Before we start let’s talk about what is SRE. SRE is the one who works with the software engineers with the collaboration between developers and operation teams.

SRE is a practice that combines both software development skills and mindset to IT operations. The main focus of SRE is to enhance the reliability and performance of applications, with automation and continuous integration and delivery.

SRE has changed the traditional way of working as an operation team by incorporating new technologies, ideas, and methods. SRE was originated at Google.

After originated more than 1000 SRE engineers employed at Google. Even though after spread about SRE, other software companies also started adopting SRE engineers gradually. As per the 2021 report, 22% of organizations in a survey of 2,000 respondents have adopted the SRE model.

SRE accepts failure because they know failure is a part of success and not a single system is perfect. SRE and DevOps both work to bridge the gap between development and operations teams to deliver faster services. SRE has removed the “silos” between development and operation teams to function effectively.

SRE also works with the DevOps team to help them in operational works to give reliable service to the customer.

SRE Skillsets

SRE is a role that requires a software developer with operational experience or as a system admin or engineer for an IT operations role that has software development skills as well.

SRE needs some skillsets to have are-

Deep knowledge of version control.

Sound knowledge of operating Systems (like LINUX).

Should be aware of DevOps concepts and best practices.

CI/CD implementation expertise.

Issue troubleshooting experience.

Communication & Collaboration skills.

Good knowledge of Cloud-native applications.

Good understanding of “Distributed computing”

Should know “coding”

Even though with these skills they should know tools sets as well which will be used by the SRE team.

Some toolsets are –

S.no       Problems                                        Tools                           

1              Operating Systems              –               Centos/Ubuntu & VirtualBox & Vagrant

2              Cloud                                   –                 AWS

3              Containers                           –                Docker & Kubernetes – Helm

4              Planning and Designing –             –                Jira & Confluence

5              Source Code Versioning                –            –                   Git using Github

6              Webserver                          –                    Apache HTTP & Nginx

7              Configuration & Deployment Management            –            Ansible

8              Infrastructure Coding                            –           Terraform

9              Services mesh Data planes & Control Planes            –      Envoy & Istio

10           Network configurations and Service Discovery         –        Consul

11           Continuous Integration                  –                   Jenkins

12           Securing credentials                       –          HashiCorp Vault & SSL & Certificates

13           Infrastructure Monitoring                   –            Datadog, Prometheus with Grafana

14           Log Monitoring                  –                   Splunk & ELK stake

15           Performance & RUM Monitoring                   –                 NewRelic

16    Emergency Response & Alerting & Chat & Notification – SMTP, SES, SNS, Pagerduty & Slack    

SRE Roles and responsibilities. 

SRE teams are responsible for how code is deployed, configured, and monitored, as well as the availability, latency, change management, emergency response, and management capacity of services in production.

SRE helps teams to determine what new features can be incorporated and when by using service-level agreements (SLAs) to define the required reliability of the system through service-level indicators (SLI) and service-level objectives (SLO).

SRE takes back the collected data to the development team to fix the issue (means the vulnerabilities which shouldn’t be there while development) and not make it again next time.

Monitoring and logging are keys to SRE roles. In monitoring, they are keeping track of what’s happening in real-time. Logging is an archive of what has happened, so you can examine it later.

Monitoring will give you the ability to anticipate failures and see them coming, so you can proactively solve them.

Logging is like, when you get an unanticipated failure, it makes you get back and see what happened. So you can do RCA (Root cause analysis) and find out how to solve it, not for now but for the future.

So as an SRE expert you are responsible to monitor the product and fixing the issues, bugs, blot wares, etc by the use of automation to eliminate some extent of manual work. 

Automation is an essential part of the SRE. With automation, we can reduce the manual work even though fix the issue on which we have to work again n again. This also helps to reduce the toil.

Maintaining the balance between operations and development work is also one of the roles of SRE.

Training Place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, and SRE courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

Key Survey Findings at a Glance

• Roughly 50% of U.S. organizations surveyed had either completed or were close to completing their digital transformation journeys.
• Nearly two-thirds of U.S. respondents reported adopting DevOps practices—consistent with the transformation progress seen in the same cohort.
• 79% said the wider business holds higher expectations for how digital systems perform.
• 72% said their technology teams are expected to deliver more innovations and updates than ever before.
• 89% believe artificial intelligence and machine learning will become important tools for managing digital systems.

The Observability Gap: Too Much Data, Too Little Insight

One of the central themes to emerge from the survey is what might be called an observability gap—the disconnect between the volume of data DevOps teams collect and their ability to act on it meaningfully.

Buddy Brewer, then Group Vice President and General Manager for Client-Side Analytics at New Relic, pointed out that most DevOps teams have access to a wide range of metrics but that those metrics typically describe averages—how many times an event occurs across the entire user population. The problem, he noted, is that averages obscure the experience of the worst-affected users, who are often a company’s most important customers.

The survey data backs this up:

• More than 50% of respondents said their complex new software and infrastructure is difficult to manage and monitor for performance issues.
• 56% acknowledged that it is impossible to properly assess all the machine data being collected.
• 48% admitted that end users or customers are likely to report a problem before the internal team is aware of it.
• 46% said they are notified of issues before they know how to fix them.

Together, these figures paint a picture of teams that are data-rich but insight-poor—a significant liability when business stakeholders expect near-real-time visibility into system health.

Business Pressure Is Intensifying

The survey also documented the organisational pressure bearing down on engineering and operations teams as transformation initiatives accelerate:

• 63% said they work longer hours to observe and manage software performance because of pressure to respond to business needs.
• 46% reported that C-suite executives want daily updates on how software systems are performing.
• 40% said CEOs demand more answers when outages or performance problems occur.
• A third of respondents said they are challenged to demonstrate business benefit metrics for their digital transformation projects.

These findings highlight a communication gap between engineering teams and executive leadership—one that monitoring and observability tooling alone cannot fully close without investment in dashboards, SLO reporting, and structured incident retrospectives that non-technical leaders can understand.

Top Five Digital Transformation Challenges

The survey identified five leading obstacles organisations face when pursuing digital transformation:

1. Uneven pace of change — Separate parts of the organisation move at different speeds to embrace digital transformation, creating internal friction and integration bottlenecks.
2. Skills shortages — A lack of employees with the technical skills needed to execute transformation at scale remains a persistent constraint.
3. Budget restrictions — Limited funding slows modernisation efforts, particularly for infrastructure and tooling upgrades.
4. Measuring business benefits — Organisations struggle to define and quantify the ROI of transformation projects in terms that resonate with business leadership.
5. Legacy system inertia — Continued resistance to decommissioning legacy systems extends technical debt and complicates migration to modern architectures.

What This Means for DevOps Teams

The survey’s central takeaway is that DevOps adoption and digital transformation tend to travel together—but adoption alone does not guarantee smooth execution. The data suggests three practical areas where DevOps teams can improve outcomes:

• Shift from average-based to percentile-based monitoring. Tracking p95 and p99 latency and error rates surfaces problems that affect a small but critical segment of users—often before those users escalate to support channels.
• Invest in business-facing observability. Connecting technical metrics to business KPIs (conversion rates, revenue impact, SLA breaches) makes it easier to justify budgets and satisfy C-suite reporting requirements.
• Build cross-functional transformation alignment. The finding that different parts of the organisation move at different speeds underscores the need for platform teams to provide self-service tooling that enables product and application teams to move faster without creating new reliability risks.

Looking Ahead: AI and ML in DevOps

The survey’s most optimistic data point is that 89% of respondents believe AI and machine learning will become important elements for managing digital systems. AIOps—using machine learning to correlate events, detect anomalies, and reduce alert noise—is an active area of development across observability platforms. However, as of this survey’s publication, most organisations were still in early stages of applying these capabilities at scale.

Until AI-driven operations mature, DevOps teams should expect that each new digital transformation project will bring a corresponding increase in operational complexity, monitoring surface area, and on-call burden. Planning for that growth explicitly—through runbooks, automated remediation, and clearly defined SLOs—remains the most reliable path forward.

Source: Original reporting via devops.com, based on a New Relic/Vanson Bourne survey presented at FutureStack (2019). Survey methodology: 750 senior IT decision-makers, enterprises with 500–5,000+ employees, across Australia, France, Germany, UK, and the US.

Why Enterprises Adopt Kubernetes

Released by Google in 2014 and donated to the Cloud Native Computing Foundation (CNCF), Kubernetes is an open-source container orchestration platform designed to automate deployment, scaling, and operation of containerized applications across clusters of hosts. It provides a vendor-agnostic, Platform-as-a-Service (PaaS)-style abstraction that works across public clouds, private data centers, and hybrid environments.

At its core, Kubernetes solves a common operational problem: when you are running many containers across many hosts, you need a consistent way to schedule workloads, manage networking, handle failures, and scale capacity. Kubernetes provides that common interface.

Key Out-of-the-Box Capabilities

• Automated and manual scaling — Horizontal Pod Autoscaler adjusts replica counts based on CPU or custom metrics; manual scaling is also supported.
• Self-healing — Kubernetes automatically restarts failed containers, reschedules pods from failed nodes, and kills containers that do not respond to health checks.
• Workload management — Supports Deployments, StatefulSets, DaemonSets, Jobs, and CronJobs for a wide range of workload patterns.
• Rolling updates and rollbacks — Progressive application deployment with configurable update strategies and the ability to roll back to a previous revision.
• Service discovery and DNS — Built-in DNS-based service discovery for pods and services within a cluster.
• Secret and configuration management — Kubernetes Secrets and ConfigMaps decouple configuration from container images.

Business Reasons to Choose Kubernetes

Large, Active Community

Kubernetes has one of the largest open-source communities in the cloud-native ecosystem. It is hosted by the CNCF and backed by major cloud providers including AWS, Google Cloud, and Microsoft Azure, all of whom contribute code and publish best practices. This breadth of support reduces the risk of vendor lock-in and ensures a steady stream of improvements and security patches.

Hybrid and Multi-Cloud Flexibility

Kubernetes runs consistently whether your infrastructure is on-premises, in a public cloud, or spread across both. This makes it well-suited for organizations that need to migrate workloads, avoid single-cloud dependency, or operate in regulated environments that require on-premises data residency.

Accelerating the DevOps Workflow

Kubernetes eliminates significant amounts of manual scripting around deployment and scaling workflows. By defining desired state declaratively in YAML manifests, teams can version-control their infrastructure alongside application code and promote the same artifact through development, staging, and production pipelines with confidence. This alignment between development and operations teams is central to the DevOps model.

A widely cited real-world example is Pokémon GO, which ran on Google Cloud with Kubernetes as the orchestration layer. At launch, the game experienced traffic far beyond initial projections, and Kubernetes allowed the platform team to scale infrastructure to meet demand without a full redesign of the deployment architecture.

Key Criteria for Evaluating an Enterprise Kubernetes Management Solution

While it is possible to install and operate Kubernetes from scratch, most enterprises benefit from a managed platform or a commercial distribution that adds the operational features Kubernetes does not include out of the box. Use the following criteria when evaluating your options.

1. Production Readiness

A production-ready platform should automate day-2 operations — backup, restore, certificate rotation, etcd management, and cluster upgrades — so that your team does not need to build and maintain those workflows internally. Look for platforms that have opinionated, tested upgrade paths and documented disaster-recovery procedures.

2. High Availability

A standard Kubernetes installation does not automatically configure a highly available control plane. An enterprise platform should provide multi-master control plane setups, etcd clustering, and automated node repair to eliminate single points of failure. Verify that the platform supports HA across multiple availability zones if you are running in a public cloud.

3. Load Balancing

Kubernetes Services of type LoadBalancer rely on the underlying cloud provider to provision an external load balancer. In on-premises environments this integration does not exist by default, so you need a platform that either bundles a software load balancer (such as MetalLB) or integrates with your existing network infrastructure. Confirm that the solution handles both ingress traffic and east-west (service-to-service) load balancing at the scale your workloads require.

4. Observability and Intelligent Monitoring

Managing a Kubernetes cluster generates large volumes of metrics, logs, and events. A good management platform should provide integrated monitoring dashboards, alerting, and log aggregation. Early detection of resource exhaustion, node pressure, or misconfigured workloads is essential to prevent outages before they affect end users.

5. Multi-Cluster and Multi-Cloud Management

Organizations often run multiple Kubernetes clusters — separated by environment, region, or business unit. An enterprise platform should provide a single control plane or dashboard from which operators can manage multiple clusters, enforce consistent policies, and federate workloads across clouds and on-premises data centers.

6. Enterprise-Grade User Interface

The default Kubernetes Dashboard is minimal and not suitable for all enterprise use cases. A commercial or managed platform typically provides a richer UI that exposes cluster health, resource utilization, RBAC management, and workload deployment in a format accessible to both operators and developers. A well-designed UI reduces the time to diagnose problems and lowers the barrier for teams new to Kubernetes.

7. Security and Compliance

Enterprise environments typically require role-based access control (RBAC), network policy enforcement, image scanning, audit logging, and secrets management that integrates with existing identity providers (LDAP, Active Directory, SAML/OIDC). Evaluate whether the platform provides these controls out of the box or requires significant custom integration work.

Overview of Leading Managed Kubernetes Platforms

The following platforms are among the most widely adopted enterprise Kubernetes management solutions. Feature sets and pricing evolve rapidly; always consult current vendor documentation before making a procurement decision.

Google Kubernetes Engine (GKE)

GKE is Google Cloud’s fully managed Kubernetes service and one of the most mature offerings in the market, given that Google developed the original Kubernetes project. GKE handles control plane management, automated upgrades, and node auto-repair. It supports Autopilot mode (where Google manages node infrastructure entirely) and Standard mode (where operators retain node-level control). GKE integrates natively with Google Cloud’s networking, storage, identity, and observability services.

