 and is being enabled by the maturation of managed cloud services, serverless platforms, and automation tooling.
• NoOps introduces greater vendor dependence, which raises real questions about security, compliance, and portability — especially in regulated sectors like government.
• Cloud cost management requires active governance, not passive monitoring, regardless of how automated the underlying infrastructure becomes.
• DevOps culture and NoOps infrastructure management are complementary, not competing philosophies.

Most Enterprises Are Still Not Practicing DevOps

Despite years of industry buzz, DevOps adoption among mid-to-large enterprises remains surprisingly low. A survey of more than 1,000 IT managers and directors — all from companies with at least 1,000 employees — conducted by Seattle-based managed service provider (MSP) 2nd Watch found that the vast majority of organizations are not structured for DevOps, and many are not even moving in that direction.

According to 2nd Watch EVP Jeff Aden, the data reflects a classic 80/20 split: just over 20 percent of respondents were genuinely leveraging DevOps practices. That leaves roughly 80 percent of the enterprise market operating in a way that is either incompatible with DevOps or only superficially adjacent to it.

What 2nd Watch Means by “True” DevOps

2nd Watch defines DevOps as the practice of operations and development engineers working together throughout a common service lifecycle — from application design through development, production, and ongoing support. Crucially, 2nd Watch distinguishes this cultural and organizational model from merely adopting individual tools or techniques.

In other words, using Terraform, Kubernetes, or a CI/CD pipeline does not automatically mean a team is practicing DevOps. The tooling has to be supported by integrated, cross-functional teams and shared ownership of the full application lifecycle.

Key Findings from the Survey

• 78% of respondents have separate, siloed teams for infrastructure/operations and application development — the organizational structure most antithetical to DevOps.
• ~60% are using infrastructure-as-code (IaC) tools such as Kubernetes, Terraform, or configuration management platforms — a positive signal, but not sufficient on its own to indicate DevOps maturity.
• 38% are still managing infrastructure manually, which 2nd Watch characterized as not only non-DevOps, but actively incompatible with any future DevOps adoption.
• ~75% perform some form of consistent testing when writing code — a practice that would support a DevOps pipeline if other structural elements were in place.
• 70% use some form of automated pipeline for application code deployment and management — another positive indicator for organizations looking to mature toward DevOps.

The Gap Between Tools and Culture

The survey data exposes a common misunderstanding: organizations often equate DevOps with specific tools rather than with the organizational model those tools are meant to support. You can instrument an entire CI/CD chain and still have operations and development functioning in separate silos with conflicting incentives, different on-call responsibilities, and no shared accountability for production systems.

The 38% who are still managing infrastructure manually present an even starker challenge. Without automated provisioning, repeatable infrastructure definitions, and version-controlled configuration, the foundational prerequisites for DevOps simply do not exist. These organizations face a multi-stage transformation — not just a cultural shift, but a technical one.

The MSP Opportunity — And the MSP Problem

The survey’s findings point to a significant commercial opportunity for managed service providers that can offer genuine DevOps enablement — not just cloud migration or infrastructure monitoring. As Aden explained in an interview with Channelnomics:

“It is a big opportunity for MSPs to provide a higher level of service and a value-add for clients. However, many traditional MSPs are not equipped or structured to provide these services to clients who are moving to the cloud because of old operating models and lack of skills.”

— Jeff Aden, EVP, 2nd Watch

The core tension is this: the very organizational patterns that make DevOps hard for enterprise customers — siloed subject-matter experts in networking, databases, storage, and compute — are also common inside traditional MSPs. An MSP built around ticket-based, reactive, domain-specific support is poorly positioned to offer the integrated, cross-functional services that DevOps engagements require.

What MSPs Need to Do Differently

To genuinely serve enterprises trying to adopt DevOps, MSPs need to rethink their own operating model. Based on the 2nd Watch analysis, this means:

• Cross-stack engineering capability: Engineers who understand the full technology stack — from infrastructure and networking to application deployment and observability — rather than domain-specific technicians.
• Integrated delivery teams: Internal teams that mirror the DevOps model, with shared ownership of outcomes rather than handoffs between departments.
• Automation-first service delivery: Using IaC, CI/CD pipelines, and automated testing as core delivery mechanisms, not optional add-ons.
• Advisory and enablement services: Moving beyond operational support to help customers build internal DevOps capability — training, process design, toolchain selection, and platform engineering.

