Hello everyone, welcome to the wonderful part of the programming session on that where we had left the most important sections of GitLab CI/CD. Now, we are going to start from where we had left on the accounts of the creation of the .gitlab-ci.yml file or how to run the CI/CD pipeline. If you want to learn from scratch so, you may visit previously happened articles for proper assistance. So, let’s start with the rest part of the topic that describes most parts of the GitLab CI/CD.

Create .gitlab-ci.yml file

Step 1: Add .gitlab –ci.yml in the root folder of your projects/repo

I’m starting from where I had left the rest part of creating the GitLab YML file. So, after saving your project, you will go to your desktop and you will press command shift and dot on your keyword because this will be your hidden file you will copy that and move it to your projects folders you will have created a project and in the project folder, you had created a folder called GitLab in GitLab you had your first project. The same thing you can do in windows so the steps will remain the same you can just copy it in your projects folder wherever it is so it will be there your first projects you can just copy that file there so, you can actually copy that file there. Now, this was step number one and you will also copy the contents of that file so, you will see it you can also copy that in notes that will be the file you will have created this was step no. one and now, the step no. two is

Step 2: commit and push file to GitLab repository  

So, now because you have added that in your project you just need to commit and push in your gitlab repository you will have already know the commands for committing and pushing so, open your terminal there if you will on windows you can open git bash or the command line and first you will change the location to your project so, you will see say CD and go to your project folder and there if you say git status you will see you are getting that file will not yet added so you will say git add dot and all these comments you have learnt and then you will say git commit so if you again say git status you would see it in green so you will see now it will be added and then now you will say git commit –m “ added .gitlab-ci.yml file  and now you will push say git push –you origin/ master so you have you will give the URL instead of origin so there you will add the URL of your repository you will go to gitlab and go to your project and there you will click on clone and copy this HTTP link and provided there and hit enter if you will not have added your credentials there and you believe you have done it on windows you wil go to your command line and go to the location of your project there and now you will say git status and your file will be having there. You will say git add and git status again you will now say git commit –m “added .gitlab-ci .yml” and you would say git push –u origin master or you will say git push –u and give the link there of your first project they would push the changes if you go and check your project now you would have your GitLab CI ml file there so you will see now it will be there so you had pushed the changes.

Run CI/CD pipeline

 Now, the next thing is step no. three will be

Step 3: Create GitLab runner for the projects

So, if you go to your project you will go to your project go to settings and there you would go to CI/CD and in the runner, if you expand you will see you will have this runner created earlier so this runner will have already created so, this we have already done step no. three. Step no. four will also make sure that the runner that you are using has your tag that you will have given in your ml file so you will have your tag there CI that you will have given in your ml file now step four will be

Step 4: Start the runner  

Now, you will start the runner so, you will also start from your Mac or windows anywhere you will have learned this how to start so you will just say there brew service says start GitLab you don’t know if you will go to GitLab runner so, there is the documentation for GitLab runner so, you are on windows you will see there you will have for Mac for windows all these things you will have. And you can use this so if you are on windows you can just run this GitLab runner .exe start so there that will have already started so that is fine so you can start and stop but that is fine it will be started so once you can start that or now then step no. five will be

Step 5: Make any change in the project >> commit >> push

 So, basically what you will have done is you have added this CI job or you had added this GitLab CI YML file in your project that will now check for any commits and push in the project whenever you do any commits it will if you will have runner running if you will have GitLab runner running having the same tags like what you will have given in the file. For example: you will have given CI there then it will start the job whatever you will have given in the CI file in the gitlab CI file so now you will make some change there you will go there to your project and you will just some file there or you will not add a folder you will just add a file you will say new file you will say that is test 1.txt that will be test file and you will save and close it if you will go to your command line if you will say git status that will be added so you will say git add you can say test1.txt or you can say dot for everything and you will say git commit –m “added test1 .txt” and now you will have to push so you will say git push –u and the URL and master and once you do this when you will go to your projects there and you will go to CI/CD and once you will go there you will see this job’s getting triggered in the job section and you will push that if you will go there and refresh or you will go to the jobs you will see it is now there it will say pending so this is that will run now if you will go inside you will see that is a demo job one that you will have created in your gitlab CI file if you will click there you will see this is running and that will be now passed and you will see it is running hello World so it will say echo hello world and it will be printing hello world and you will see the locks there you will scroll from there that was a very small job so you don’t have lot of logs you can erase the job log you can see the complete raw log all that you can do so now on every commit and push in project it will check if you will have gitlab runner running and you will have this CI file the gitlab CI file it will run the file and whatever jobs you will have added whatever scripts you will have added it will run accordingly so from now on words whenever you will make changes it will triggers that jobs whatever you will have configured in your file so this is in very basic way how you can get started  with gitlab CI/CD of course there are lot of complex yml file set you can create as per your needsif you will go to the gitlab documentation you will see examples and samples of a lot of yml file and you can check them and see how you can use them so, this was the beginning of gitlab CI/CD at a very basic level so, I hope all this will very useful for you please do a hands-on and try some more jobs running some more jobs and gitlab CI/CD.                

Hello everyone, you know the different phases of programming and about its evaluation on that, it is performing their task. So, we are going to learn about Gitlab CI/CD from very basic & scratch and if you don’t have primer knowledge on the above topic so, don’t worry, we will go up step by step. And in this lesson, we will engage with various topics like what is Gitlab CI/CD? We will create a .gitlab-ci.yml file, this is a file where we add our jobs and create a pipeline and I will show you how to do that then we will run CI/CD pipeline in Gitlab so, this is going to be very easy and interesting.

What is Gitlab CI/CD?