Amazon Elastic Kubernetes Service (EKS)

Amazon EKS runs a managed Kubernetes control plane spread across multiple AWS availability zones, providing high availability without operator intervention. EKS integrates with AWS IAM for authentication, AWS Load Balancer Controller for ingress, Amazon EBS and EFS for persistent storage, and CloudWatch for logging and metrics. It is a natural choice for organizations already invested in the AWS ecosystem.

Azure Kubernetes Service (AKS)

Microsoft’s managed Kubernetes offering integrates with Azure Active Directory, Azure Monitor, Azure Container Registry, and Azure Policy. AKS provides a free managed control plane (you pay only for worker nodes) and supports both Linux and Windows node pools, making it relevant for organizations running mixed workloads.

Red Hat OpenShift

OpenShift is Red Hat’s enterprise Kubernetes platform and is available as a self-managed distribution (OpenShift Container Platform) or as a fully managed cloud service (OpenShift Dedicated, ROSA on AWS, ARO on Azure). OpenShift adds an opinionated developer experience on top of Kubernetes, including an integrated image registry, CI/CD pipelines via Tekton, and a web console designed for both developers and cluster administrators. It supports both cloud-native and traditional applications on the same cluster. OpenShift Origin (now called OKD) is the community upstream distribution.

Platform9 Managed Kubernetes

Platform9 offers a SaaS-managed Kubernetes control plane that can run on bare metal, VMware, OpenStack, or public clouds, making it one of the more flexible options for organizations with heterogeneous infrastructure. The management plane is hosted by Platform9, while workloads run on the customer’s own infrastructure. It includes a dashboard for cluster and service management and supports multi-cluster operations.

How to Choose the Right Solution for Your Organization

No single platform is the right answer for every organization. Use the following questions to guide your evaluation:

• Where does your infrastructure live? — If you are all-in on a single cloud, that cloud’s native managed Kubernetes service is often the lowest-friction choice. If you need on-premises or multi-cloud support, look at OpenShift or Platform9.
• What is your team’s Kubernetes maturity? — Teams new to Kubernetes benefit from opinionated platforms (OpenShift, GKE Autopilot) that reduce operational surface area. Experienced teams may prefer more control.
• What are your compliance requirements? — Regulated industries (finance, healthcare, government) often require specific security controls, audit trails, and data residency guarantees that vary between platforms.
• What is your budget model? — Fully managed services trade operational effort for higher per-node costs. Self-managed distributions (OKD, upstream Kubernetes) reduce licensing costs but increase operational burden.
• Do you need Windows workload support? — AKS and OpenShift support Windows node pools; not all platforms do.
• How important is ecosystem integration? — If your organization is deeply integrated with AWS, GCP, or Azure services, the corresponding managed Kubernetes service will integrate most naturally.

Summary

Kubernetes has established itself as the standard for container orchestration, but running it reliably at enterprise scale requires more than a vanilla installation. The right managed platform adds the high availability, load balancing, observability, security controls, and management UI that Kubernetes does not provide out of the box, allowing your engineering teams to focus on delivering applications rather than maintaining infrastructure plumbing.

Evaluate candidates against your specific infrastructure environment, compliance requirements, team skill set, and budget. Conduct a proof-of-concept with your actual workloads before committing to a platform, and revisit your choice periodically as the ecosystem continues to evolve rapidly.

Background: Listening at DOES19

The DevOps Enterprise Summit (DOES) is one of the most respected annual gatherings for technology leaders navigating large-scale DevOps transformations. At the 2019 London event, Mark Smalley spent three days at the GamingWorks booth speaking with delegates about a single question: What is your biggest DevOps transformation challenge right now?

The answers clustered into seven recurring themes. What follows is a summary of those themes, enriched with practitioner commentary. If you recognize your own organization’s struggles here, you are far from alone.

1. Business Value

• Understanding the link between day-to-day IT work and overarching business strategy.
• Gaining end-to-end visibility and transparency across the delivery chain.
• Measuring the right things — team performance expressed in terms of business outcomes, not just technical metrics.

Commentary: Many IT professionals struggle not only to understand the business value of their work but also to articulate it clearly to non-technical executives. Bridging this communication gap requires a shared vocabulary and agreed-upon outcome metrics that resonate beyond the engineering team. Frameworks and whitepapers on the business value of DevOps — such as those produced by the DevOps Agile Skills Association (DASA) — offer useful starting points for structuring that conversation.

2. Governance

• Finding the right balance between centralized standardization and team-level autonomy.

Commentary: Governance is frequently cited but rarely well-defined. The core tension is this: standardization can drive efficiency and compliance, but applied too broadly it creates bureaucracy that slows down the very teams it is meant to support. In complex, unpredictable systems, professionals need latitude to exercise judgment. The guiding question should always be: Does this standard actually improve outcomes for the value stream, or does it simply satisfy an organizational need for uniformity? If the answer is the latter, decentralized decision-making often produces better results — even if it costs slightly more to operate.

3. Velocity

• Delivering quickly without sacrificing quality.

Commentary: Speed versus quality is a false binary. The real challenge is sustaining all five dimensions simultaneously: better, value, sooner, safer, and happier — a framing popularized by Jonathan Smart and Sonja Blignaut at Deloitte under the hashtag #BVSSH. Resilient operations, co-created customer value, and regulatory conformance must all remain in the picture as delivery cadences increase. Automating quality gates within CI/CD pipelines is one of the most effective levers teams have for maintaining this balance.

4. Value Streams

• Breaking down organizational silos.
• Improving collaboration across team and departmental boundaries.
• Granting teams greater autonomy over their work.
• Integrating DevOps practices into predominantly non-DevOps parts of the value stream, and vice versa.

Commentary: At conferences, value stream thinking is widely understood. In most IT departments, it remains an alien concept. There is a real risk that practitioners develop an echo-chamber view of how far adoption has spread. The practical implication: when beginning a transformation, meet teams where they are rather than where you wish they were. Cross-team collaboration consistently ranks as one of the hardest soft skills to develop — something confirmed by DevOps Institute (DOI) skills surveys that identify it as a top-priority competency. It may be called “soft,” but it is anything but easy.

5. Operations (Ops)

• Defining what modern ops looks like in a DevOps context.
• Transitioning from traditional, process-heavy ops models to leaner, more adaptive ones.

Commentary: The evolution of ops is one of the most active areas of change in the industry. Frameworks such as ITIL 4 have moved toward a less prescriptive, more value-oriented model — emphasizing value streams, co-creation with service consumers, and human judgment over rigid process compliance. However, ops work is fundamentally different from development work: it is often more interrupt-driven, involves higher stakes around availability, and requires different patterns of working. A healthy DevOps culture must accommodate and respect those differences rather than simply importing dev-centric practices wholesale into ops teams.

6. Organizational Change

• Sustaining momentum after the initial enthusiasm fades.
• Overcoming resistance and organizational inertia.
• Accepting the full consequences of committing to DevOps.
• Unlearning decades of entrenched working habits.
• Building the skills needed to lead and execute transformation.
• Assessing and managing the risks of transformation itself.
• Sharing practices across teams and learning from peers.
• Scaling what works beyond pilot teams.

Commentary: Organizational change is where most DevOps transformations succeed or fail. Too often there is enthusiastic talk at the executive level followed by underwhelming commitment in practice. There is no shortcut. As Taiichi Ohno, the architect of the Toyota Production System, observed: “You have to think for yourself and face your own difficulties instead of trying to borrow wisdom.” Borrowed frameworks and copied playbooks can only take you so far. Genuine transformation requires deep, context-specific problem-solving. Notably, similar patterns were observed at the DOES18 USA event, suggesting these are systemic, industry-wide challenges rather than isolated organizational issues.

7. Culture and Emotions

• Building diverse, cross-functional teams.
• Getting people with very different personalities and working styles to collaborate effectively.
• Building mutual trust across team and organizational boundaries.
• Addressing the perception that non-DevOps teams are less valued.

Commentary: Culture is finally getting the serious attention it deserves in enterprise DevOps conversations. Psychological safety — the degree to which team members feel comfortable speaking up, raising concerns, and being themselves — is a foundational enabler of high performance. Research by Amy Edmondson at Harvard Business School has made this concept mainstream, and it regularly surfaces in Phoenix Project simulation workshops as a critical factor in team effectiveness. Burnout researcher Dr. Christina Maslach offers a sharp analogy that captures the stakes: “You want less toxic mines, not more resilient canaries.” Resilience is a virtue, but it should never substitute for fixing the systemic conditions that cause harm in the first place.

8. Tooling

• Navigating tool sprawl and confusion in the DevOps ecosystem.
• Integrating disparate tools into a coherent, orchestrated toolchain.

Commentary: The DevOps tooling landscape is vast and grows more complex each year. Teams frequently struggle with two distinct problems: first, selecting the right tools from a crowded market; second, making those tools work together reliably in a coherent pipeline. Tool selection should follow strategy, not precede it. Defining what outcomes you need to achieve — faster deployments, better observability, automated security scanning — and then choosing tools to serve those outcomes is far more effective than adopting tools because they are popular or because a vendor presented them well.

Key Takeaways

The challenges reported at DOES19 London are not new, and they are not unique to any single organization or industry. The fact that the same themes — business value alignment, governance balance, collaboration, culture, and sustainable change — recur at every major DevOps event points to something important: these are deep, structural challenges that no tool, framework, or conference talk will solve on its own.

Progress requires honest self-assessment, genuine executive commitment, and the patience to build capability incrementally. Start from where your organization actually is. Measure outcomes that matter to the business. Invest as heavily in people and culture as in tooling and process. And share what you learn — the DevOps community grows stronger when practitioners are candid about what is hard, not just what worked.

Original reporting and commentary by Mark Smalley, published on devops.com. Mark Smalley is a DevOps practitioner, DASA contributor, and speaker known as “The IT Paradigmologist.”

Next week marks the kickoff of Microsoft’s annual Inspire convention in Las Vegas, where the Seattle company reliably announces a slew of enterprise product updates across its portfolio. This year, in addition to Microsoft Teams news and a new AI for Good initiative, it launched Azure Lighthouse in general availability alongside Azure Migration Program enhancements

Azure Lighthouse
In essence, Azure Lighthouse is a control panel that integrates with portals, IT service management (ITSM) tools, and monitoring tools to let service providers view and manage Azure deployments across customers. It’s powered by Azure delegated resource management, a capability that allows companies to delegate permissions to the providers in question and perform operations on their behalf over scopes, including subscriptions, resource groups, and individual resources.

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As Azure Compute corporate vice president Erin Chapple explains in a blog post, once customers assign resources to individual providers, the providers can in turn extend access to users or accounts in its tenant within the constraints specified by the aforementioned customers using Azure role-based access control mechanisms. Standard mechanisms work as if customer resources were resources in the provider’s own subscriptions and regardless of the licensing construct at play (e.g., pay-as-you-go).

“Inspired by Azure partners who continue to incorporate infrastructure-as-code and automation into their managed service practices, Azure Lighthouse introduces a new delegated resource concept that simplifies cross-tenant governance and operations,” wrote Chapple. “Partners can now manage tens of thousands of resources from thousands of distinct customers from their own Azure portal or [command line interface] context.”

Azure delegated management furthermore enables service providers to automate status monitoring, and to apply, create, update, change, and delete changes across multiple customers’ resources from a single location. Additionally, it allows both customers and service providers to see who took actions on the resources thanks to Azure’s Activity Log and the newly built resource provider, Microsoft Managed Services, which helps services determine if a call was made from a resource’s home tenant or a service provider’s tenant.

Azure Migration Program
Alongside Azure Lighthouse, Microsoft rolled out updates to its Azure Migration Program, a service designed to help enterprises move systems, apps, and data to Microsoft’s cloud platform. It comprises step-by-step guidance from experts and specialized migration partners, along with technical skill building through courses and free migration and cost assessment tools like Azure Migrate and Azure Cost Management.

Starting this week, Azure Migrate Program participants will gain access to first-party tools like Server Assessment, Server Migration, Database Migration Service, and App Service Migration Assistant, as well as utilities from Carbonite, Cloudamize, Corent, Device42, Turbonomic, and UnifyCloud (with additional integrations on the way). That’s in addition to offers to reduce migration costs, including Azure Hybrid Benefit, free Extended Security Updates for Windows Server and SQL Server 2008, and agentless migration and support for Hyper-V assessments.

“Azure Migrate delivers a unified, integrated experience across Azure and partner migration tools, so customers can identify the right tool for their migration scenario,” said Azure corporate vice president Julia White. “I couldn’t be more excited about the collective opportunity that lies ahead of us and look forward to helping our customers confidently plan and migrate to Azure.”

The following nine pillars provide a practical framework for assessing and improving your organisation’s continuous security maturity. Each pillar maps to a core element of modern DevOps practice, and together they form an end-to-end picture of what mature DevSecOps looks like in production.

1. Leadership

Security programmes succeed or stall based on executive sponsorship. Leaders who treat security as a compliance checkbox rather than an engineering concern create the cultural conditions for breaches. Effective security leadership means:

• Articulating a clear, multi-year vision for the organisation’s security direction and communicating it consistently to all teams.
• Encouraging teams to challenge existing security assumptions and ask new questions about threat models, tooling, and process.
• Recognising and rewarding individuals and teams who demonstrate above-average security practices—publicly acknowledging improvements reinforces the behaviours you want repeated.

2. Collaborative Culture

The biggest security gaps in DevOps organisations are often organisational, not technical. When security operates as a separate gate at the end of the pipeline, vulnerabilities accumulate and release velocity drops. A genuinely collaborative culture looks like:

• Cross-functional teams that share responsibility for security outcomes across development, operations, QA, and security engineering—with no silos.
• DevOps toolchain design and workflow changes reviewed by a coalition of stakeholders that explicitly includes security engineers.
• Team members trained and empowered to take personal responsibility for security, compliance, and privacy obligations—not just security specialists.
• Security engineers involved in design reviews for modular components and consulted whenever security patterns within those modules change.