Positive Signals to Build On

While the headline numbers are sobering, the survey does reveal meaningful foundations. The fact that 75% of organizations conduct consistent code testing and 70% use some form of automated deployment pipeline suggests that many enterprises already have the technical building blocks in place. What they often lack is the organizational restructuring, cultural alignment, and institutional knowledge to connect those tools into a genuine DevOps practice.

That gap — between having tools and knowing how to operate as a DevOps organization — is precisely where a well-positioned MSP can deliver lasting value. The 30% without any automated deployment pipeline will need more foundational investment, but the majority are reachable with targeted enablement.

Bottom Line

DevOps adoption in the enterprise remains substantially lower than the industry conversation might suggest. 2nd Watch’s research paints a picture of a market where tooling investment has outpaced organizational transformation, and where most companies lack the integrated team structures that define genuine DevOps. For MSPs willing to retool their own delivery models, this represents a high-value service opportunity — but only if they can credibly offer what their customers are missing: cross-functional expertise, automation maturity, and hands-on enablement at scale.

Continuous integration (CI) and continuous delivery (CD) often force this realization. These practices serve as the foundation for a mature DevOps approach, but they only deliver their full value when paired with a robust, automated testing strategy. What many IT leaders underestimate, however, is that adopting continuous testing requires significant cultural and process changes — not just new tooling.

Testing Must Happen at Every Stage of Development

The most important principle of continuous testing is straightforward: automated tests must run throughout the development process, not only after a feature is complete. There is real, measurable value in testing as you build rather than after the fact.

When a DevOps team tests code at each stage of the pipeline, issues surface while the relevant changes are still fresh in the developer’s mind. This tight feedback loop does two things simultaneously:

• Increases release velocity — defects caught early are cheaper and faster to fix than those discovered late in the cycle or, worse, in production.
• Improves software quality — incremental validation means problems never accumulate into a large, hard-to-diagnose backlog.

From a business perspective, continuous testing translates directly into faster delivery of high-quality features, which drives customer satisfaction and competitive advantage.

What Continuous Testing Actually Looks Like

A common misconception is that continuous testing means running every test against every change, around the clock. In practice, effective continuous testing means having the right test at the right stage of the pipeline — optimising for both speed and thoroughness.

Early-Stage: Smoke Tests

Early in the pipeline — typically triggered on every commit or pull request — teams should run a smaller, focused set of tests commonly called smoke tests. These tests serve several purposes:

• Catch major bugs and regressions before they propagate downstream.
• Verify that core functionality works as expected across a representative set of platforms.
• Execute quickly so they do not disrupt developer productivity or slow the CI loop.

For mobile application development, a practical best practice is to run smoke tests against iOS simulators and Android emulators rather than physical devices. This approach speeds execution time and reduces infrastructure cost while still providing meaningful early feedback.

Later-Stage: Full Regression Tests

Later in the pipeline — for example, before a release candidate is promoted to a staging or production environment — teams should run a comprehensive regression test suite. These tests are run less frequently than smoke tests and are designed to:

• Provide full regression coverage across the application’s feature set.
• Validate compatibility across a wide range of browsers, operating system versions, and real physical devices (for mobile).
• Ensure the end-user experience meets quality standards before release.

This two-tier approach — lightweight smoke tests early, comprehensive regression tests later — lets organisations move fast without sacrificing quality, and without incurring the cost of running a full test suite on every single commit.

Development Culture Must Evolve

Tooling changes alone will not make continuous testing succeed. The biggest obstacle is cultural. Many developers operate under the assumption that testing is the exclusive responsibility of a separate QA team. This silo mentality directly undermines any attempt to shift testing left and integrate it into the development workflow.

Breaking down this barrier requires a team-wide commitment to quality. In practice, that means:

• Developers learn to write testable code. Simple practices — such as consistently assigning unique identifiers to UI elements and objects — make test authoring and maintenance significantly easier for automation frameworks.
• Teams adopt test-first methodologies. Approaches like Test-Driven Development (TDD) and Behavior-Driven Development (BDD) require teams to write tests before writing application code. This fundamentally changes how requirements are understood and how code is designed.
• Ownership of quality is shared. In mature DevOps organisations, developers often write all or most of the automated tests, rather than delegating that responsibility entirely to QA engineers.