Let us learn about what is Gitlab CI/CD? So, you will go to Google & search for continuous integration and delivery. You will go to images and select one image there. You will see there, you will have continuous integration then CI stands for continuous integration so, the continuous delivery is CD and again, we will call continuous deployment is CD so, this is what we call CI/CD. And there, if you will look at continuous integration. There will be a process where you will have these three jobs and processes, chained and linked with each other. Why is it will be linked? I mean, if the first dev process or dev job runs and passes. And it will trigger the next job that is in the test job application test. If the passes it will trigger then the next job that is an integration test and you will have our continuous integration pipeline completed so this is what is called as CI or continuous integration and if this all these jobs in this cycle are chained with each other and our automated triggered we call it as a continuous integration system now if you also add an acceptance test job or acceptance test process which is also automated and linked this becomes a continuous delivery because after this step our application or our product will be ready for delivery so this becomes continuous delivery and if we also add our deployment job or a deployment process that will reply to the production and all these jobs are automated and chained and can be automatically triggered then this becomes a continuous deployment pipeline so this is what we call as continuous integration and continuous delivery and continuous deployment. Now, in GitLab as well we have this corners integration delivery and deployment, and here this GitLab CI is an open-source CI service or a continuous integration service included with GitLab and by default, this is integrated with every in your GitLab since version 8.0 so, if you are using version 8.0 or above GitLab then in all your projects by default you will have GitLab CI integrated and enabled by default and to go to the settings and to configure GitLab CI you should be a maintainer or our owner or an admin on your project so, you go to your GitLab account if you just go to your Gitlab you will sign in so, you can see there your projects and if you see the projects there you will be maintainer on one projects and owner on the other projects so, you can configure GitLab CI/CD so, this is what GitLab CI/CD is now.

Create .gitlab-ci.yml file

Actually, create a GitLab CI/CD pipeline, you will have to add a file called .gitlab –ci.yml so, yml files or ml is a format of writing text files, and like you have JSON and XML. Ml is also a format and I will show you that how to create it and you have to create this file in the root folder of your project or your repository so, whatever the root folder is and you will be creating this file in the main folder or the root folder of your project. Now, before we start creating this file so, this is used to configure our pipelines and in every project, you will be having, you will be adding this file where you can add the details and it will define the structure and order of the pipeline and once you create it you will know exactly what you mean do not worry if there is there are some things that you are not getting once you create the file and run it you will know exactly what it means? So, you will create a file now if you are on Mac, you can use any text editors like you have text editors on Mac then you have text wrangler anything will work if you are on windows, you can again use notepad or notepad ++ or in fact, you can use any editor but you have to make sure the syntax is correct and I will show you how you can check the syntax so, you will open your window with notepad++. You will open a new file and on your mac, you will open your text wrangler file so that you can create it there so to create a file you will just give some name of the job you will say that is demo job one. So, you will say demo job one any name you can give there and give a colon go to the next line and you will give five spaces so, you can give any number of spaces but you will have to be consistent with that so now you will say tags and there give a colon again go the next line give two spaces and you will give a hyphen and spaces and I can give any tag here now tags are important because in your gitlab you will be having the gitlab runners and they have some tags and the text should match only then the gitlab runner will run this particular file so if you go to my project on gitlab and there if you go to settings and go to CI/CD and you have runners there you will click on expand and you can see you had created this runner earlier and you had given this these tags there CI and SSS so you should be having one of these tags so that the runner can pick up this file and run it so, you will say CI there and you will go again to next line now again you will give there script and this is in line with tags again go to next line two spaces – and there you can write any command or script you want to run so, you will say hello echo hello world so they should run this command in this job this is a very simple file where you are running that script echo hello world so they should print hello world on the console and now you can also do the same thing on your windows on the notepad so you can create it like this and now you will have to make sure the syntax is correct and for that you can copy the contents and you can just go to google and search for any ml file validator utility or application and you will see that you have yam lint, yaml validator online this all you will go to yaml lint and paste your contents of the ml file and there you have to go button you will click there go and check and yes everything is fine it will says it is a valid YAML so that is fine you will now just save this so you will save this file and you have to make sure the name there should be .gitlab –ci.yml as of now you will save on desktop but you will move it to your project folder so, you will say as it is. I hope this will more authenticate your performances and give you a hike in the sections of Gitlab CI/CD.

Related video:

Security Becomes the Next Frontier for DevOps Teams

DevOps transformed how engineering teams ship software by uniting development and operations around shared tooling, automated pipelines, and a culture of continuous delivery. The next major challenge those teams face is security. In most organizations, security controls still operate as external gates — checklists and approval queues that slow releases down rather than integrated processes that move at the pace of the pipeline.

Gartner analysts predicted that the share of DevOps teams integrating security practices would grow from roughly 40% to approximately 90% by 2022, reflecting a broad industry shift toward what is now commonly called DevSecOps. Whether that specific forecast materialized on schedule is worth verifying, but the directional trend is widely corroborated by industry surveys and practitioner experience.

Why Integrating Security Into DevOps Matters

When security is treated as an afterthought, vulnerabilities are caught late — after code is already in production — when fixes are expensive and the blast radius of a breach is large. Embedding security throughout the software development lifecycle provides several concrete benefits:

• Earlier defect detection: Finding a vulnerability at the code-writing stage costs a fraction of what it costs to patch a production system.
• Reduced data breach risk: Teams that understand the threats their applications face can defend against the right risks in the right places.
• Continuous feedback: Security tooling wired into a CI/CD pipeline provides developers with actionable signals at the same cadence as build and test results.
• Scalability: Automated security controls grow with the team; manual review queues do not.

Shifting Security Left — and Extending It Right

The DevSecOps model asks teams to do two things simultaneously: shift left by integrating security earlier in development, and extend right by maintaining security visibility into production operations. As Jeff Williams, CTO of Contrast Security, explained:

“DevOps has dramatically improved the way that teams ship software quality and performance. DevSecOps has similar benefits for security, but requires changes in culture, people, process, and technology. Successful teams are both shifting security ‘left’ into development as well as extending ‘right’ into operations, and they’re leveraging instrumentation-based approaches like IAST and RASP instead of traditional outside-in scanners and firewalls.”