3. Design for DevOps

Secure software at speed requires security to be considered at the design and pre-commit stage, not discovered post-deployment. Key practices include:

• Pre-commit static analysis (SAST): Run static analysis tools before code reaches the integration branch to catch common vulnerabilities—memory leaks, uninitialised variables, array-boundary errors, SQL injection—before they are shared with other developers.
• Software composition analysis (SCA): Scan third-party libraries and open-source components for known vulnerabilities as part of the build process.
• Security framework documentation: Document and share approved security frameworks for each technology stack (for example, Apache Shiro or Spring Security for JVM projects) so developers have clear, consistent guidance.
• Security-aware peer code reviews: Code review checklists should explicitly include defensive coding patterns and known vulnerability categories before code is merged to the trunk.
• Shared security components: Maintain a curated library of common security components—identity, authorisation, key management, audit logging, cryptography, protocol implementations—that module developers can consume rather than re-implement.

4. Continuous Integration

The CI stage is a high-value control point because every change passes through it. Integrating security checks here catches vulnerabilities early, when they are cheapest to fix.

• Use a version control system to track all changes to source code, compiled artefacts, and the tools used to build and test the software.
• Wire incremental static analysis into CI pre-commit and commit hooks. Scanning only the changed code keeps feedback fast. Control-flow and data-flow analysis at this stage can surface issues such as misuse of cryptographic functions, configuration errors, and injection vulnerabilities.
• Digitally sign binary artefacts and store them in access-controlled, audited repositories to prevent tampering.
• Trigger automatic notifications or pull requests to security engineers when changes touch sensitive security patterns—session management, authentication, authorisation, or encryption logic.

5. Continuous Testing

Security tests that are not automated are security tests that do not run consistently. Embedding security testing into the continuous testing process ensures coverage keeps pace with delivery velocity.

• Write new security tests alongside new code and commit them to the trunk at the same time, so the tests are in place before the next integration cycle.
• Automate security tests for each pipeline stage and select them according to predefined criteria relevant to the change being made.
• Include security regression tests in every release cycle. Aim for the majority of these tests to be fully automated; where manual steps are unavoidable, provide tooling to assist the tester.
• Tag test results that indicate potential security concerns so they are routed to security engineers for analysis.
• Run dynamic application security testing (DAST) or interactive application security testing (IAST) and deliver results back to developers through the tools and feedback mechanisms they already use.
• If containers are in use, regularly scan image repositories for known vulnerabilities, verify image integrity with hash checks to detect drift, and perform run-time vulnerability checks during image deployment.
• Build abuse cases and attack-pattern tests for each application module’s threat profile.
• Include boundary-condition tests, negative tests, error-handling tests, and exception-handling logic in unit and integration test suites.
• Automate coverage of the OWASP Top 10 vulnerability classes within the automated testing suite.

6. Continuous Monitoring

Dynamic infrastructure and rapid release cycles mean the security posture of a system changes constantly. Continuous monitoring provides the observability needed to detect and respond to threats in near real time.

• Automatically collect, calculate, and surface security metrics to anyone on the team who subscribes to them. Useful security metrics include:
  • Number of security defects identified pre-production
  • Percentage of code covered by security tests
  • Number of builds failed due to security policy violations
  • Mean time to detection (MTTD)
  • Mean time to resolution (MTTR)
• Consolidate vulnerability data from all tools and pipelines into a unified view so teams can understand cumulative risk and prioritise remediation effectively.
• Feed telemetry from production security controls—such as Web Application Firewalls (WAF) and Runtime Application Self-Protection (RASP)—back into security test design to close the loop between production threats and test coverage.
• Share threat intelligence and security event data across DevOps teams to enable attack-driven defence: using knowledge of real attack patterns to inform defensive improvements.

7. Continuous Security

Continuous Security is both a pillar in its own right and the thread that runs through every other pillar. It describes the automation, tooling, and operational discipline that make security a persistent property of the system rather than a periodic activity.

• Require all security platforms to expose their full functionality via APIs so they can be integrated into automated pipelines without manual intervention.
• Adopt an immutable infrastructure mindset: production systems are built from known-good images and replaced rather than patched in place, reducing configuration drift and attack surface.
• Automate security controls so they enforce policy without creating bottlenecks that slow delivery.
• Integrate security tooling directly into the CI/CD pipeline rather than running it as an out-of-band process.
• Restrict access to source code for key intellectual property on build and test machines to trusted users with auditable credentials. Build and test scripts must never contain embedded credentials to systems holding sensitive data.
• Schedule periodic external penetration tests—performed independently of the pipeline—to provide deep-dive analysis that automated tooling may miss.
• Deliver WAF and RASP telemetry from production back to development teams as a feedback loop for application hardening.
• Maintain an accurate, automated inventory of all software packages and version information via infrastructure as code. Use automated detection to identify packages with known CVEs and define specific remediation actions for each.

8. Elastic Infrastructure

Cloud-native and elastic infrastructures offer significant operational advantages, but they also expand the attack surface: more services, more API endpoints, more ephemeral instances, and more shared-responsibility boundaries to manage. Security practices must adapt accordingly.

• Include automated security checks in all infrastructure configuration code:
  • Disable unnecessary services; open only the ports required for the workload.
  • Enforce file permissions, audit policies, and logging configurations.
  • Verify that development tools are not present in production images.
• Build infrastructure exclusively from security-approved OS versions, software versions, and frameworks. Define security controls such as access control lists (ACLs) and file integrity monitoring (FIM) as code.
• Enforce a least-privilege model for all processes running on shared infrastructure.
• Prefer smaller clusters to reduce complexity and limit the blast radius of a compromise.
• Validate that cloud service providers’ (IaaS/PaaS) security controls meet your business requirements within their portion of the shared responsibility model. Do not assume compliance by default.

9. Continuous Delivery and Deployment

The final stage of the pipeline—releasing software to users—is where security failures become customer-facing incidents. Security gates here are the last programmatic check before code reaches production.

• Base release-to-production decisions on predefined metrics that explicitly include security metrics, not just functional test pass rates or deployment success.
• Enforce an allow-list (whitelist) policy for application network segmentation in each environment, with production receiving the most restrictive policy.
• Trigger run-time security and compliance checks as part of the continuous deployment process:
  • Confirm unnecessary services are disabled and only required ports are open.
  • Verify file permissions, audit policies, and logging configurations are correctly applied.
  • Confirm that development tools are absent from the production environment.
• Vault all secrets required for deployment and retrieve them programmatically at run-time or pipeline initialisation. Never store secrets in pipeline configuration files or environment variables checked into source control.

Putting It Together: Assessing Your DevSecOps Maturity

No two organisations have identical risk profiles, team structures, or technology stacks. The nine pillars above are a framework for assessment, not a prescriptive checklist. Use them to identify which areas are mature, which are nascent, and where investment will yield the greatest risk reduction for your specific context.

A few principles apply universally:

• Shift left: The earlier in the pipeline a vulnerability is caught, the cheaper it is to fix. Pre-commit SAST and peer review security checklists cost far less than post-production incident response.
• Close the feedback loop: Production telemetry should inform test design. Threat intelligence should inform architecture decisions. Security is not a one-way gate—it is a cycle.
• Automate to accelerate, not just to enforce: Automated security controls should enable teams to move faster with confidence, not create new bottlenecks that incentivise bypassing them.
• Measure what matters: Metrics such as MTTD and MTTR, pre-production defect density, and pipeline security gate failure rates give leadership a data-driven view of security posture over time.

End-to-end security metrics—spanning planning through to production operations—and the tooling to surface them (value stream management applied to DevSecOps, enterprise security dashboards) are a natural extension of this framework and warrant dedicated treatment beyond the scope of this article.

This article draws on a framework originally published on devops.com.

Cloud Migration Is Only the Beginning

Cloud has become a foundational component of modern business strategy, driven largely by the race to digitally transform. Organizations worldwide are migrating applications and services to the cloud, attracted by reductions in capital and operational expenditure. But the migration itself is not the destination — it is the starting line.

When harnessed correctly, cloud infrastructure becomes a platform for continuous innovation, enabling business models and use cases that were simply not feasible a few years ago. No group inside an organization feels this more acutely than DevOps teams, who are responsible for continuous delivery and deployment pipelines. Yet in many organizations, the people making cloud strategy decisions are rarely the engineers doing the day-to-day delivery work.

For digital transformation (DX) to succeed, DevOps teams must be agile, efficient, and capable of delivering higher-quality software at greater velocity — while working collaboratively across quality assurance (QA), security, development, and IT operations functions.

DevOps Maturity: Culture and Visibility

At its core, the DevOps function exists to produce new software based on business needs, at high velocity, under real-world constraints, and at the highest possible quality. High velocity in this context means a continuous delivery pipeline that can, in advanced organizations, support multiple production releases per day — each requiring cycles of build, test, and integration before deployment. High quality means reliable, responsive services with minimal downtime and a positive customer experience.

The maturity of a DevOps organization can be assessed across two key dimensions, loosely analogous to frameworks such as the Software Capability Maturity Model (SW-CMM) developed at the Software Engineering Institute at Carnegie Mellon University:

• Cultural maturity: The degree to which teams collaborate effectively and take shared ownership of outcomes, rather than optimizing for function-specific metrics in isolation.
• Holistic visibility and situational awareness: The availability of telemetry and KPIs that are meaningful across all functional teams — development, QA, operations, and security — rather than being siloed within each group.

In less mature organizations, each team focuses on its own domain. Developers track release frequency. QA tracks test coverage and defect rates. Operations monitors production performance. Each group optimizes its own work without a shared view of the system as a whole. This siloed approach creates bottlenecks, slows feedback loops, and increases the time it takes to detect and resolve issues.

Mature DevOps organizations break through these silos. They establish a common situational awareness — a shared, real-time understanding of system state that enables all teams to work from the same picture. This shared awareness is what allows development, QA, operations, and security to act as a coherent unit rather than a collection of competing departments.

How Visibility Transforms the Delivery Pipeline

To understand the value of visibility, consider a typical software delivery cycle in an organization that lacks it:

1. Developers write and build code.
2. Code is handed off to QA for testing.
3. A release manager oversees integration and deployment.
4. Operations identifies a production issue — often one that only appears at scale.
5. Development must rapidly diagnose and patch the issue, often without full context about what is happening in production.

At each handoff, teams are working with incomplete information about what happened before and after their stage. The feedback loop is slow and lossy.

When all teams share a common view of the system — the same telemetry, the same dashboards, the same real-time signals — this dynamic changes fundamentally. Developers do not need to wait for Operations to surface a problem; they can observe the same production signals directly. This compresses feedback loops, accelerates root-cause analysis, and reduces the coordination overhead that slows delivery in siloed organizations.

Smart Data: The Engine of Common Situational Awareness

Visibility at the level required by a mature DevOps organization depends on the quality and timeliness of the underlying data. One approach that has gained attention is the concept of smart data — metadata derived from processing and organizing wire data (network packet data) at the collection point, optimized for real-time analytics.

The distinction between smart data and traditional log-based monitoring is significant:

By analyzing every IP packet traversing the network in real time, smart data approaches can deliver condensed, actionable datasets to all teams simultaneously. QA, Development, and Operations can all monitor evolving leading indicators without waiting for log aggregation pipelines to catch up. This eliminates bottlenecks in the feedback loop and enables issues to be identified and addressed in real time — rather than discovered in a post-mortem.

Security: The DevSecOps Dimension

The importance of visibility becomes even more acute when security is considered as part of the picture — as it must be in a DevSecOps organization. In traditional security models, a breach is typically analyzed after the fact: logs are reviewed, forensic work is done, and a retrospective report is produced. This is valuable, but it is inherently reactive.

In a DevSecOps model, security engineers work alongside developers throughout the development lifecycle. Real-time visibility into which application behaviors are anomalous — or which known vulnerability patterns are being triggered — allows security findings to feed directly back into the development pipeline. Issues can be addressed at the source, before they compound.

When combined with automation, this approach can:

• Reduce the need for reactive incident response war rooms.
• Enable security defects to be caught and fixed earlier in the delivery cycle, when they are cheaper to address.
• Limit potential reputational damage by reducing the window between vulnerability introduction and remediation.

Putting It Together: Visibility as a Strategic Capability

Digital transformation and hybrid cloud adoption introduce genuine complexity. Applications span on-premises infrastructure, private cloud, and public cloud environments. Traffic flows are more varied and harder to instrument than in traditional data center deployments. This is precisely the environment in which visibility becomes a strategic differentiator rather than a nice-to-have.

Organizations that invest in common situational awareness — through real-time telemetry, cross-functional dashboards, and smart data pipelines — give their DevOps and DevSecOps teams the foundation they need to operate effectively. Siloes break down not through cultural mandates alone, but through shared tooling that makes collaboration the path of least resistance.

The organizations that will maintain a competitive edge in rapidly disrupted markets are those whose engineering teams can see the full picture, react quickly, and continuously improve — together. Mature DevOps practice is not a destination; it is an ongoing journey of improving telemetry, tightening feedback loops, and deepening the shared awareness that allows fast, safe, and secure delivery at scale.

Research from Forrester has described DevOps adoption as having reached “escape velocity,” with a significant proportion of enterprises implementing DevOps principles in some form. However, “implementing DevOps” covers a wide spectrum. When you look across organisations, three distinct patterns emerge: DevOps Lite, Ops-Centric DevOps, and Dev-Centric DevOps.