None of these changes happen overnight. They require investment in training, process redesign, and sustained leadership support. But teams that make the shift consistently report faster release cycles and fewer production incidents.

Looking Forward

The benefits of continuous testing — reduced defect rates, faster feedback, higher release velocity, and improved customer satisfaction — are well established in the DevOps community. However, realising those benefits demands that both developers and QA professionals fundamentally rethink their roles and their relationship to automated testing.

This transformation is neither quick nor easy. It requires dismantling long-standing organisational silos, adopting new development methodologies, and investing in automation infrastructure. But organisations that commit to it gain a compounding advantage: every improvement to the testing pipeline makes the next release faster and safer than the last.

For teams just beginning this journey, the most important first step is simply to start — pick one stage of your pipeline, automate one set of tests, and build from there. Continuous testing, like DevOps itself, is an iterative practice.

This article is based on concepts originally published on TechTarget.com. The image above is attributed to GrowingAgile.

The warning signs are often unmistakable. “Easy to spot warning signs include the team being constantly stressed about deployments, bugs only appearing in production, the team being scared to change code because of how critical it is, and finger pointing when collaboration breaks down,” said Brian White, senior solutions architect at Small Footprint.

“If you start to notice that your system, processes or workflow on technical teams seems to be increasing in complexity, this is an early warning sign that your DevOps initiatives are failing,” added Collin Meadows, VP of engineering and technology at Terbium Labs. “If engineers are having more trouble deploying code, making updates, spinning up servers or any other engineering task due to your DevOps practices, then it is time to revisit what those practices were put in place for and how to change them to make things less complex.”

When those signals appear, the answer is not to abandon the initiative — it is to diagnose and fix it systematically. The following five steps provide a practical framework for doing exactly that.

Step 1: Get to the Heart of the Problem

Most faltering DevOps initiatives have one or more deeply rooted problems, typically tied to a specific technology gap, a missing skill set, or a misaligned team structure. Before reaching for a new tool or reshuffling personnel, set aside dedicated time to evaluate what is not working and why.

Ensure that every stakeholder shares the same vision, goals, and success criteria. “Find people that can work alongside the team, helping and at the same time training the team to be self-sufficient,” White advised.

Scope is another common culprit. Teams can feel overwhelmed when a DevOps rollout is treated as a single, all-encompassing project. “It’s important for IT leaders not to approach application development as monolithic projects. Rather they should be broken down into smaller microservices that can be maintained by a single DevOps team,” observed Jasper van der Hoek, enterprise architect at Mendix. Breaking work into smaller, independently deployable units reduces risk and makes progress visible faster.

Step 2: Resolve Internal Dissension

Some DevOps initiatives fail not because of technical shortcomings but because the organization cannot achieve internal alignment. “People hate change, whether it’s due to fear, uncertainty, doubt, inherency or accretion,” noted Robert Reeves, co-founder and CTO of Datical. When change is forced on teams rather than developed collaboratively, it breeds resistance instead of momentum.

Addressing dissension requires a deliberate focus on culture. Van der Hoek recommended working closely with each team to enable them across all their initiatives. “This will allow them to own the processes and products that they work on, making them more willing to focus on both the development and operations side of the process.” Ownership converts reluctant participants into invested contributors.

Practical actions that help build alignment include:

• Holding open retrospectives where team members can voice concerns without blame.
• Giving teams agency over how new practices are adopted, not just whether they are adopted.
• Celebrating small wins publicly to demonstrate that the initiative is making real progress.

Step 3: Check the Metrics

You cannot improve what you do not measure. Track and visualize every part of the initiative that is capable of producing a meaningful signal — deployment frequency, lead time for changes, mean time to recovery (MTTR), and change failure rate are well-established starting points.

“This will allow you to see which part of the DevOps effort is deviating; then take extra precautions to help nudge it back on track,” explained Davy Hua, head of DevOps at ShiftLeft. He cautioned that throwing more resources at a failing initiative without reliable objectives and key results (OKRs) — and a clear understanding of what is failing and why — is a common and costly mistake.

When reviewing metrics, look for divergence between leading indicators (pipeline pass rates, test coverage trends) and lagging indicators (incident frequency, customer-reported defects). A gap between the two often pinpoints where the breakdown is occurring.

Step 4: Restate the Initiative

When momentum stalls, teams lose sight of why the transformation matters in the first place. Clearly restating the purpose of the initiative — with concrete business outcomes, not just technical goals — can re-energize a demoralized team.