Jeff Williams, CTO, Contrast Security

Training is a foundational part of this cultural change. As Calvin Lo, program manager for training at Security Compass, noted:

“Training and security education is another crucial piece of the puzzle. By understanding threats and weaknesses of how applications are hacked, software professionals can gain direct expertise and then prevent many common problems from happening in the first place.”

Calvin Lo, Security Compass

Key Security Tools for the DevOps Pipeline

Several categories of tooling are particularly well suited to the DevSecOps pipeline. Each addresses a different phase of the development and operations lifecycle.

Interactive Application Security Testing (IAST)

IAST instruments a running application — much like APM tools such as New Relic, Dynatrace, and AppDynamics instrument it for performance — to detect security defects in real time without requiring dedicated security expertise on every team. Because IAST observes the application from the inside, it can identify issues that network-level scanners miss, including:

• SQL Injection: User input reaching an SQL command without proper validation.
• XML External Entity (XXE): An XML parser configured in a way that exposes local files to external users.
• API flow mapping: Understanding how data moves through an application to inform automated threat modelling.

Software Composition Analysis (SCA)

Modern applications depend heavily on third-party and open-source libraries. SCA tools scan those dependencies to identify known vulnerabilities (referenced in databases such as the NVD) and flag potential licence conflicts before they reach production. Tools like OWASP Dependency Check are well-established open-source options in this space.

Runtime Application Self-Protection (RASP)

Where a Web Application Firewall (WAF) watches network traffic from the outside, RASP lives inside the application runtime and can observe exactly how data is used by the code. This gives it the context to distinguish a real exploit attempt that reaches sensitive logic from an irrelevant crawler request — a distinction that WAFs structurally cannot make. RASP can block attacks while providing developers with information that is directly actionable at the code level.

Chaos Engineering

Chaos Engineering deliberately introduces failures — degraded dependencies, network partitions, resource exhaustion — into a system before unplanned incidents do. While not a security tool in the traditional sense, it builds resilience and helps teams understand failure modes that attackers might exploit, such as inadequate error handling or insecure fallback behaviours.

Why Traditional Security Tools Struggle in a DevOps Context

Many legacy security tools were designed for slower, waterfall-style release cycles. They create friction rather than feedback when dropped into a modern pipeline:

• Static Application Security Testing (SAST): Traditional static analysis can be slow to run and prone to high false-positive rates, particularly when it cannot traverse non-imperative code flows such as dependency injection or inversion-of-control frameworks. Triaging false positives requires security expertise that most development teams do not have in-house.
• Web Application Firewalls (WAFs): WAFs can see network traffic but lack architectural visibility into what the application code actually does with that data. Operations teams end up flooded with alerts from irrelevant crawler traffic, while developers get statistics (for example, SQL Injection block counts) that may be meaningless if the application uses a NoSQL database. The critical question — could this attack actually succeed? — often goes unanswered.

The core challenge for any security tooling in a DevOps pipeline is the combination of speed, accuracy, and appropriate skillset requirements. Tools that are too slow, too noisy, or too complex to interpret will be ignored or bypassed by teams under delivery pressure.

Where to Start: Frameworks and Resources

Teams looking to build or mature a DevSecOps practice have several well-established starting points:

• Microsoft Security Development Lifecycle (SDL): A process framework that prescribes security activities at each phase of development.
• OWASP: The Open Web Application Security Project provides free guidance, checklists, and tools including OWASP Dependency Check and the OWASP Top Ten.
• Gartner’s Application Security Hype Cycle: Useful for understanding the maturity and trajectory of specific security technologies.
• Free training: Vendors and non-profits offer role-specific security training for both managers and technical practitioners. Security Compass’s SD Elements platform is one example; OWASP and SANS also provide widely used educational resources.

Summary

Security in DevOps is not a single tool purchase or a one-time audit. It is an ongoing discipline that requires the same cultural investment as DevOps itself: shared ownership, automated feedback, continuous improvement, and a willingness to change existing processes. Teams that treat security as an integrated engineering concern — rather than an external compliance function — are better positioned to ship software that is both fast and defensible.

Why Microservices Matter—and Why They’re Hard

Containers have captured the imagination of both infrastructure teams and application developers. Infrastructure engineers see containers as a more efficient way to allocate virtual compute resources. Developers see them as the path to building disaggregated, distributed, next-generation applications. Decomposing traditional monolithic applications into discrete, independently deployable components promotes code reuse, enables more granular scalability, and supports rapid development cycles by breaking work into smaller, lower-dependency chunks.

Adrian Cockcroft, former head of web engineering at Netflix, defines a microservices architecture as “a service-oriented architecture composed of loosely coupled elements that have bounded contexts.” That definition is elegant, but implementing it across an enterprise application portfolio is far from trivial. It involves a cascade of architectural, organizational, and cultural decisions that many organizations underestimate.

Despite the hype, microservices adoption in enterprises still comes with a steep learning curve, new deployment platforms, unfamiliar developer toolchains, and significant organizational restructuring. IDC has forecast that by 2022, 90 percent of all new applications would feature microservices architectures, with 35 percent of production apps being cloud-native designs. Whether or not that specific figure proves accurate, the directional trend is clear: modular, service-based application architectures are becoming the default for organizations that need development agility and shorter release cycles.

The critical insight that separates successful adopters from struggling ones: microservices is a redesign and construction project, not a remodeling job. You cannot layer microservices on top of a monolith and call the work done. The underlying service boundaries, data ownership, deployment pipelines, and team structures all have to change.

From Monoliths to Microservices: Key Steps

IT consultancy TekSystems has published guidance on making a successful transition from monolithic to microservice-based applications. Combined with Gartner’s advice on designing microservices for agile architecture, the following steps represent a practical framework for practitioners—not just executives—tasked with turning a microservices strategy into working production systems.