If you believe digital transformation will rapidly enable you to achieve scale, increase revenue, and outpace competitors, you need well-developed DevOps practices, tooling, and culture that match the scale of that investment. On the other hand, if you are cautiously testing whether DevOps is right for your organisation, a lighter-touch approach is appropriate. Taking on too much too quickly—especially when it is out of step with business priorities—is a reliable path to failure.

The decision ultimately comes down to three factors: culture, tools and skill sets, and budget. Let’s explore each in the context of the three maturity patterns.

Culture

Culture is the foundation of any DevOps initiative. A mature DevOps culture is characterised by deep collaboration between development and operations teams, shared responsibility for application lifecycle management, the elimination of organisational silos, and a high degree of team autonomy. Each of the three maturity patterns reflects a different cultural starting point.

DevOps Lite: Dipping a Toe In

DevOps Lite organisations are exploring whether DevOps will work for them, typically by applying the approach to one or two low-risk applications. While development and operations staff on those projects may collaborate, their instincts remain siloed. No fundamental reorganisation has taken place, and decision-making around deployments and change management stays process-heavy, leaving teams with limited autonomy.

Ops-Centric DevOps: Infrastructure Leads the Way

In an Ops-Centric model, infrastructure and operations (I&O) professionals with strong infrastructure backgrounds drive the DevOps effort. They actively engage development teams to share responsibility for a system across its entire lifetime. Silos slowly break down, teams gain more autonomy to ship changes or resolve incidents quickly, and automation becomes a cultural priority. The primary end-state goal in this pattern is reliability and availability: a fully automated environment that keeps systems stable and predictable.

Dev-Centric DevOps: Full Collaboration, Continuous Delivery

Dev-Centric organisations feature robust, ongoing collaboration between development and operations. The DevOps function is typically embedded within the broader application team, so it naturally absorbs developer-oriented cultural values. I&O teams are still involved—but early in the development lifecycle, contributing input on deployment and maintenance requirements rather than acting as a gate at the end.

These organisations have embraced both continuous integration (CI)—ensuring that new code commits do not break the application—and continuous delivery (CD)—ensuring the software can be released to production at any time. Development teams share operational responsibility and use production monitoring data to simplify future deployments. Where Ops-Centric DevOps targets reliability as the primary end-state, Dev-Centric DevOps targets rapid, repeatable application deployment.

Tools and Skill Set

Automation is the technical engine of DevOps. It eliminates friction by reducing manual handovers and approval chains between development and operations. Enterprises achieve automation through tools covering infrastructure provisioning and configuration, software testing, continuous integration and delivery pipelines, and application and infrastructure monitoring.

DevOps Lite: Existing Tools, Basic Automation

DevOps Lite organisations typically do not invest in purpose-built DevOps tooling. Instead, they extend the capabilities of tools they already own—for example, existing VMware tooling, traditional IT operations management platforms, or the basic CLI utilities bundled with Docker. Staff at this stage are generally novices with these tools, and automation coverage is limited.

Ops-Centric DevOps: Advanced Automation with Legacy Alongside

Ops-Centric organisations start adopting purpose-built automation and orchestration tools while still relying on legacy platforms to manage a broad application portfolio. Open-source editions of configuration management tools—Puppet, Chef, and Ansible—become the backbone of operations. Teams experiment with these tools to build expertise before committing to enterprise licensing.

Dev-Centric DevOps: Standardised, Enterprise-Grade Toolchains

Dev-Centric organisations have standardised on a coherent, enterprise-grade toolchain. They use infrastructure automation tools such as Puppet and Ansible, monitoring platforms such as Nagios, and a full CI/CD pipeline supported by tools like GitHub, GitLab, and Jenkins. Enterprise editions are preferred for their advanced features and vendor support. Teams are skilled across this full stack and can build, deploy, and validate new applications quickly and reliably.

The key differentiator between Ops-Centric and Dev-Centric toolchains is not the infrastructure automation layer—both use similar tools there—but the addition of a mature CI/CD layer that connects code commits directly to production deployments.

Budget

Investment commitment varies significantly across the three patterns, and it should: the right level of spending is the one that matches your transformation ambitions, not the one that looks most impressive on paper.

• DevOps Lite: No dedicated DevOps budget. These organisations reuse existing tools and invest primarily staff time. This is appropriate when the goal is exploration rather than transformation.
• Ops-Centric DevOps: Budget is directed at advanced infrastructure automation and orchestration tooling. The focus is on enabling I&O teams to automate deployment, configuration management, and monitoring across a growing range of applications.
• Dev-Centric DevOps: A dedicated budget exists for enterprise-grade tooling, defined team structures, and ongoing staff training. This budget often sits outside traditional application or I&O cost centres—reflecting the fact that DevOps at this level generates its own measurable business value and warrants independent funding.

Mapping Your DevOps Pattern to Your Appetite for Transformation

The three patterns are not a ladder that every organisation must climb. They are reference points. The right pattern for your organisation depends on the role digital capabilities play in your business strategy.

If digital transformation is a core competitive strategy for your business, your DevOps model must reflect that magnitude—in the talent you hire, the processes you build, and the tools you invest in. Conversely, if digital is one initiative among many, over-investing in DevOps maturity creates unnecessary complexity and cost.

The most important step is an honest assessment: where are you today, where do your business goals require you to be, and what is the realistic path between those two points? Use the culture, tooling, and budget dimensions above as a practical diagnostic to locate your organisation on the scale—and to decide whether your current pattern is genuinely aligned with your transformation ambitions.

12-Factor App Methodology: The Foundation

The 12-Factor App methodology describes patterns that make web applications easier to scale and operate in cloud environments. The following factors are especially relevant when architecting a Laravel application on AWS.

Externalise Configuration

Never hard-code database connection strings, API keys, hostnames, or environment flags in your codebase. Store them as environment variables and inject them at runtime. Laravel’s .env file is designed for local development; on AWS, prefer AWS Systems Manager Parameter Store or AWS Secrets Manager to distribute secrets securely to your instances or containers.

Declare Dependencies Explicitly

Use Composer to declare every PHP library your application needs. Nothing should be installed manually on a server that is not tracked by composer.json. This makes your instances reproducible and your deployments predictable.

Use Version Control

Every change to your codebase should flow through a version-controlled Git workflow. Options include GitHub, Bitbucket, or AWS CodeCommit. A single source of truth for code is a prerequisite for any reliable CI/CD pipeline.

Design Stateless Processes

Your Laravel application processes must not rely on local filesystem state or in-memory data that persists between requests. If an instance is terminated and replaced by AWS Auto Scaling, no data should be lost. Sessions, uploaded files, and cached data must live in external services — Redis, S3, or a database — never on the instance itself.

Immutable Infrastructure

Once an EC2 instance is provisioned from an Amazon Machine Image (AMI), it should not be modified in place. Instead, bake a new AMI whenever you need to update the OS, PHP runtime, or application dependencies. This approach removes configuration drift, makes rollbacks straightforward, and lets Auto Scaling launch pre-validated instances reliably.

Keep Environments as Similar as Possible

Development, staging, and production should run the same PHP version, the same OS image, and the same service versions. Infrastructure-as-code tools such as AWS CloudFormation or Terraform help close the gap between environments and surface integration issues before they reach production.

Implement CI/CD from the Start

As your team and traffic grow, manual deployments become a liability. Build a continuous integration and delivery pipeline — using AWS CodePipeline, GitHub Actions, or a similar tool — that automatically tests, builds, and deploys your application across all environments. CI/CD is what makes the other factors operationally viable at scale.

10 AWS Practices for Scaling Your Laravel SaaS Application

Practice 1: Offload Static Assets to Amazon S3

The first and most impactful step is to move all static content — images, videos, documents, CSS, and JavaScript bundles — out of your EC2 instances and into Amazon S3. This distributes requests so that your web servers handle only dynamic PHP workloads while S3 handles the high-volume, high-throughput delivery of static files.

Practically, this means:

• Creating an S3 bucket for your application assets.
• Assigning an IAM role to your EC2 instances that allows write access to the bucket.
• Configuring the Laravel filesystem driver to use the s3 disk in config/filesystems.php.
• Migrating existing uploaded files from local storage to S3.

Amazon S3 also supports hosting static front-end applications (React, Angular, plain HTML) directly, giving you a fully serverless delivery path for your front end without any web server involvement.

Practice 2: Horizontal Scaling with AWS Auto Scaling and an Application Load Balancer

AWS Auto Scaling and the Application Load Balancer (ALB) are complementary but distinct services. The ALB distributes incoming requests across a target group of EC2 instances. Auto Scaling monitors instance metrics and automatically adds or removes instances from that target group based on policies you define.

To set this up you need to:

1. Create an Application Load Balancer and configure listeners for HTTP and HTTPS.
2. Build a golden AMI — a base image with your OS, PHP runtime, Nginx or Apache, and application code baked in.
3. Create an Auto Scaling Launch Template (or Launch Configuration) referencing that AMI.
4. Create an Auto Scaling Group with a minimum, desired, and maximum instance count.
5. Define CloudWatch alarms that trigger scale-out and scale-in actions based on CPU utilisation, request count, or a custom metric.

This combination means your infrastructure grows during traffic peaks and shrinks during quiet periods, directly controlling your AWS bill.

Practice 3: AWS RDS Aurora with Read Replicas

Amazon RDS is a managed relational database service that handles patching, automated backups, and failover. For a SaaS application that needs high read throughput, Amazon Aurora is worth serious consideration. AWS positions Aurora as significantly faster than standard MySQL and PostgreSQL, though real-world gains depend on your workload profile.

Key Aurora features to leverage:

• Read replicas: Offload read-heavy queries (reporting, dashboards, search) from the primary writer instance to one or more reader endpoints.
• Multi-AZ deployment: Aurora automatically replicates data across multiple Availability Zones, providing automatic failover without manual intervention.
• Aurora Serverless: For workloads with unpredictable or intermittent traffic patterns, Aurora Serverless can automatically scale database capacity up and down.

In Laravel, configure your config/database.php to use separate read and write connections, pointing the read connection to the Aurora reader endpoint and the write connection to the writer endpoint.

Cost note: Aurora with Multi-AZ and read replicas carries a meaningfully higher cost than a single-AZ RDS instance. Right-size your Aurora cluster and review AWS Cost Explorer regularly.

Practice 4: Application Load Balancer for Layer 7 Routing

The AWS Application Load Balancer operates at OSI Layer 7, which means it can make routing decisions based on the HTTP request — including the hostname, URL path, and HTTP headers. For a Laravel SaaS application this unlocks several patterns:

• Route /api/* requests to a separate target group of API-optimised instances.
• Host multiple domains or sub-applications behind a single ALB, each with its own SSL certificate (using SNI support).
• Integrate with AWS WAF to filter malicious traffic before it reaches your instances.
• Terminate SSL at the load balancer, simplifying certificate management across your fleet.

The ALB is billed per hour plus a Capacity Unit charge based on the number of new connections, active connections, and processed bytes — generally less expensive than the Classic Load Balancer for most web workloads.

Practice 5: CloudFront CDN in Front of S3 and Your Origin

Once your static assets are in S3, place Amazon CloudFront in front of them. CloudFront caches your S3 content at edge locations closest to your users, reducing latency and eliminating repeated data-transfer charges from S3 for the same objects.

CloudFront capabilities relevant to Laravel SaaS:

• Edge caching: Static assets are cached at the edge, reducing origin load and improving Time to First Byte globally.
• Dynamic content acceleration: CloudFront can also proxy requests to your ALB origin, improving TCP connection reuse on the AWS backbone.
• DDoS protection: CloudFront is integrated with AWS Shield Standard at no additional charge and can be paired with AWS WAF for application-layer filtering.
• Custom domain and SSL: Attach your own subdomain (e.g., cdn.yourapp.com) using a CNAME record and provision a free certificate from AWS Certificate Manager.
• Video streaming: CloudFront supports both HLS and DASH streaming for media-heavy SaaS products.

Integration steps: create a CloudFront distribution with your S3 bucket as the origin, configure your Laravel application to prefix asset URLs with your CloudFront domain, and set appropriate cache-control headers on your S3 objects.

Practice 6: Amazon VPC with Public and Private Subnets

Never place database instances or internal services on a publicly routable subnet. A properly designed Amazon VPC separates your infrastructure into layers:

• Public subnets: ALB, NAT Gateways, and any bastion host that requires internet reachability.
• Private subnets: EC2 application instances, RDS Aurora, and ElastiCache clusters. These resources have no direct inbound internet access.

Security groups act as stateful virtual firewalls. Define granular rules: your EC2 instances should only accept traffic from the ALB security group, and your RDS cluster should only accept traffic from your EC2 security group. This defence-in-depth approach supports PCI DSS, HIPAA, and other compliance frameworks.

For developer access to private resources, deploy an AWS Systems Manager Session Manager or a bastion host rather than opening SSH to the internet.

Practice 7: AWS ElastiCache for Sessions and Data Caching

ElastiCache provides managed Redis or Memcached clusters. For a stateless Laravel application running across multiple EC2 instances, it is the correct place to store PHP sessions and cached data — not the local filesystem.

Common ElastiCache use cases in Laravel:

• Session storage: Configure SESSION_DRIVER=redis so that any instance in your Auto Scaling group can serve any user’s session.
• Application cache: Set CACHE_DRIVER=redis to use Laravel’s Cache facade with ElastiCache as the backend, reducing repetitive database queries.
• Queue backend: Laravel Horizon can use Redis as its queue backend, with ElastiCache providing the Redis cluster.
• Rate limiting: Laravel’s built-in rate limiter uses the cache store, making ElastiCache a natural fit.

Integration steps:

1. Deploy an ElastiCache Redis cluster in your private subnet.
2. Install the predis/predis package via Composer (or use the phpredis extension).
3. Set the Redis connection details in your environment variables and update config/database.php accordingly.