“Everyone needs to have a clear line of sight to ‘why’ DevOps will make the company better and how,” observed Rashad Neloms, VP of technology and strategy at Forty8Fifty Labs. “Get people to invest back into that first, then they can begin to see how to contribute to the success and point out where to gain more traction both process- and business-wise.”

A restatement session works best when it is cross-functional. Bring together engineering, operations, product management, and business stakeholders so that the “why” reflects organizational priorities, not just the preferences of a single department. Connecting DevOps outcomes — faster releases, fewer incidents, reduced toil — to business metrics like revenue velocity or customer satisfaction makes the case concrete.

Step 5: Assert Leadership

Lack of effective leadership is one of the most frequently cited reasons DevOps transformations underdeliver. “When DevOps initiatives fail to live up to expectations, it’s typically because business and technology leadership doesn’t have the trust in their teams to deliver quality software and allow automation to do its job,” noted Ed Price, director of compliance at Devbridge Group.

Effective DevOps leadership is cross-functional by nature. “You need a leader who is able to work with people cross-functionally,” added Antony Edwards, CTO of Eggplant. Defining clear, agreed-upon objectives is equally critical. “If there is no clear, agreed goal, then you’ll never get people to agree what to do.”

Importantly, leadership cannot be confined to the top of the organizational chart. “There should be champions in each team that will push the effort along some of the most difficult and painful transformational periods,” ShiftLeft’s Hua explained. Embedding advocates at the team level ensures that the initiative has energy and accountability at every layer of the organization, not just during executive presentations.

Putting It All Together

A DevOps initiative that is struggling is not necessarily a failed one. The five steps above — diagnosing root causes, resolving internal conflict, measuring what matters, restating the purpose, and reinforcing leadership at every level — form a repeatable recovery playbook. None of them require a complete restart; most can be applied incrementally while the team continues to deliver.

The underlying principle connecting all five steps is the same: DevOps is as much a cultural and organizational discipline as it is a technical one. Tooling improvements mean little if the team is misaligned, unmeasured, or unsupported. Address the human and process dimensions first, and the technical improvements will stick.

Original source: informationweek.com (June 2018). Quotes attributed to practitioners in the original article have been preserved as cited.

The Digital Transformation Gap

Many organizations are deep into “digital transformation” programs—virtualizing infrastructure, migrating to cloud, tightening security, and enabling workforce mobility. Throw in AI and machine learning, and the roadmap looks impressive on paper. But a persistent problem undermines even the best-intentioned initiatives: fragmentation.

The headline projects attract investment and attention. What gets overlooked are the hard-to-modernize applications, entangled services, aging databases, and what can only be called legacy cloud—cloud deployments that were cutting-edge five years ago but are now rigid, under-automated, and difficult to extend. These components are the real blockers to becoming a genuinely digital organization.

Where DevOps Fits Into the Picture

DevOps has moved well past early-adopter status. Adoption is broad across enterprise organizations, and analyst coverage from firms such as Forrester and Gartner confirms that DevOps investment is now mainstream rather than experimental. The business case is clear: faster delivery cycles, reduced deployment risk, and better collaboration between development and operations teams.

Yet adoption does not automatically translate into results. George Spafford, research director at Gartner, captured the core difficulty well:

“DevOps challenges conventional IT thinking with its lack of a standard definition and approach, its constant evolution, and its requirement for acceptance and management of risk. This imprecise target state has caused many IT organizations to hesitate in implementing a DevOps strategy.”

George Spafford, Research Director, Gartner

In other words, the biggest obstacle to DevOps value is often not tooling or budget—it is uncertainty about where and how to begin. The sections below ground DevOps in three concrete scenarios where it delivers the most leverage for organizations on a digital journey.

Scenario 1: Modernizing Legacy Applications Without Rip-and-Replace

Almost every organization carries at least one legacy application that still functions but resists integration with modern services. The application may be custom-built, undocumented, or dependent on infrastructure that cannot easily be changed. The instinctive responses—scrap it entirely or buy a proprietary replacement—are expensive and disruptive.

A mature DevOps approach offers a third path: application abstraction. Rather than replacing the application wholesale, a DevOps architect will decompose it into its functional layers—code logic, backend services, data interactions, database dependencies, and user-facing components. Custom APIs can then be built around the application to extract and expose data, feeding it into new development pipelines or modern services without requiring the core application to change on day one.