1. Start with the Full Cloud and Service Ecosystem

TekSystems frames this as “modernization should be holistic.” Because microservices depend on a wide range of infrastructure and platform components—container orchestration clusters, an API gateway, a service registry, a service bus, and often public cloud services like serverless functions and identity and access management (IAM) directories—you cannot adopt microservices in isolation. Piecemeal adoption leads to integration gaps and architectural debt. Begin by mapping the full platform ecosystem your services will depend on.

2. Identify Core Application Services and Dependencies

The goal of microservices is to decouple reusable service logic from application-specific code. Achieving this requires a thorough analysis of your existing application fleet: what components exist, what they do, and how they depend on one another. Skipping this step produces microservices that are logically monolithic despite being physically separated—sometimes called a “distributed monolith,” which captures the operational complexity of microservices without delivering their benefits.

3. Design Loosely Coupled Services

Reuse is one of the primary value propositions of microservices: a well-designed authentication service, for example, should be usable by any application in your portfolio without modification. That is impossible if services are tightly coupled to each other or to specific applications. The target is a library of generic, composable components that provide functions useful across a wide variety of applications.

Gartner extends this principle to data: each service should own its data in an independent repository that is accessible to other services through a well-defined API, rather than sharing a common database. This is a significant departure from how most enterprise applications are currently structured and is one of the hardest design constraints to enforce in practice.

4. Start Small and Iterate

TekSystems advocates for “quick wins”—minimum viable implementations that demonstrate value, build organizational confidence, and create a foundation for iterative improvement. Big Bang transitions, where an organization attempts to re-architect an entire application portfolio simultaneously, multiply project dependencies, stretch timelines, and are almost guaranteed to fail. Identify a bounded, lower-risk service as a first candidate, deliver it successfully, learn from the experience, and scale from there.

5. Adopt a Service-Oriented Mindset Across People, Process, and Technology

Microservices architecture is inseparable from organizational design. Project teams should be small and autonomous—Amazon’s “two-pizza team” model is frequently cited here, where teams are small enough to be fed by two pizzas. Smaller teams improve communication, reduce coordination overhead, and enable faster decision-making. Deloitte has published analysis on the benefits of shifting IT’s orientation from projects to products, which aligns with how successful microservices teams are typically structured.

TekSystems is direct on the leadership requirement: “You need to have strong leadership. The decision tree can’t be big and bloated, and the silos can’t be fighting with each other.” On the tooling side, the development platform must support rapid changes, multiple concurrent versions, and automated testing, integration, and deployment (CI/CD pipelines are non-negotiable in a mature microservices environment).

6. Stay Focused on Business Value, Not Technology for Its Own Sake

Microservices are a means to an end: faster development and evolution of applications, business processes, and customer-facing services. It is easy for engineering teams to become absorbed in platform decisions and architectural debates while losing sight of why the work is being done. A useful prompt from TekSystems: “Companies should not only think about improving what they have today, but more importantly, think about what doesn’t exist today.”

Measuring Success: Business Metrics Over Technical Metrics

The success of a microservices program should ultimately be measured in business outcomes, not just technical indicators like deployment frequency or container counts. Metrics worth tracking include:

• Revenue growth attributable to faster feature delivery
• Customer satisfaction scores and retention rates
• Reduction in operational costs
• Reduced cycle times for new application delivery, bug fixes, updates, and incident resolution
• Improved security posture with measurable reduction in cyber vulnerabilities

Learning from Early Adopters: The Netflix Case

The most vocal and extensively documented early adopters of microservices are large technology and online services companies. The FAANG cohort—Facebook, Amazon, Apple, Netflix, and Google—all have microservices embedded in their core infrastructure. Netflix has been the most public about its journey and the lessons learned.

Netflix moved from a monolithic architecture to a microservices-based system roughly a decade before this article was originally published. The motivation was direct: the monolithic codebase was buckling under explosive subscriber growth, and accumulated dependencies meant a single coding error could take the entire platform offline for hours. The decision to decompose was driven by operational necessity, not architectural fashion.

As an early adopter, Netflix encountered immature tooling and had to develop significant portions of its microservices infrastructure internally. That work produced reusable software components covering:

• Service discovery – enabling services to locate and communicate with one another dynamically
• Inter-process and inter-service communication – managing reliable message passing between services
• Service routing – directing traffic to the appropriate service instance
• Monitoring and security – providing visibility and access control across a distributed service mesh
• Workload scheduling – allocating compute resources efficiently across services
• Distributed configuration management – managing configuration state across many independent service instances

Netflix also experienced well-publicized outages caused by insufficient redundancy in its AWS cloud infrastructure—demonstrating that even organizations with significant engineering resources encounter serious failures in distributed systems. The result of sustained investment, however, is a platform that supports more than 150 million subscribers streaming vast volumes of content globally.

Where Enterprise Adoption Stands

As of the original publication of this analysis in late 2019, enterprise microservices adoption was still in early stages, with the share of enterprises running microservices in production likely still in single-digit percentages. The IDC forecast of 90 percent adoption by 2022 was viewed skeptically by some analysts, as it would have required adoption to roughly double each year from that baseline.

The core technology has matured substantially since Netflix made its initial transition. Many components that Netflix had to build from scratch—service meshes, API gateways, distributed tracing, container orchestration—are now available as managed cloud services or mature open-source projects. That lowers the implementation barrier significantly. The harder challenges—service boundary design, data ownership, team structure, and organizational culture—remain as demanding as ever and cannot be purchased as a managed service.

The practical takeaway: before embarking on a microservices program, invest time in studying the documented experiences of organizations that have already made the transition. The patterns of success and failure are well established. The organizations most likely to succeed are those that treat microservices adoption as a long-term redesign program, staff it with appropriate leadership and engineering authority, and measure outcomes in business value rather than architectural purity.