Practice 8: Amazon Route 53 for DNS and Traffic Routing

Migrating your DNS from a third-party registrar to Amazon Route 53 gives you deeper integration with AWS services and access to advanced routing policies:

• Alias records: Route 53 supports alias records that resolve directly to an ALB, CloudFront distribution, or S3 endpoint — no CNAME workarounds needed at the zone apex.
• Health checks: Route 53 can monitor your endpoints and stop routing traffic to an unhealthy origin.
• Failover routing: Define a primary and a secondary environment; Route 53 automatically redirects traffic to the secondary if the primary fails health checks.
• Latency-based routing: If you run instances in multiple AWS regions, Route 53 routes each user to the region with the lowest measured latency.
• Geolocation routing: Serve users from the AWS region physically closest to them, or enforce data-residency requirements by routing users in specific countries to specific regions.

Note: DNS failover operates at the DNS TTL level and is not equivalent to real-time load balancer failover. DNS caching by resolvers means failover convergence can take minutes, not milliseconds.

Practice 9: Why AWS EFS Is Usually the Wrong Choice for Application Code

A tempting pattern for sharing application code across multiple Auto Scaling instances is to mount an Amazon EFS (Elastic File System) share on every instance. In practice this introduces a network file system in the critical path of every PHP file read, adding latency and a single dependency that can degrade the entire fleet.

The stateless, immutable-infrastructure approach is a better alternative: bake your application code into an AMI or deploy it via a CI/CD pipeline to each instance at boot time. EFS remains useful for genuinely shared file storage scenarios — for example, storing user-uploaded files that require a POSIX filesystem interface — but S3 is typically the better choice even for that use case in a Laravel application.

Practice 10: Extend Your Architecture with AWS Lambda

Not every workload belongs on a long-lived EC2 instance. AWS Lambda lets you run code in response to events without managing servers. For a Laravel SaaS application, Lambda is most useful for workloads that are event-driven, short-lived, or infrequent.

Practical Lambda patterns for Laravel teams:

• Scheduled tasks: Replace EC2-hosted cron jobs with scheduled Lambda functions triggered by Amazon EventBridge. This eliminates a single point of failure and removes idle instance cost.
• Image and media processing: Trigger a Lambda function when a file lands in S3 to resize images, transcode video, or generate thumbnails — without tying up a web server.
• API integrations and webhooks: Use Lambda behind API Gateway to handle inbound webhooks or lightweight API endpoints that do not justify a persistent server.
• Disaster recovery scripts: Move backup scripts from EC2 cron jobs into Python or Node.js Lambda functions to make them independently runnable and auditable.
• Queue consumers: For tasks that are genuinely event-driven, Lambda can consume messages from SQS queues at scale without running a persistent worker process.

Lambda charges based on the number of invocations and the duration of each execution, making it cost-effective for sporadic workloads. It is not a replacement for your primary Laravel application server, but it is a powerful complement for offloading discrete tasks.

A Note on AWS Elastic Beanstalk

AWS Elastic Beanstalk is a PaaS layer that automates environment provisioning, load balancer setup, and deployments. It reduces initial configuration effort but can make custom configurations — non-standard PHP extensions, advanced Nginx tuning, complex networking requirements — harder to implement and maintain. Stack version upgrades and troubleshooting can also be less transparent than managing the underlying AWS resources directly.

The architecture described in this article deliberately bypasses Elastic Beanstalk in favour of directly managed EC2, ALB, and Auto Scaling resources. This approach requires more initial setup but gives engineering teams full control over the infrastructure. Beanstalk remains a reasonable starting point for smaller teams or early-stage products where operational simplicity outweighs flexibility.

Summary: Building a Production-Ready Laravel SaaS Architecture on AWS

The practices above combine into a coherent, layered architecture:

1. S3 stores static assets and front-end applications.
2. CloudFront caches and accelerates delivery of those assets globally.
3. Route 53 manages DNS with health checks, failover, and latency-based routing.
4. Application Load Balancer terminates SSL and routes Layer 7 traffic to your application fleet.
5. Auto Scaling keeps the right number of EC2 instances running based on demand.
6. VPC with public and private subnets isolates and protects every tier of the stack.
7. ElastiCache (Redis) provides shared session storage, caching, and queue brokering across all instances.
8. RDS Aurora delivers a managed, replicated, highly available relational database.
9. Lambda handles event-driven and scheduled workloads without idle server cost.
10. CI/CD and immutable AMIs ensure every deployment is consistent, repeatable, and reversible.

Applying these practices incrementally — starting with S3 offloading and stateless session storage, then adding Auto Scaling and CloudFront — lets a small team progressively harden their Laravel SaaS application without a big-bang infrastructure overhaul.

The challenges are not purely technical. DevOps admins sit at the intersection of competing cultural pressures, misaligned team incentives, and an ever-expanding toolchain. Understanding these obstacles clearly is the first step toward working through them.

1. Over-Reliance on DevOps Tooling

One of the most common traps organizations fall into is treating DevOps as a tooling problem rather than a process and culture problem. Automation platforms, CI/CD pipelines, and observability stacks are genuinely valuable — but they are ingredients, not the finished product. When organizations become enamored with the latest automation tool without addressing the underlying workflows and team dynamics, they create isolated pockets of efficiency that fail to benefit the wider organization.

The principle often cited in the DevOps community is that tooling should account for a minority of your investment and attention. The majority — communication patterns, organizational structure, information flows, and process consistency — determines whether tools actually deliver value. A well-configured CI/CD pipeline staffed by siloed teams that do not communicate will not ship better software faster.

For DevOps admins, this means advocating for process and culture investment alongside any tool procurement. Before rolling out a new platform, audit whether the team structure and communication norms are in place to support it. Tool adoption without organizational readiness reliably produces shelfware and frustration.

2. Building Genuine Collaboration Between Dev and Ops

The foundational tension in any DevOps transformation is the historically adversarial relationship between development and operations teams. The motivations of each group, while not incompatible, are easy to frame as being in direct conflict:

• Operations prioritizes stability, uptime, and controlled change. Every release is a potential source of unplanned downtime, and Ops teams are measured on availability metrics.
• Development prioritizes speed, feature delivery, and responsiveness to business needs. Developers are measured on how quickly they ship working software.

DevOps administrators effectively act as a bridge between these two groups. This means filling the information gap — making sure developers understand what it takes to run their software reliably in production, and ensuring operations teams understand the business urgency driving release timelines. This is a communication and process challenge as much as it is a technical one.

Practical steps that help close this gap include shared on-call rotations, joint post-incident reviews (blameless postmortems), shared dashboards that surface both deployment frequency and service reliability, and co-ownership of runbooks. The goal is not to turn developers into operators or vice versa — it is to build enough shared context that both sides can make better decisions together.

3. Lack of Visibility and Transparency Across the Delivery Pipeline

Even organizations with mature DevOps tooling frequently lack a coherent picture of their software delivery health. Data exists — deployment frequency, lead time, change failure rate, mean time to recovery — but it is often scattered across disconnected tools, interpreted inconsistently, or not acted upon at all.

From a practical standpoint, administrators need a reporting structure that aggregates release, environment, quality, and deployment data at the portfolio level, not just per project. When this kind of visibility exists, delivery teams can:

• Identify bottlenecks that consistently delay releases
• Correlate deployment patterns with incident rates
• Predict risk before a release goes out, rather than responding to problems after the fact
• Demonstrate to leadership that improvements are measurable and real

Establishing this visibility early — ideally at the beginning of a DevOps initiative rather than as an afterthought — makes every subsequent improvement easier to justify and execute. The four key metrics popularized by the DORA research (deployment frequency, lead time for changes, change failure rate, and mean time to restore service) provide a useful, well-established starting framework.

4. Breaking Down Silos While Working in Small Teams

There is an inherent tension in DevOps team design: the methodology encourages small, autonomous teams working on discrete services or features, but that same structure naturally produces organizational silos. When multiple small teams share test environments, release windows, or downstream dependencies, a lack of cross-team coordination can cause release conflicts, duplicated work, and cascading failures.

In large organizations running frequent release cycles — sometimes multiple times per day — this coordination problem becomes acute. Individual teams may be highly efficient within their own scope while remaining blind to the broader release picture. Without cross-team visibility and defined escalation paths, release trains collide.

Addressing this requires explicit organizational design choices, not just better tooling:

• Defined team interfaces: Each team should have clear ownership boundaries and documented APIs or contracts with other teams.
• Release coordination mechanisms: Whether a release train model, a feature flag strategy, or a change advisory process, there needs to be a structured way for teams to signal their readiness and dependencies.
• Shared environment management: Test and staging environments should be treated as shared infrastructure with explicit scheduling and ownership, not a free-for-all.
• Cross-team communication rhythms: Regular syncs between team leads, shared backlogs for shared services, and visible dashboards reduce the surprise factor that derails coordinated releases.

The Organizational Dimension: Admins Cannot Do This Alone

A thread running through all of these challenges is that DevOps administrators are frequently expected to solve organizational problems with technical authority they do not have. An admin can configure a world-class CI/CD pipeline, but they cannot mandate that a VP of Engineering change how their teams are structured. They can instrument a deployment dashboard, but they cannot force directors to act on the data it surfaces.

For DevOps transformations to succeed at scale, executive sponsorship and top-down cultural change must accompany bottom-up technical implementation. This means:

• Leadership communicating clearly that DevOps practices are an organizational priority, not a team experiment
• Incentive structures that reward collaboration and system-level outcomes, not just individual team throughput
• Budget and headcount allocated to the process and culture work, not only to tooling
• Psychological safety for teams to surface problems without fear of blame

DevOps administrators who can articulate these organizational requirements in business terms — reduced time to market, lower incident costs, improved retention of engineering talent — are better positioned to secure the organizational backing they need to make meaningful progress.

Key Takeaways

• Technology is necessary but not sufficient. Culture and process improvements must lead any DevOps initiative.
• Bridging the Dev-Ops divide requires shared context, shared metrics, and structured communication — not just shared tools.
• Visibility into the full delivery pipeline is a prerequisite for data-driven improvement.
• Small autonomous teams are a feature of DevOps, but cross-team coordination mechanisms must be designed deliberately to prevent silo formation.
• DevOps admins need organizational authority and executive sponsorship to drive change that extends beyond their technical scope.

Working through these challenges is genuinely difficult, but the payoff — faster, more reliable software delivery with healthier team dynamics — is well worth the investment. The organizations that succeed are those that treat DevOps as a continuous organizational practice rather than a one-time tool rollout.

From DevOps to DevSecOps: Why the Shift Matters

As Federal agencies adopt DevOps practices to shorten development cycles and increase deployment frequency, security must be woven into every phase of the process—from design and coding through testing, release, and ongoing operations. Treating security as a final gate before deployment is no longer viable when releases happen continuously.

DevSecOps—a natural evolution of DevOps—addresses this gap directly. Where DevOps combines development and operations into a unified workflow, DevSecOps adds a third, equally important pillar: security. The goal is to break down the traditional silos between development, security, and operations teams so that all three work collaboratively and continuously throughout the software development lifecycle (SDLC). It is both a cultural philosophy and an engineering practice, emphasizing automation, continuous monitoring, and shared responsibility for security outcomes.

Federal Agencies Are Already Embracing DevSecOps

Several major Federal departments were reported to be leading the way in rethinking how they develop and secure applications. The Departments of Homeland Security and Veterans Affairs, along with the General Services Administration and the Environmental Protection Agency, were cited as agencies actively breaking the traditional mold of DevOps to incorporate stronger security practices. Elizabeth Lawler, then Vice President of DevOps Security at CyberArk, highlighted these agencies’ efforts in a piece published by MeriTalk.

The Department of Agriculture (USDA) also moved to formalize its DevSecOps posture, seeking external support as it entered Phase II of its IT Modernization Centers of Excellence (CoEs). In a solicitation released in late July 2018, USDA sought assistance across cloud adoption, infrastructure optimization, data center consolidation, application migration, cloud portfolio management, and explicit DevSecOps support—signaling that DevSecOps had become a procurement-level requirement, not just a technical aspiration.

The Federal Software Factory Approach

One of the most significant architectural shifts accompanying DevSecOps adoption in the Federal space is the move toward a software factory model. Adam Clater, then Chief Architect with Red Hat U.S. Public Sector, described this trend:

“The Federal government has gotten very savvy about software development. What I’m beginning to see in the Federal government, at least in large programs where it is available, is a move to the concept of software factory development.”

— Adam Clater, Chief Architect, Red Hat U.S. Public Sector

A software factory is a unified platform on which software is created, complete with end-to-end automation covering code scanning, testing, and operations. Rather than assembling these capabilities ad hoc for each project, the factory standardizes the pipeline so security and quality controls are consistent and repeatable. Whether owned by the agency or a contractor, the key benefit is rigorous standardization that enables both scalability and improved security posture.

DevSecOps Is a Cultural Change, Not Just a Toolchain

One of the most important—and frequently underestimated—aspects of DevSecOps is that it is fundamentally a cultural transformation. For many agencies, a DevSecOps kickoff meeting may be the first time development, security, and operations teams have ever sat in the same room together. That convergence alone can have a profound impact on communication and shared ownership of outcomes.

Clater offered four practical recommendations for agency managers building DevSecOps programs:

1. Start small and build iteratively. Resist the temptation to transform everything at once. Begin with a small, focused team to learn how your organization needs to adapt before scaling the model. Early iterations surface cultural and process friction while the cost of change is still manageable.
2. Celebrate success openly. Recognizing wins—even small ones—builds momentum and reinforces the behaviors that DevSecOps depends on, including cross-team collaboration and transparency.
3. Fail fast and learn continuously. This is not a license for the initiative to fail; it is permission for developers to commit code, discover it doesn’t meet requirements, and fix it the following day. Small, daily improvements compound into meaningful progress. A constrained footprint and tight feedback loops make course corrections cheap and fast.
4. Build organizational adaptability, not just cloud adoption. Technology—especially cloud infrastructure—evolves constantly. Measure success by how well your teams and processes can absorb change, not simply by how many workloads you have migrated to the cloud.