This is structurally similar to how experienced teams handle virtualization migrations:

• Stage: Build the new environment in parallel with the existing one.
• Migrate incrementally: Move services, workloads, or data in controlled batches.
• Test with real users: Validate functionality before cutting over fully.
• Iterate: Repeat until the legacy dependency is reduced or eliminated.

The result is a non-disruptive, iterative upgrade path. The business keeps running on the existing system while a modern replacement is built and validated alongside it. Teams avoid the compounded risk of a single large cutover.

Scenario 2: Adopting Serverless and Modern Compute Models

One of the most practical tools in the DevOps toolkit for becoming a digital entity is serverless compute. Rather than provisioning and managing servers, developers define event triggers and write the code that should execute when those events fire. The underlying platform handles resource allocation, scaling, and teardown automatically.

Major cloud providers—AWS (Lambda), Microsoft Azure (Azure Functions), and Google Cloud Platform (Cloud Functions)—offer mature serverless platforms. The key advantages for digital transformation work include:

• Granular resource consumption: Teams pay only for the compute actually used during execution, not for idle capacity.
• Rapid iteration: Serverless functions are small, independently deployable units that align naturally with microservices architecture.
• Reduced operational overhead: Infrastructure management shifts to the cloud provider, freeing engineering time for application logic.
• Agility at scale: Event-driven architectures can absorb variable workloads without manual intervention.

Think of serverless as utility-model consumption for cloud resources. You use exactly what the task requires, when it requires it—an efficient foundation for building agile, digital-first capabilities without the overhead of traditional infrastructure management.

Scenario 3: Aligning DevOps With Business Outcomes

Technology improvements mean little if they are disconnected from business objectives. This is a common failure mode: teams adopt DevOps practices, improve pipeline velocity, and reduce deployment friction—but cannot articulate the business impact. Without that connection, DevOps initiatives are vulnerable to budget cuts and organizational skepticism.

Gartner research has indicated that a significant proportion of CEOs rank product improvement and in-house technology capability development among their highest priorities—and that more CEOs favor building internal digital capability than outsourcing it. (See the Gartner newsroom for the underlying report.) This executive-level appetite creates an opening for DevOps teams to position their work as a direct enabler of strategic goals.

Practical steps for maintaining business alignment:

• Define success in business terms before starting. Faster releases are a means, not an end. What customer outcome, revenue impact, or risk reduction does the initiative support?
• Map DevOps metrics to business KPIs. Deployment frequency, change failure rate, and mean time to recovery are valuable internally; translate them into customer experience and operational cost terms for stakeholder communication.
• Involve business stakeholders in prioritization. The teams closest to customers should influence which capabilities are built and in what order.
• Review and adjust regularly. Continuous delivery applies to strategy as well as software. Revisit alignment at regular intervals as business priorities evolve.

Continuous innovation requires continuous alignment. DevOps teams operating as isolated technology units—however technically excellent—will ultimately struggle to demonstrate value to the broader organization.

Getting Started: You Do Not Need to Be an Expert

The DevOps landscape is broad and evolving rapidly. That breadth is not a reason to delay action—it is a reason to be deliberate about where to focus first. A few practical starting points:

• Audit your most painful application dependencies. Which legacy systems create the most friction for delivering new features or integrating with modern services? These are prime candidates for abstraction work.
• Engage architects early. Application architects, cloud architects, and experienced DevOps practitioners can assess your existing environment and define a phased modernization path—without requiring a full replacement on day one.
• Start with a bounded problem. Pick one application, one service, or one workflow. Demonstrate value in a constrained scope before scaling the approach.
• Ask the right questions about serverless and microservices. Even if your team is not ready to adopt them immediately, understanding how they differ from your current compute model helps inform longer-term architecture decisions.

The Bigger Picture

Becoming a digital entity is not a project with a completion date—it is an ongoing capability that organizations build over time. The fact that a legacy application still runs does not mean it is delivering value. If engineering teams are afraid to change the application, that fear itself is a business risk: it signals fragility, technical debt, and competitive vulnerability.

DevOps, applied thoughtfully and aligned with real business goals, provides the model and the practices to move forward without the disruption of wholesale replacement. The combination of application abstraction, modern compute models like serverless, and strong business alignment gives organizations a credible path from where they are today to where they need to be as digital entities.