Key Takeaways

• Microservices adoption is a ground-up architectural redesign, not an incremental upgrade to an existing monolith.
• Success depends equally on organizational changes—small autonomous teams, product-oriented ownership—as on technology choices.
• Start with a holistic view of your cloud and platform ecosystem before designing individual services.
• Loose coupling must extend to data: each service should own its data store independently.
• Begin with small, bounded projects to prove value and build organizational capability before scaling adoption.
• Measure the program’s success in business outcomes: revenue, customer satisfaction, cost, and cycle time reduction.
• Study early adopters like Netflix—their published post-mortems and architectural decisions contain hard-won, directly applicable lessons.

Overview

Adopting Kubernetes in an organization is rarely a straight line. At SpringOne Platform 2019, Ray Tsang — Developer Advocate at Google — walked developers through the practical challenges of containerization and shared a toolkit of projects, libraries, and operational patterns that can smooth the journey. The talk touched on image building, local testing, memory management, container security, and deployment best practices, with a particular focus on the Java and Spring ecosystem.

The Challenge of Containerizing Java Applications

Containerizing applications — especially Java applications — introduces friction at multiple levels. Writing and maintaining Dockerfiles is tedious and error-prone. IDE support for container-based workflows has historically lagged behind. These friction points slow down development cycles and create barriers to adoption for teams that are otherwise ready to move to Kubernetes.

The Spring Cloud Kubernetes project addresses part of this problem by providing common interfaces that allow Spring Cloud and Spring Boot applications to consume Kubernetes-native services directly, reducing the amount of Kubernetes-specific boilerplate developers need to write themselves.

Local Testing with TestContainers

One of the tools Tsang highlighted was TestContainers, a Java library that integrates with JUnit to spin up disposable Docker containers for common dependencies — databases, message brokers, Selenium browsers, and other services — during test execution. TestContainers is particularly useful for:

• Data access layer tests: Run tests against a real database instance rather than an in-memory mock, without needing a persistent external database.
• Integration tests: Compose multi-service environments locally that mirror production topology.
• UI tests: Drive a containerized browser instance, removing the need for a locally installed browser.

The key benefit is that developers can run a representative test suite on a laptop without requiring a full Kubernetes cluster, reducing the feedback loop significantly.

Managing Java Memory Inside Kubernetes Containers

A common operational problem when running Java applications in Kubernetes is the OOMKilled state — where the container is terminated because it has exceeded its memory limit. This often happens because the JVM’s default memory settings are not aware of container resource limits.

Tsang pointed to the Cloud Foundry Java Buildpack Memory Calculator as one tool for computing a holistic JVM memory configuration. The calculator accounts for heap, metaspace, thread stacks, and other JVM memory regions to produce settings that keep total memory consumption within the container’s declared limit. Properly sizing JVM memory avoids both OOMKilled restarts and the opposite problem of under-allocating memory and hurting application performance.

Container Security: Don’t Run as Root

A recurring theme in Tsang’s container best practices discussion was privilege reduction. Most base Docker images default to running processes as the root user, which is a significant security risk in shared Kubernetes environments. The recommended practice is to explicitly set a non-root user in the Dockerfile or via Kubernetes security context settings.

Additional image hygiene best practices mentioned include:

• Keeping image layers small to reduce attack surface and pull time.
• Cleaning up unnecessary build artifacts and package manager caches within the image.
• Avoiding storing sensitive data in image layers.

Fast Image Builds with Jib

Jib is a Google-developed open-source tool that builds optimized Docker and OCI-compliant container images for Java applications — without requiring a Docker daemon. Key characteristics:

• No Docker required locally: Jib builds and pushes images directly to a registry, so developers don’t need Docker Desktop installed.
• Automatic layering: Jib separates application code, resources, and dependencies into distinct image layers, meaning that a code change only invalidates the application layer rather than the entire image. This dramatically speeds up rebuilds and pushes.
• Build tool integration: Available as a Maven plugin, a Gradle plugin, and a standalone Java library, so it fits into existing build pipelines with minimal configuration.

# Example: build and push with Jib via Maven
mvn compile jib:build -Dimage=gcr.io/my-project/my-app

Continuous Development with Skaffold

Skaffold is a command-line tool that automates the development workflow for Kubernetes applications. It watches source files, triggers a rebuild when changes are detected, and redeploys the updated image to a Kubernetes cluster — local or remote. Skaffold covers the full inner loop:

• Build: Invoke Docker, Jib, Buildpacks, or other builders.
• Push: Publish the image to a registry (or skip this step for local clusters).
• Deploy: Apply Kubernetes manifests, Helm charts, or Kustomize configurations.

Skaffold also provides building blocks for CI/CD pipelines, allowing the same workflow definition used locally to be reused in automated pipelines, ensuring consistency between development and production deployments.

Operational Best Practices for Java on Kubernetes

Beyond tooling, Tsang covered several configuration and operational patterns that Java applications need to work reliably in Kubernetes:

• Health checks: Configure liveness and readiness probes so Kubernetes knows when a pod is ready to receive traffic and when it needs to be restarted. Spring Boot Actuator exposes endpoints well-suited to this purpose.
• Zero-downtime deployments: Use rolling update strategies and ensure readiness probes are correctly tuned so new pods only enter the load balancer rotation when they are genuinely ready.
• Externalizing configuration: Store environment-specific configuration in Kubernetes ConfigMaps and Secrets rather than baking it into the image, keeping images portable across environments.
• Structured logging: Output logs in a structured format (e.g., JSON) so they can be ingested efficiently by cluster-level logging infrastructure such as Fluentd or Stackdriver.

Summary

The tools and practices Ray Tsang outlined at SpringOne Platform 2019 remain highly relevant for teams adopting Kubernetes with Java and Spring workloads. Using Jib to simplify image builds, Skaffold to tighten the development feedback loop, TestContainers to enable realistic local testing, and pairing all of that with sound operational practices — proper memory sizing, non-root containers, health probes, and externalized config — gives teams a practical roadmap for a successful Kubernetes adoption.