Security Vulnerability Testing Lags Behind Emerging Threats

Even as agencies mature their DevSecOps practices, the fundamental challenge remains: security testing of known vulnerabilities consistently lags behind the emergence of new threats. A report by FreeForm Dynamics and CA Technologies, titled Integrating Security Into The DNA of Your Software Lifecycle, identified behavioral analytics and machine learning as two technologies expected to help close this gap.

“Today security testing of vulnerabilities always lags behind known threats, increasing the requirement for continuous testing throughout the entire software lifecycle. Machine learning (ML) and behavioral analytics may enable more prescriptive lifecycle vulnerability testing. But beyond this, ML could also soon make it possible for apps to be able to make decisions on sensitive data access in real time, essentially helping improve the security of the app as it is being used.”

— Integrating Security Into The DNA of Your Software Lifecycle, FreeForm Dynamics and CA Technologies

The implication for DevSecOps practitioners is clear: static, point-in-time security testing is insufficient. Continuous security validation—integrated directly into the CI/CD pipeline—is the only approach that keeps pace with a threat landscape that evolves faster than any release cycle.

You Cannot Buy DevSecOps—You Have to Build It

Perhaps the most important principle for agency leaders to internalize is one articulated by Booz Allen Hamilton analysts Jimmy Pham and Bill Ott in their examination of DevSecOps myths: you cannot simply procure DevSecOps. It is a methodology, not a product.

Agencies can and should invest in supporting tools—continuous integration platforms, release management systems, static and dynamic code analysis tools—but the tools only enable the capability. It is the cross-functional delivery teams, empowered to collaborate and empowered to own quality and security outcomes, who make DevSecOps real. They are the engine of continual improvement in what is ultimately a cultural and paradigm shift.

Key Takeaways for DevOps Practitioners and Agency Leaders

• DevSecOps integrates security as a continuous, shared responsibility rather than a final checkpoint before deployment.
• The software factory model provides a standardized, automated platform that bakes security and quality controls into every build.
• Cultural change—getting development, security, and operations teams collaborating—is as critical as any technical implementation.
• Start with small, iterative pilots; fail fast; and measure adaptability rather than just feature delivery.
• Machine learning and behavioral analytics are emerging capabilities for continuous, prescriptive vulnerability testing, though their maturity and adoption in Federal environments should be evaluated carefully.
• Tools enable DevSecOps; empowered, cross-functional teams make it work.

The gap between aspiration and execution comes down to a fundamental misunderstanding of what DevOps actually requires. As Marko puts it: “DevOps is like dieting: it requires changes in values, attitudes, processes and habits. Such changes are hard and must be practiced, not bought. They require education and discipline, not a purchase order.”

Google’s approach to this challenge — Site Reliability Engineering (SRE) — offers one of the most mature, battle-tested models for bridging the gap between development velocity and operational stability. It is not simply a job title or a toolset. It is a discipline, a culture, and a set of engineering principles applied to the problem of running software at scale.

SRE vs. DevOps: Related but Distinct

SRE and DevOps are complementary movements that emerged independently but share the same core goals. Jayne Groll, writing on DevOps.com, draws a useful distinction: “DevOps focuses on engineering continuous delivery to the point of deployment; SRE focuses on engineering continuous operations at the point of customer consumption. Both domains rely on sharing, culture, metrics and automation. Both require human and automated resources to ensure a seamless value stream and exceptional customer experience.”

In practice, the two approaches sit on the same spectrum. DevOps pushes software through the pipeline quickly and reliably; SRE takes ownership once that software is running in production, ensuring it stays reliable at scale.

The Classic Dev vs. Ops Conflict — and How SRE Resolves It

Patrick Hill, a site reliability engineer at Atlassian, describes the historical tension between development and operations teams clearly:

“Dev teams want to release awesome new features to the masses, and see them take off in a big way. Ops teams want to make sure those features don’t break things. Historically, that’s caused a big power struggle, with Ops trying to put the brakes on as many releases as possible, and Dev looking for clever new ways to sneak around the processes that hold them back. SRE removes the conjecture and debate over what can be launched and when. It introduces a mathematical formula for green- or red-lighting launches, and dedicates a team of people with Ops skills (appropriately called Site Reliability Engineers, or SREs) to continuously oversee the reliability of the product.”

The “mathematical formula” Hill refers to is the concept of an error budget — a calculated tolerance for downtime or errors derived from a service’s agreed reliability target (its Service Level Objective, or SLO). When a service is well within its error budget, teams can move fast and ship freely. When the budget is being consumed too quickly, the brakes go on automatically. The decision is data-driven, not political.

What Google’s SRE Team Actually Does

In their book Site Reliability Engineering, Google engineers Betsy Beyer, Chris Jones, Jennifer Petoff, and Niall Murphy describe how SRE works in practice at Google. The approach “represents a significant break from existing industry best practices for managing large, complicated services.” It began as a pragmatic response to the problem of repetitive operational toil: rather than having engineers manually perform the same operational tasks repeatedly, Google applied software engineering principles to automate and eliminate that toil.

Over time, SRE evolved into something broader: “a set of principles, a set of practices, a set of incentives, and a field of endeavor within the larger software engineering discipline.”

In concrete terms, Google SREs apply computer science and engineering principles to large-scale distributed systems. Their work spans writing production software alongside product development teams, building shared infrastructure components such as load balancers and backup systems, and adapting existing solutions to new operational problems.

The Three Core Missions of an SRE

The Google SRE team identifies three primary areas of responsibility:

• Reliability: Reliability is the foundational priority. As Ben Treynor Sloss — Google’s VP of Engineering and the originator of the SRE concept — puts it: “Reliability is the most fundamental feature of any product: a system isn’t very useful if nobody can use it.” SREs define explicit reliability targets (SLOs), measure against them, and treat reliability as an engineering problem to be solved, not merely monitored.
• Features: Once a service meets its reliability targets, SREs shift attention to supporting new features and product development. The error budget framework ensures that feature velocity and reliability are balanced rather than opposed.
• Operating services: SREs are responsible for the ongoing operation of services built on distributed computing infrastructure — from planet-scale storage systems and email services handling hundreds of millions of users to the web search systems where Google began.

What Non-Google Organizations Can Take From This

A common objection to studying Google’s SRE model is that it was built for hyper-scale — a context most enterprises will never face. But the Google SRE authors address this directly: “For sizes between a startup and a multinational, there probably already is someone in your organization who is doing SRE work, without it necessarily being called that name, or recognized as such.”

The practical implication is that SRE principles — reliability targets, error budgets, toil reduction, blameless postmortems, and engineering-driven operations — are applicable well below Google’s scale. Smaller teams can adopt the mindset and the practices without replicating the organizational structure.

The key shift is cultural and organizational: stop treating operations as a separate concern managed by a separate team with separate incentives, and start treating reliability as an engineering discipline owned by the people who build the systems. That shift doesn’t require a purchase order. It requires commitment, education, and consistency — the same things that separate real DevOps maturity from mere aspiration.

Further Reading

Google has made significant portions of the Site Reliability Engineering book available for free at sre.google. The companion volume, The Site Reliability Workbook, provides more prescriptive guidance on implementing SRE practices in organizations that are not Google.

Despite the wealth of courses, certifications, and books now available, some of the most valuable DevOps lessons cannot be taught in a classroom. They come from lived experience — from projects that stalled, rollouts that backfired, and cultural battles that nobody anticipated. The following lessons were shared by DevOps veterans as advice they would give to their earlier selves. Whether you are just starting out or trying to mature an existing DevOps practice, these insights are worth taking seriously.

1. Beware of the “Them and Us” Mentality

Fin Goulding, international CIO, Aviva: “Without a shadow of a doubt, I underestimated the cultural challenges of implementing DevOps. The fusion of two ‘tribes’ into one sounds easy on paper: But the reality is very different and it takes quite some time to remove the ‘them and us’ mentality in order to have developers and engineers working side by side delivering value. I wish I had spent more time coaching individuals rather than just implementing the model across all the technical teams at once. In fact, it would have been best to start in a small way with a ‘lighthouse’ implementation before going for full roll-out.”

Goulding’s experience reflects one of the most consistent findings across DevOps transformations: tooling and process changes are relatively straightforward compared to the human side. Development and operations teams often carry years of entrenched habits, different incentive structures, and mutual mistrust. Forcing them into a shared workflow without adequate coaching frequently produces friction rather than collaboration.

A practical takeaway: before scaling DevOps organization-wide, identify a willing cross-functional team and run a pilot — a “lighthouse” — that can demonstrate value and surface cultural issues at a manageable scale. Use what you learn there to inform the broader rollout.

2. C-Suite Alignment Is Not Optional

Ben Grinnell, managing director, North Highland: “I would have liked to have understood the breadth of the changes needed to have a sustainable impact and the impact required across many of the typical C-suite functional areas. When we started out we were typically working with IT leaders (CIOs, CTOs or CDOs) trying to improve flow, feedback, and continuous improvement between development and operations functions. Often that solution wasn’t sustainable and didn’t deliver the benefits anticipated due to the problems elsewhere in the value stream, which gradually pulled the transformed Dev and Ops functions back to working in the old way.”

“This type of change is easily attacked by the corporate antibodies that destroy anything foreign. In order to stop this effect, you need to change the DNA of the organization.”

Ben Grinnell, North Highland

Grinnell goes on to explain that isolated DevOps improvements — even impressive ones — can be undone when the rest of the organization continues to operate under incompatible financial governance, procurement cycles, security review processes, and audit requirements. Without broader organizational alignment, improved Dev and Ops teams are essentially swimming against the current.

His advice: start small and learn, but invest early in building executive alignment that extends beyond the CIO or CTO. Procurement, finance, security, and audit all touch the software delivery value stream. Getting CEO-level sponsorship, in his experience, can accelerate progress significantly compared to trying to influence individual C-suite members one at a time.

3. When in Doubt, Have a Mitigation Plan

Eran Kinsbruner, director, author, and lead software evangelist, Perfecto: “Looking back, I wish I knew the importance of planning the DevOps pipeline to accommodate for changes. Along each iteration, changes are constantly being introduced, whether it’s a change to requirements, a change in backlog features, or other interruptions around quality (escaped defects, customer reported issues, etc.). Having a mitigation plan ahead of the end of journey can make the difference between business success and failure.”

Kinsbruner points out that mitigation planning is not simply a matter of writing a risk register. It requires strategic decisions about tooling, clear quality guidelines, and — critically — management buy-in to enforce those guidelines when schedule pressure tempts teams to cut corners.

He illustrates the stakes with a concrete example: enterprises that failed to incorporate new device environments into their continuous integration pipelines early enough struggled to ship quality software when major platform updates arrived. The lesson is that a CI pipeline that only reflects today’s environment is not truly continuous — it is already out of date. Regularly expanding test coverage to include emerging platforms, devices, and dependencies is part of maintaining a resilient pipeline.

Practical steps that follow from this lesson:

• Treat market and platform changes (new OS versions, new device form factors, dependency updates) as predictable sources of disruption, not surprises.
• Build explicit checkpoints into each iteration to evaluate whether the pipeline still reflects the real target environment.
• Define escalation paths and rollback procedures before a release, not during an incident.
• Make sure management understands that mitigation planning is not pessimism — it is what separates teams that ship reliably from teams that scramble.

4. Outcomes Define Success — Not the Tools

Mirco Hering, APAC DevOps and agile lead, Accenture: “I wish I’d have known from the beginning that technology is not the only solution; success is defined by the outcome, not the way to get there. I, like so many, was initially focused on ‘how to become DevOps’ rather than using the ever-increasing tool belt of DevOps practices and tools to achieve business outcomes. As technologists, we are sometimes too focused on the next shiny object. If I had learned this lesson earlier in my career I think I would have avoided some of the more spectacular failures of projects that implemented a technology but still did not achieve success.”

Hering’s observation addresses one of the most persistent failure modes in DevOps adoption: treating DevOps as a destination defined by a particular tool stack rather than as a means to a business end. Teams can adopt containers, implement CI/CD pipelines, instrument their services with monitoring, and still fail to deliver meaningful value if none of those choices are tied to a clear understanding of what success looks like for the business.

The antidote is to start every initiative with a clear statement of the business outcome it is meant to improve — deployment frequency, lead time, customer satisfaction, incident recovery time — and then work backwards to the practices and tools that best support that outcome. When a new tool or practice is proposed, the first question should be: “Which of our target outcomes does this improve, and how will we measure that?”

Common Threads Across All Four Lessons

Reading these four perspectives together, several themes emerge that are worth naming explicitly:

• Culture precedes tooling. Every practitioner touched on the human dimension — tribal mentalities, organizational antibodies, management buy-in. Tooling choices matter, but they are secondary to getting people aligned.
• Start small, but plan for scale. Lighthouse implementations and small pilots reduce risk, but they need to be designed with an eye toward how the lessons will propagate across the organization.
• Executive support changes the physics. Without alignment at the leadership level, even successful local improvements tend to regress as the surrounding organization exerts pressure to return to familiar ways of working.
• Define success before you start. Agreeing on measurable outcomes at the outset keeps teams honest about whether their choices are actually delivering value or just generating activity.

These lessons are not new — they recur in DevOps literature, conference talks, and post-mortems year after year. The reason they keep appearing is that they are genuinely hard to internalize until you have experienced the consequences of ignoring them. Treat them as warnings worth heeding before you learn them the hard way.

Original quotes sourced from enterprisersproject.com. Attribution has been preserved as it appeared in the original article.