The Shift from Monolithic to Modern Applications

Software developers building modern applications have broadly adopted agile development practices, DevOps toolchains, and continuous integration and continuous delivery (CI/CD) pipelines. This shift has been driven by a fundamental change in how software is consumed: consumers and enterprises increasingly rely on web and mobile apps that must be updated frequently rather than delivered in large, infrequent releases.

Traditional monolithic applications were released on cycles of six to twenty-four months, with presentation, business logic, and data tier layers all shipped together in a single “big bang” deployment. That model cannot meet the demands of modern software, which must scale to potentially millions of concurrent users worldwide, handle massive data volumes, and process requests with near-zero latency. As a result, teams have decomposed codebases into discrete modules—proprietary code, open source libraries, microservices, and APIs—each independently deployable.

This architectural shift has a direct security implication: no single developer can hold a complete mental model of the entire codebase anymore, and the attack surface has grown proportionally. Security must evolve at the same pace as development.

DevOps Security Tools: Why Point Solutions Matter

A common but mistaken assumption is that simply migrating a monolithic application to a cloud infrastructure-as-a-service (IaaS) platform—such as Amazon Web Services (AWS)—automatically addresses security requirements. Modern applications built around microservices and APIs require security tooling that matches their architecture, not generic platform-level protections.

In its Critical Capabilities Report for Application Security Testing, Gartner noted that buyers often seek a single-vendor platform covering static application security testing (SAST), dynamic application security testing (DAST), and other AST techniques. However, Gartner cautioned that while this approach may be adequate for conventional web application testing, modern applications and DevOps workflows frequently require targeted point solutions—particularly for API security testing.

The key categories of application security testing tools relevant to DevOps environments include:

• SAST (Static Application Security Testing): Analyzes source code, bytecode, or binaries for vulnerabilities without executing the application. Integrates early in the SDLC.
• DAST (Dynamic Application Security Testing): Tests a running application from the outside, simulating attacker behavior against exposed endpoints.
• IAST (Interactive Application Security Testing): Instruments the application at runtime using agents or sensors, identifying vulnerabilities during normal test execution—combining the depth of SAST with the runtime accuracy of DAST.
• SCA (Software Composition Analysis): Identifies known vulnerabilities in open source and third-party components within the codebase.
• API Security Testing: Specifically targets the APIs that modern applications rely on for inter-service communication and external integrations.

Modern Applications Move Fast—Security Must Keep Up

Under DevOps and CI/CD models, development teams may commit code multiple times per day. This pace of “continuous innovation” creates tension with traditional security practices, which were often bolt-on steps performed at the end of the development cycle. Waiting until a release candidate is ready to run security scans introduces costly delays and can create a backlog of vulnerabilities that are expensive to fix late in the SDLC.

The solution is to shift security left—embedding automated security testing directly into the CI/CD pipeline so that every code commit, build, and deployment triggers relevant security checks. Gartner’s analysis of the DevOps and modern applications use case emphasizes that effective security tools in this context must:

• Integrate tightly with the existing DevOps toolchain (IDEs, source control, build systems, orchestration platforms)
• Support full automation with minimal manual intervention
• Minimize test execution time to avoid blocking pipeline throughput
• Provide actionable, developer-friendly vulnerability reports that link findings to specific lines of code
• Support bug-tracking integrations (such as Jira or similar systems) for seamless remediation workflows

The goal is not to slow development—it is to make security a natural, low-friction part of the development workflow so that teams can deliver secure software at DevOps speed.

IAST and Its Role in CI/CD Pipelines

Interactive Application Security Testing (IAST) is particularly well-suited to modern application development and CI/CD environments. Unlike SAST tools that require access to source code and can produce false positives, or DAST tools that require a fully deployed application and can miss server-side logic flaws, IAST instruments the application at runtime—typically via a language agent—and observes actual code execution paths during automated test runs.

This approach offers several advantages in a DevOps context:

• Low friction: IAST runs passively during existing automated tests (unit, integration, end-to-end), requiring no separate test execution phase.
• High accuracy: Because findings are tied to observed runtime behavior, IAST typically generates fewer false positives than SAST alone.
• Continuous feedback: Results are available immediately after each test run, giving developers real-time security feedback within the pipeline.
• Broad coverage: IAST is effective at identifying vulnerabilities in applications that rely heavily on open source components, APIs, client-side code, and JavaScript frameworks.

According to the Gartner Critical Capabilities report, the modern applications use case concentrates on API security testing, software composition analysis (SCA), automation and turnaround speed, SDLC integration, and IAST—with SAST and DAST treated as secondary considerations for this specific use case.

Embedding Security Across the Full SDLC

Regardless of deployment model—whether fully cloud-native microservices, legacy on-premises monoliths, or hybrid architectures—security cannot be an afterthought. The most effective approach is to treat security as a continuous practice that runs in parallel with development and testing throughout the entire software development life cycle.

Gartner recommends that security and risk management leaders responsible for application security embed AST into the SDLC by selecting solutions that:

• Work effectively within integrated development environments (IDEs)
• Provide native bug-tracking and issue management integrations
• Support quality assurance workflows
• Integrate with other application development and testing systems through plugins and full API enablement

When DevOps security tools are automated within the CI/CD pipeline, potential security risks can be identified and remediated before code reaches production—dramatically reducing the cost and complexity of fixing vulnerabilities and reducing exposure windows.

Key Takeaways for DevOps Teams

• Shift security left: Integrate automated security testing at every stage of the CI/CD pipeline, not just pre-release.
• Choose tools designed for DevOps: Prioritize solutions that support automation, pipeline integration, and fast feedback over tools that require manual setup or separate test cycles.
• Address the full component landscape: Modern applications depend on open source libraries, third-party APIs, and microservices—your security tooling must cover all of these, not just proprietary code.
• Consider IAST for runtime coverage: For applications where runtime behavior is critical to accurate vulnerability detection, IAST complements SAST and DAST effectively.
• Use SCA for open source risk: Software composition analysis is essential for identifying known vulnerabilities in the open source dependencies that make up a significant portion of most modern codebases.
• Treat API security as a first-class concern: APIs are primary attack vectors in microservices architectures and require dedicated security testing beyond what general-purpose AST tools provide.

Teams that integrate the right mix of SAST, DAST, IAST, SCA, and API security testing tools into their DevOps workflows—and automate those tools within CI/CD pipelines—are best positioned to deliver secure, high-quality software at the pace modern application development demands.

What Is a Cloud Centre of Excellence?

At its most basic, a CCoE is a cross-functional team — either in-house, externally sourced, or a blend of both — with the expertise to plan, govern, and accelerate cloud adoption across an organisation. But defining it purely as a team undersells what it actually needs to be.

A CCoE is fundamentally a change-management vehicle. It exists not just to handle technical migration tasks, but to drive a shift in how the entire organisation thinks about and works with technology. That means its members must combine technical depth — cloud architecture, security, DevOps — with business acumen, product knowledge, and communication skills capable of engaging stakeholders at every level.

Why Cloud Adoption Fails Without a CCoE: The Silo Problem

In most organisations, cloud adoption starts organically. A forward-thinking development team adopts AWS for a new service. A data team moves its analytics workloads to a managed platform. Finance runs a SaaS product. These efforts are valuable, but when they happen in isolation they create inconsistent governance, duplicated tooling, security gaps, and unpredictable costs.

The root cause is rarely technology — it’s people and process. Without a coordinating body, individual departments make their own decisions about cloud platforms, identity management, networking topology, and compliance controls. The result is shadow IT at scale, with no unified strategy to rationalise it.

A CCoE breaks down these silos by creating a single source of truth for cloud strategy, standards, and best practices. It ensures that cloud adoption is a coherent, organisation-wide programme rather than a series of one-off projects.

What Is a CCoE Responsible For?

The responsibilities of a CCoE evolve as an organisation moves through the stages of its cloud journey — from initial planning, through migration, to optimisation and innovation. A practical way to organise CCoE responsibilities is around four distinct functional areas:

1. Structure and Operations

This function defines how the organisation operates in the cloud. It focuses on consistency — establishing account structures, landing zone designs, naming conventions, tagging policies, and the operating model for cloud-hosted workloads. Members in this area actively engage other departments, identify constraints (such as regulatory or security requirements), and surface opportunities to improve delivery — for example, identifying where CI/CD pipelines would reduce manual toil and deployment risk.

2. Evaluation

Cloud platforms release features and services at a rapid pace. The evaluation function keeps the CCoE — and by extension the business — ahead of the curve. These team members assess new services, proof-of-concept emerging capabilities, and advise on whether and how to adopt them. Over time, this function builds organisational confidence: teams learn to lead on cloud strategy rather than react to it.

3. Governance

Security and compliance cannot be retrofitted. The governance function embeds policy controls from day zero — identity and access management, data classification, encryption standards, cost guardrails, and audit logging. By automating policy enforcement through infrastructure-as-code and policy-as-code tools, the CCoE allows engineering teams to move quickly without inadvertently violating compliance requirements. Governance is the guardrail, not the gatekeeper.

4. Innovation

The cloud’s value lies partly in its ability to compress the feedback loop between idea and experiment. The innovation function within a CCoE encourages a culture of measured risk-taking: building prototypes, running experiments, measuring outcomes, and iterating. These team members work across departments to understand business problems and translate them into cloud-native solutions, operating with the grain of cloud architecture rather than against it.

Who Benefits from a CCoE?

When cloud strategy is consistent and well-governed, every stakeholder group benefits:

• Leadership and executives gain visibility into cloud spend, risk posture, and strategic progress. Decision-making becomes data-driven rather than anecdotal.
• Engineering and IT teams work from standardised templates, pre-approved patterns, and clear guardrails — reducing onboarding time for new services and cutting troubleshooting overhead.
• Product and business teams can self-serve cloud resources within defined boundaries, accelerating delivery without waiting for centralised approvals.
• End users and customers benefit indirectly from faster delivery cycles, improved reliability, and security practices that reduce breach risk.

Templatised, repeatable deployment patterns are a practical illustration of this benefit: once the CCoE defines a secure, compliant deployment pipeline for a workload type, every team that adopts it inherits the same baseline quality — without having to re-solve the same problems independently.

Principles for Building an Effective CCoE

Standing up a CCoE is not a purely technical exercise. The following principles guide successful implementation:

Cross-Functional Composition

A CCoE that is staffed entirely by infrastructure engineers will struggle to win adoption across the business. Effective CCoEs include cloud architects, security engineers, finance analysts (for FinOps), product managers, and representatives from major business units. This breadth ensures that decisions account for business impact, not just technical elegance.

Executive Sponsorship

Without visible executive support, a CCoE risks being ignored or undermined by departments that do not want to change their practices. Executive sponsorship signals that cloud governance is organisational policy, not optional guidance.

Start Small, Design to Scale

It is better to launch a small, effective CCoE than to wait until you have assembled the perfect team. Start with the highest-priority workloads and the most critical governance gaps. Build processes and tooling that can scale to cover the full organisation as the CCoE matures and gains credibility.

Leverage External Expertise Where Needed

If your organisation lacks the internal knowledge to bootstrap a CCoE, engaging an external cloud consultant or managed service partner is a pragmatic option. A good partner will help you define your cloud roadmap, identify skills gaps, assist with hiring or training, and act as a co-pilot as your internal capability matures. The goal should always be to build internal competency over time, not to create a permanent external dependency.

Embed a Culture of Continuous Improvement

Cloud strategy is not a project with a completion date. The CCoE should run retrospectives, monitor cloud usage and costs, track adoption metrics, and continuously refine its standards and tooling. A CCoE that stops learning will quickly fall behind a cloud landscape that never does.