Why Virtual Conferences Work for DevOps Professionals

In-person conferences are valuable — keynote presentations, hands-on workshops, one-on-one networking, and hallway conversations can all accelerate your learning and career. But they come with real costs: expensive travel, delayed flights, jet lag, and days away from the team. For many DevOps practitioners, those trade-offs simply don’t add up.

Virtual conferences have evolved significantly beyond basic webinar formats. Modern virtual summits can replicate the social and educational dimensions of physical events — including lobbies for networking, exhibitor floors, downloadable resources, and live Q&A — without requiring attendees to leave their desks. For time-pressed engineers and managers, that’s a compelling proposition.

About DevOps Experience

DevOps Experience was a free virtual summit presented by MediaOps — the company behind DevOps.com, Container Journal, Security Boulevard, and DevOpsTV — in partnership with CA Technologies. The event was designed specifically for DevOps professionals and delivered in a virtual reality-style environment.

The platform went beyond a simple slide deck stream. Attendees could:

• Watch keynotes and presentations in dedicated virtual theaters
• Socialise and network in a conference lobby environment
• Browse a virtual trade show floor with exhibitor stands featuring video content, downloadable resources, and promotional materials

Crucially, the event was free to attend — removing cost as a barrier to professional development.

Keynote Speakers

The speaker lineup represented some of the most recognised voices in the DevOps community at the time of the event:

• Gene Kim — CTO, researcher, co-author of The Phoenix Project and The DevOps Handbook, and founder of IT Revolution
• Nicole Forsgren — Founder and CEO of DevOps Research and Assessment (DORA), known for leading the annual State of DevOps research
• Alan Shimel — Editor-in-chief and founder of DevOps.com and Security Boulevard
• Aruna Ravichandran — Vice President of Global Marketing DevOps at CA Technologies, and author of DevOps for Digital Leaders and The Kitty Hawk Venture

Spotlight Sessions

Beyond the keynotes, the event included a series of focused Spotlight Sessions covering topics of practical relevance to DevOps teams:

• The Accelerate State of DevOps Report — An overview of the research findings driving evidence-based DevOps practice
• Delivering DevOps Practices for Mainframe — Addressing the often-overlooked challenge of applying modern DevOps methods to mainframe environments
• Women in DevOps Panel — A discussion highlighting diversity, inclusion, and representation within the DevOps community
• DevSecOps: Why the Time Is Now — Making the case for integrating security into the DevOps pipeline from the start, rather than treating it as a downstream concern

The Value Proposition of Free Virtual Summits

Events like DevOps Experience reflect a broader shift in how technical communities share knowledge. When a summit is free, virtual, and packed with credible speakers, the barrier to continuous learning drops significantly. DevOps practitioners at all levels — from engineers looking to sharpen their toolchain knowledge to leaders benchmarking their transformation efforts — can benefit without budget approval or travel requests.

If you missed DevOps Experience or similar virtual events, many recordings are made available after the fact through the organising platforms. Checking the DevOps.com and MediaOps archives is a good starting point for accessing past session content.

Dreariness in the IT department can be a major roadblock in the journey to success. DevOps looks to attack this problem like it’s never been done before. The DevOps philosophy is centered on the idea of collaboration between the development and operations groups within the organization. By developing a set of shared goals, DevOps eliminates the sources of friction between the two functions. This helps IT deliver software to end customers and internal customers much quicker than before, and in a more reliable manner. But the key to fully understanding DevOps is to see it as a culture you must nurture and not as a step-by-step method you can break down into granular details.

This is the era of blurring lines between business and IT. Today, every business has to operate, to a great extent, as a technology business. Software development becomes a great capability for businesses of all kinds, and not only IT companies. Companies are always under pressure to:

• Do things faster
• Be agiler in their practices
• Maintain strong security practices
• Deliver projects reliably

Key DevOps practices

DevOps focuses immensely on standardizing processes that cut across software development and operations. Some of these processes are:

• Application deployment and testing
• Automation of configuration management
• Application version control
• Continuous integration to automate code builds
• Fast feedback loops to enable quicker iterations of low-risk releases

The purpose of all these processes is not restricted to IT benefits. In fact, each of these processes helps companies develop software in a quicker and more reliable manner, ensure system security, make sure that frequent upgrades don’t cause problems to end users, and guarantee that employees are able to make use of advanced software features to do their work better.

DevOps as a culture

Considering how DevOps spreads across two crucial enterprise functions (development, and operations), it’s clear that you need leaders to manage it. IT organizations, as well as business organizations, need to get buy-in from the top, as well as the bottom, to make DevOps a success factor. Your DevOps staff needs to be comprised of people with tremendous communication skills. Also, these people must be prepared for challenges and possess cutting-edge people-management skills. DevOps aims to change the way people think, develop, and execute software. When people from development and operations have to wear different hats, they are able to see and imagine their roles in the larger scheme of things.

How to create and nurture the DevOps culture

Understanding DevOps means understanding the crucial components of the DevOps culture.

Super-quick and effective communication

DevOps team members need to be highly communicative, using multiple methods of communications, right from email to IM, telephone to video conferencing. The speed of info exchange is crucial to the success of DevOps, which requires your people to be quick decision makers in choosing whom to communicate to, and which medium to use for communication. Staying in touch, responding to messages quickly, and broadcasting the message to all stakeholders — that’s what keeps the DevOps spirit alive.

Problem-solving attitude

DevOps won’t exist if your development and operations people aren’t already brimming with a problem-solving attitude. Because DevOps erases the lines and definitions of responsibility distribution among different teams, it calls upon people to think “solution” and not “problem.” When unanticipated issues occur, your DevOps teams need to be able to prioritize and solve them in a manner such that business continuity is ensured. Instead of finding the “best person for the job,” DevOps call out people to think of how they can contribute to the solution.

Flexibility

We’ve touched upon the importance of agility in DevOps. To achieve process agility, DevOps requires the organization’s IT management to be more flexible that it’s traditionally been. IT project management is nefariously rigid; DevOps culture has no place for this rigidity. Your project management office, instead, needs to lead by example, promoting and rewarding people who show intelligent flexibility in their work and to drive business benefits of out of IT practices.

Let’s look at some more important aspects of how understanding DevOps will help you implement it as a cultural shift and not merely a mechanism.

To capture the benefits of DevOps without exposing critical infrastructure and data, organizations need a deliberate strategy around Privileged Access Management (PAM). The following three best practices — drawn from guidance by Morey Haber, CTO at BeyondTrust, as reported by DevOps.com — provide a practical starting point for teams building security into their continuous delivery pipelines.

1. Discover and Inventory All Privileged Accounts and Assets

Effective PAM starts with visibility. You cannot manage or protect privileged accounts that you do not know exist. In a DevOps environment, this is easier said than done: automation scripts, compiled executables, CI/CD tools, and infrastructure-as-code all accumulate credentials in ways that are difficult to track.

“If it’s embedded in other runtimes or it’s hard-coded into compiled executables, that discovery is the hardest,” said Haber. Despite the difficulty, a complete inventory is non-negotiable. Teams need clear answers to questions such as:

• Which tools are executing automation, and what privileges have been assigned to them?
• Is the automation running as a PowerShell script, embedded in Jenkins, or somewhere else entirely?
• Who is running each process, and when?
• Where are automation scripts stored, and where are their embedded credentials stored?

That last point matters because wherever credentials are stored determines how easily they can be tampered with or exfiltrated. The audit must cover the full lifecycle of a privileged credential: how it is edited, stored, retrieved, and used at runtime. Without this foundation, all other PAM controls are incomplete.

2. Manage Shared Secrets and Hard-Coded Passwords

Hard-coded passwords are one of the most dangerous anti-patterns in credential management. Development teams may rigorously eliminate hard-coded credentials from production application code, yet leave them scattered throughout the supporting infrastructure — build servers, deployment pipelines, configuration files — simply because it is faster to do so.

Similarly, shared accounts are a common shortcut: a single set of credentials is used by multiple developers or automation processes to reduce friction. The trade-off is severe — shared credentials destroy auditability. When multiple actors use the same account, there is no reliable way to attribute a specific action to a specific person or process.

“Whether that is using some type of proxy technology, a VDI environment — anything — you don’t want people logging in directly with shared accounts because you can’t trace that,” Haber explained.

The remedy is a control plane that enforces unique credentials for both human users and automated processes. Every action — whether taken by a developer or by a CI/CD pipeline — should be traceable back to a unique identity. This is essential for:

• Compliance: Regulatory frameworks typically require demonstrable audit trails for privileged access.
• Forensics: In the event of an incident, investigators need to reconstruct exactly what happened and who or what caused it.
• Pipeline integrity: Knowing which automated process triggered a change is fundamental to maintaining a trustworthy software delivery “factory.”

Secrets management tools (such as HashiCorp Vault, AWS Secrets Manager, or Azure Key Vault) can help teams retire hard-coded passwords and rotate credentials automatically without breaking automation workflows.

3. Enforce the Principle of Least Privilege

Even when credentials are properly inventoried and uniquely assigned, over-provisioning remains a serious risk. The principle of least privilege holds that every user account and every automated process should have only the minimum level of access required to perform its specific function — nothing more.

“We see credentials being given to DevOps teams all the time that are way, way overprivileged,” said Haber. “It should come down to the least privileged model — that you’re giving enough privileges for the DevOps process to work, but not overprovisioning so that if any process or account is compromised, it doesn’t jeopardize the rest of the environment.”

In practice, this means:

• Scoping service accounts tightly: A deployment pipeline that only needs to push artifacts to a specific repository should not have write access to the entire infrastructure.
• Using just-in-time (JIT) access: Grant elevated privileges only when needed, for a defined time window, then revoke them automatically.
• Auditing privilege assignments regularly: Access needs change as teams and pipelines evolve; periodic reviews catch permissions that have become unnecessarily broad over time.
• Applying least privilege to non-human identities: Automation accounts, service accounts, and CI/CD pipeline identities need the same scrutiny as human user accounts.

Putting It Together: PAM as a DevSecOps Foundation

These three practices — discovery and inventory, elimination of shared secrets and hard-coded passwords, and enforcement of least privilege — form an interconnected foundation. Discovery tells you what you need to protect. Eliminating shared secrets ensures each identity is traceable. Least privilege limits the blast radius if any credential is compromised.

Building PAM controls into the continuous delivery pipeline from the start is far less costly than retrofitting them after a breach. For DevOps teams pursuing a DevSecOps posture, privileged access management is not a compliance checkbox — it is a core engineering concern.

Original reporting by DevOps.com, based on an interview with Morey Haber, CTO at BeyondTrust.

Why Blockchain and DevOps Belong Together

Blockchain and DevOps are two of the most transformative concepts in modern software engineering. Each delivers meaningful benefits on its own: blockchain provides a tamper-resistant, decentralised way to record and share data, while DevOps accelerates delivery, improves quality, and breaks down organisational silos. Together, they can offer compounding tactical and strategic advantages—but only if the inherent challenges of combining them are addressed deliberately.

What Is Blockchain?

Blockchain is frequently conflated with cryptocurrency, but it is a distinct technology with applications far beyond digital currencies. At its core, blockchain is a distributed ledger: a shared, append-only record of transactions that is maintained across a network of participating nodes rather than by a single central authority.

Three foundational concepts underpin how blockchain works:

• Peer-to-peer networking: Nodes communicate directly without a central server, which removes single points of failure and control.
• Public-key cryptography: Each participant holds a cryptographic key pair that authenticates transactions without exposing identity, providing pseudonymity and verifiability.
• Distributed consensus: Agreement on the validity of a new block is reached across participating nodes through a defined protocol (such as proof-of-work or proof-of-stake), rather than by trusting a central authority.

Every transaction is packaged into a data block and appended to the chain of all previous blocks. No single party owns or controls the ledger; all participants can inspect the full chain, ensuring transparency and integrity. Critically, blocks already added to the chain are immutable—they cannot be modified or deleted, only extended.

Key Advantages of Blockchain

• Elimination of reconciliation overhead: Data validated across all nodes does not need to be cross-checked between siloed systems.
• Auditability: The full transaction history is always available and verifiable by any participant.
• Tamper resistance: Immutability makes retroactive alteration computationally impractical.
• Reduced intermediary costs: Decentralised architecture can remove the need for trusted third parties in many workflows.

Challenges of Adopting Blockchain in a DevOps Organisation

The promise of blockchain is real, but so are its adoption barriers. Organisations must navigate three interconnected challenge areas: culture, process, and tooling.

Cultural Challenges

Executive sponsorship and adequate resourcing are prerequisites for any blockchain initiative. Without champions who can secure budget and drive cross-functional alignment, blockchain projects typically stall before delivering value.

Agreement on technology choices and governance is harder when blockchain networks span organisational boundaries—which is precisely when blockchain delivers the most value. The absence of widely accepted industry standards makes consensus-building harder and increases the risk of building on incompatible or short-lived foundations.

Skilled blockchain developers remain scarce. Organisations should plan for longer hiring timelines and competitive compensation, or invest in upskilling existing engineers.

Process Challenges

Blockchain is still a relatively young discipline. Management frameworks, design patterns, and role definitions are not yet as mature as those available for conventional application development. Teams must define from scratch how blockchain fits into the software development life cycle (SDLC), what success metrics to track, and how to structure cross-functional collaboration.

Key process questions that organisations must answer include:

• How will smart contracts be reviewed, tested, and promoted through environments?
• How will network topology changes be managed as the node count grows?
• What performance and latency thresholds are acceptable, and how will they be monitored?
• How will consensus protocol changes be deployed without disrupting running nodes?

Tooling Challenges

The blockchain developer toolchain has historically lagged behind that of mainstream software development. A mature blockchain development environment requires:

• An integrated development environment (IDE) with language-aware linting and plug-ins
• Build tools and compilers for smart contract languages
• Automated deployment tooling for contracts and node configuration
• Comprehensive testing frameworks (unit, integration, and property-based)
• Debugging and logging tools adapted to distributed execution
• Security auditing tools capable of detecting common smart contract vulnerabilities
• Performance and analytics instrumentation

While tooling has improved since blockchain’s early days, gaps still exist compared to mainstream languages and platforms. Teams should audit the available toolchain for their chosen blockchain platform before committing to it.

Infrastructure Cost Realities

Despite being rooted in open-source technology, running a blockchain network is not free. Each transaction is processed on every participating node, which means hardware, networking bandwidth, electricity, and compute costs multiply as the network grows. Smaller organisations should model these costs carefully before assuming that blockchain is an economical alternative to a centralised database for their use case.

Applying DevSecOps Principles to Blockchain Development

Several core DevOps and DevSecOps principles map directly onto the challenges of blockchain development and can accelerate both adoption and security posture.

Break Down Silos

DevOps is built on cross-functional ownership. Blockchain projects benefit from the same philosophy: security engineers, infrastructure engineers, and application developers should collaborate throughout the SDLC rather than handing off work over walls. Shared ownership reduces the risk that a critical vulnerability in a smart contract survives to production.

Shift Security Left

Smart contracts are particularly unforgiving of security flaws: once deployed to a public blockchain, a buggy contract may be impossible to patch without complex migration strategies. Integrating security testing early in the development cycle—linting for known vulnerability patterns, automated static analysis, and peer code review—catches defects when they are cheapest to fix.

Automate Testing

Blockchain applications require all the standard test types—functional, integration, security, and performance—plus additional specialised testing:

• Smart contract testing: Verify that contract logic behaves correctly under all expected and edge-case inputs, including re-entrancy and integer overflow scenarios.
• Peer and node testing: Confirm that the network reaches correct consensus and maintains ledger integrity as nodes join, leave, or behave maliciously.
• Performance and latency testing: Measure throughput and latency under realistic network sizes, transaction volumes, and consensus protocols. Results will vary significantly with these parameters.

Measure and Iterate

DevOps emphasises continuous feedback loops. For blockchain, this means tracking metrics such as:

• Mean time to detect (MTTD) and mean time to remediate (MTTR) security vulnerabilities
• Transaction throughput and confirmation latency in staging and production
• Node synchronisation health and consensus failure rates
• Smart contract gas consumption (where applicable) as a proxy for efficiency

Make these metrics visible to every team member. Shared visibility drives shared accountability, which is the cultural foundation of both DevOps and successful blockchain programmes.

Where Blockchain and DevOps Converge: Strategic Value

Blockchain’s core properties—immutable audit trails, distributed trust, smart contract automation, and shared ledgers—align with what many DevOps-mature organisations are already trying to achieve in their supply chains, compliance workflows, and partner integrations. DevOps provides the delivery infrastructure: the CI/CD pipelines, infrastructure-as-code, automated testing, and monitoring needed to develop and operate blockchain applications reliably.

The combination is most powerful in use cases that require trust between parties who do not share a common authority: multi-party supply chain tracking, financial settlement, identity verification, and software provenance. In each scenario, DevOps practices ensure the blockchain application can be iterated quickly and safely, while blockchain provides the trust layer that no single party controls.

Getting Started: Practical Recommendations

1. Validate the use case first. Not every problem benefits from a blockchain solution. Confirm that decentralisation, immutability, or multi-party trust is genuinely required before committing to the added complexity.
2. Secure executive sponsorship early. Blockchain initiatives cross organisational and sometimes company boundaries. They will stall without senior advocacy and budget.
3. Choose a platform with an active ecosystem. Evaluate tooling maturity, community support, and enterprise adoption before selecting a blockchain platform.
4. Apply DevSecOps from day one. Embed security reviews, automated testing, and compliance checks into the CI/CD pipeline from the first sprint.
5. Model infrastructure costs realistically. Account for node operation, networking, and energy costs at projected network scale before building a business case.
6. Invest in developer education. The blockchain skill gap is real. Budget for training and consider partnering with specialist consultancies to accelerate the first project.

Conclusion

Blockchain and DevOps are complementary rather than competing paradigms. DevOps delivers the speed, quality, and collaboration model that complex blockchain projects demand, while blockchain extends DevOps-built systems with capabilities that centralised architectures cannot match. Organisations that invest in resolving the cultural, process, and tooling challenges—applying DevSecOps principles throughout—are best positioned to extract durable value from both.

The speed of business today demands that the development and deployment of applications is fast-moving, with frequent yet error-free releases. That’s why the adoption of DevOps is trickling down from Amazon, Facebook, Google and the other usual suspects to every company that relies on technology to drive its communications or sales with users.

The database is now entering the picture, too, because when front-end applications are changed, there often needs to be a change at the back end to accommodate it. Redgate’s 2018 “State of Database DevOps Survey” found that more than one-third (35 percent) of organizations are deploying database changes either daily or more than once a week.

Some are working even faster, with companies such as online travel provider Skyscanner moving from releasing database changes once every six weeks to 95 times a day when it first introduced database DevOps—and increasing the frequency since.

This accelerated speed of delivery is a key benefit of database DevOps, with 28 percent of respondents to the DevOps survey quoting it as the biggest advantage. But as releases become more frequent, so, too, does the chance of something going wrong, along with the difficulty of pinpointing the exact cause in a large, complex, fast-changing environment.

Database DevOps Best Practices

Even with the most comprehensive testing, issues can occur. And as more and more developers now work across both the application and database, and with larger, more disparate teams, companies need the ability for everyone to drill down quickly to identify and fix issues.

Achieving this means bridging the database and application worlds by focusing on three key areas.

Deployment Choice

Different developers and database administrators will use specific tools to deploy database changes. This can depend on their experience, skills or simply that they have a preference for a particular way of working.

You need to be able to allow this choice, but without affecting the ability to track down issues. Therefore, ensure that you have a monitoring tool in place that can pinpoint not just when and where changes were made, but which tool was used. This will help quickly find the root causes of any issues, by providing the full context around the problem.

A Single View

Data is the lifeblood of many businesses—companies now have more and more databases within their organization, often spread across different locations. However, no matter how complex your IT environment or the size of your estate, you need to be able to monitor database performance from a single place. That way, you can easily identify issues and pinpoint precisely which database is being affected.

Wherever a database is, and whatever the distance it is being monitored from, the monitoring also needs to be in real time, with updates in seconds, not minutes, providing the reassurance that you always have the latest picture of what is occurring. This enables you to become more proactive, spotting developing trends and issues such as deadlocks or blocking processes, before they affect your users or overall performance. Having a single, up-to-date view is therefore essential when integrating the database into DevOps processes.

Fast, Customizable Alerts

The ability to make frequent, reliable, repeatable changes to the database as well as the application is integral to DevOps flows, and monitoring them means evaluating dozens of different parameters constantly.

Clearly, it’s impossible to keep an eye on everything that is happening, even when events are condensed to a single monitoring screen. Therefore, ensure you’re able to easily create and customize alerts for specific parameters that are important to your business, so that they don’t get missed. This enables you to better understand DevOps processes and ensure that you have early warnings of potential complications, no matter where they occur.

For the database to be an integral part of DevOps, organizations need a complete, interoperable view of their entire estate so  they can track down problems, monitor processes and spot issues before they affect users. That view should be equally available to the whole DevOps team, including developers, so they can see immediately what effect their latest change is having and take the appropriate action to fix any breaking change while it is still fresh in their mind.

Only then can databases take their key place in DevOps flows, eliminating bottlenecks and speeding up development to meet fast-changing business needs.

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.

Why DevOps Is Reshaping Software Development

Developing software has always been about creating products that work. But “working” is a relative term when you need to satisfy hundreds of thousands—or even millions—of users across vastly different disciplines, devices, and contexts.

This pressure is a core reason why DevOps is supplanting the traditional waterfall model of software delivery. Once you have basic functionality in place, it makes far more sense to ship continuous, incremental improvements quickly than to wait a year or more for the next major release—which may or may not address the problems users actually care about.

But this is a significant cultural and technical shift for developers who have spent their careers in environments that valued consistency and stability over speed and adaptability. Getting the DevOps model right from the outset is proving critical for enterprise engineering teams.

Removing Friction from the Developer’s Workflow

“Before DevOps practices, the burden of deploying a piece of code to production was on the developer and the release engineer,” said Fred Simon, co-founder and chief architect at JFrog. “The goal of all the tooling and DevOps practices is to remove these burdens by making the supply chain fully automated, enabling the developer to concentrate on writing code, releasing, and getting feedback as fast as possible.”

In practice, this means DevOps must actively improve the development experience—not simply add process overhead. A convoluted pipeline that frustrates engineers and slows delivery defeats the purpose entirely.

Simon frames the shift this way: DevOps converts a single, monolithic release cycle into a steady series of micro-deliveries, each small enough to ship fast and receive rapid feedback. JFrog describes the ideal end state as “liquid software”—a continuous flow of small changes delivered seamlessly to end consumers, whether those consumers are enterprise banking platforms, mobile apps, or embedded firmware in smart devices.

“The key to fast release cycles and easy updates is small changes that are delivered quickly and seamlessly to the end consumers. Ideally, the flow is continuous, like flowing water.”

— Fred Simon, Co-Founder and Chief Architect, JFrog

Choosing Open, Interoperable Tooling

One of the most practical steps an organization can take to smooth the DevOps transition is to get its toolchain right. According to Simon, many enterprises are moving toward open solutions that foster diversity while maintaining a common, integrated pipeline.

“Rather than one dominant DevOps platform, most DevOps tool providers are actually eating their own dog food by using DevOps best practices to release fast, with open systems that easily integrate with one another.”

The integration chain should span the full lifecycle: version control (such as Git), CI/CD platforms, code review tools, testing frameworks, and runtime environments. The harder it is to hand work between these stages, the slower the overall system becomes—and the greater the chance that security vulnerabilities and regressions go undetected.

Multi-Language Development and REST APIs

A well-designed REST API is a powerful enabler in a DevOps pipeline because it allows development teams to select the right language and runtime for a given service without coupling the entire organization to a single technology stack.

“The fact that today’s developers are using more than one language, and everyone is using the language that fits their environment and needs, is pushing innovation, speed of development and software creation forward,” Simon noted. “Multiple development teams using different languages can work on the same product and provide a single experience to the end user—which is something very recent and quite amazing for our industry.”

On the operations side, however, the challenges are different. Simon points out that the primary friction on the ops side centers on runtime platforms and orchestration tooling: Java VMs, Go runtimes, Kubernetes, Docker, and the monitoring and automation layers that tie them together at scale.

Key Development Tools for DevOps Pipelines

The following tools represent widely adopted solutions across the development stages of a modern DevOps pipeline. This is not an exhaustive list, but these tools address common needs from build management and version control through project tracking and environment consistency.

Apache Maven

Apache Maven originated as part of the Apache Turbine project and was designed to simplify build and dependency management for Java-based projects. It provides a uniform build system, a best-practices project structure, and declarative dependency resolution via a Project Object Model (POM) file. Maven’s goal is to give a developer a complete, reproducible project build state in a predictable way.

Bitbucket

Bitbucket is a distributed version control platform designed for team-scale code collaboration. It supports both Git and (historically) Mercurial repositories, and emphasizes pull requests for code review, granular branch-level access control, and integration with CI/CD pipelines. It is commonly used in organizations already invested in the Atlassian ecosystem alongside Jira and Confluence.

GitHub

GitHub is a web-based Git hosting platform supporting both open-source and private repositories. It provides lightweight code review via pull requests, project management boards, GitHub Actions for CI/CD automation, and an extensive ecosystem of integrations. It has a very large global developer community and is widely used as the default home for open-source software projects.

JFrog Artifactory

JFrog provides artifact lifecycle management as a core DevOps infrastructure component. Its flagship product, Artifactory, acts as a universal package repository supporting formats including Maven, npm, Docker, Helm, and others. It offers binary transparency, release management, and integration with major CI servers, enabling organizations to track exactly what artifact is running in production at any point in time.

Jira

Jira, developed by Atlassian, is a project tracking and issue management platform tailored for agile software teams. It supports Scrum and Kanban boards, sprint planning, backlog management, and real-time reporting. It integrates with most major development and CI/CD tools, making it a common central coordination point for engineering teams practicing iterative delivery.

Vagrant

Vagrant, developed by HashiCorp, automates the creation and provisioning of development virtual machines using a simple declarative configuration file (Vagrantfile). It supports providers including VirtualBox, VMware, and cloud platforms. Vagrant’s primary value in a DevOps context is ensuring that every team member works in an identical, reproducible local environment, reducing the classic “works on my machine” class of bugs.

Visual Studio

Visual Studio is Microsoft’s full-featured integrated development environment (IDE), supporting development for Windows, web, cloud, Android, and iOS targets. It includes debugging, profiling, testing, and source control integration tools. Visual Studio Code, the lightweight cross-platform sibling, has become especially popular in DevOps workflows due to its extensibility and broad language support.

webpack

webpack is an open-source JavaScript module bundler. It builds a dependency graph of a project’s modules and assets, then bundles them into optimized output files for the browser. It is highly configurable via a JavaScript configuration object, supports loaders and plugins for transforming assets (such as transpiling TypeScript or processing CSS), and is a standard part of modern front-end build pipelines.

Building a Pipeline That Works for Developers

The underlying theme across all of these tools and practices is developer experience. A DevOps pipeline that automates toil, surfaces feedback quickly, and integrates cleanly across the software lifecycle enables developers to focus on what they do best: writing code that solves real user problems.

The development side of DevOps is not just about tool selection. It requires deliberate decisions about pipeline architecture, artifact management, environment reproducibility, and cross-team communication. Done well, these decisions compound over time—producing faster releases, fewer production incidents, and a measurably better user experience.