Key Takeaways

• A CCoE is a cross-functional team that governs, guides, and accelerates cloud adoption across an entire organisation.
• It solves the silo problem by creating a unified strategy, shared standards, and consistent governance.
• Core CCoE functions cover operations, evaluation, governance, and innovation.
• Every stakeholder group — from engineers to end users — benefits when cloud adoption is coherent and well-governed.
• Successful CCoEs require executive sponsorship, cross-functional membership, and a commitment to continuous improvement.
• External advisors can accelerate the bootstrapping phase when internal expertise is limited.

A Cloud Centre of Excellence is not a luxury for large enterprises. For any organisation serious about making cloud investment pay off, it is the organisational infrastructure that makes sustainable, scalable adoption possible.

Original source credit: cloudcomputing-news.net

Overview

In mid-2019, Kubernetes security company Alcide published findings from a cross-environment analysis conducted using its Alcide Advisor tool — a multi-cluster vulnerability scanner designed to audit Kubernetes and Istio environments against security best practices and compliance benchmarks. The results painted a concerning picture of how organizations were (or weren’t) handling sensitive configuration data and network segmentation in their Kubernetes workloads.

Key Findings

The analysis surfaced three significant gaps in Kubernetes security hygiene across the scanned environments:

• 89% of deployment scans showed that organizations were not using Kubernetes’ native Secrets resources. Instead, sensitive values such as credentials, tokens, and API keys were being wired into workloads in plain text — whether via environment variables, ConfigMaps, or container image layers.
• Over 75% of scanned deployments used workloads that mounted high-risk host file systems, including /proc, which can expose sensitive kernel and process information and widen the attack surface significantly.
• None of the surveyed environments had implemented network segmentation using Kubernetes’ native NetworkPolicy resources, meaning workloads could communicate freely within the cluster with no enforced isolation boundaries.

Why Kubernetes Secrets Matter

Kubernetes Secrets are objects designed to store and manage sensitive information — such as passwords, OAuth tokens, and SSH keys — separately from application code and pod specifications. When used correctly, Secrets reduce the risk of sensitive data being accidentally committed to version control, baked into container images, or exposed through environment variable inspection.

However, Secrets are not encrypted at rest by default in older Kubernetes versions, and they are base64-encoded (not encrypted) within etcd unless cluster operators explicitly configure encryption at rest. Simply adopting Secrets is a meaningful step forward compared to plaintext embedding, but it is not sufficient on its own. Teams should also:

• Enable etcd encryption at rest for Secret objects.
• Use RBAC to restrict which service accounts and users can read Secrets.
• Consider integrating with an external secrets manager (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) for stronger lifecycle management.
• Avoid passing Secrets as environment variables where possible; mounting them as files reduces certain exposure vectors.

The Risk of Mounting Sensitive Host Paths

Mounting host file system paths such as /proc into a container gives that container visibility into — and in some cases the ability to interact with — low-level kernel and process data. This is occasionally necessary for observability or system-level tooling, but should be treated as an exception rather than a default. When misused or misconfigured, these mounts can enable container escape or privilege escalation attacks. Security-conscious teams should:

• Audit all hostPath volume mounts in pod specifications.
• Apply PodSecurityPolicy (deprecated in Kubernetes 1.21+) or its successor, Pod Security Admission, to restrict which host paths workloads may mount.
• Avoid mounting /proc, /sys, or / unless there is a documented, reviewed operational need.

The Absence of Network Policies

Kubernetes NetworkPolicy resources allow teams to define rules that govern how pods communicate with each other and with external endpoints. Without any NetworkPolicy applied, all pods in a cluster can, by default, communicate freely — a flat network topology that violates the principle of least privilege and makes lateral movement trivially easy for an attacker who compromises any single workload.

That none of the environments surveyed by Alcide had implemented any NetworkPolicy was one of the starkest findings in the report. Implementing even basic default-deny ingress and egress policies, then selectively allowing required traffic, dramatically reduces the blast radius of a compromised pod.

Alcide Advisor and Azure DevOps Integration

Alongside the findings, Alcide announced that Alcide Advisor had been integrated with Azure DevOps, making it available in the Microsoft Visual Studio Marketplace at the time of the announcement. The integration was designed to embed continuous Kubernetes security scanning directly into CI/CD pipelines, covering:

• Vulnerability scanning of master API server and worker node components, including container runtimes.
• Support for multiple cluster types, including AKS, Kops, and AKS Engine.
• Continuous configuration drift detection across multi-team, multi-cluster environments.

The goal was to shift security checks left — catching misconfigurations and policy violations during development and staging rather than after production deployment.

Practitioner Perspective

David J. Brewer, founder of Eupraxia Labs — a software vendor running workloads on Azure AKS — was quoted in the announcement:

“With Alcide Kubernetes Advisor, Eupraxia Labs has been able to significantly reduce security drifts in our entire CI/CD pipeline — from development and staging, all the way to production.”

David J. Brewer, Founder, Eupraxia Labs

Gadi Naor, CTO and co-founder of Alcide, noted the broader challenge:

“The increasing complexity of multi-cluster Kubernetes environments and the persistent lack of connectivity with DevOps workflows is complicating efforts by DevOps teams to keep pace with vulnerabilities and best practices. Alcide Advisor automates and integrates cluster hygiene into the CD process natively, to arm DevOps teams to immediately identify potential vulnerabilities, configuration drifts and threats, without sacrificing agility.”

Gadi Naor, CTO and Co-founder, Alcide

Practical Takeaways for DevOps Teams

Whether or not you use a dedicated scanning tool, the gaps identified in this analysis point to a concrete checklist for improving Kubernetes security hygiene:

1. Audit for plaintext secrets. Scan your pod specs, ConfigMaps, and Helm values files for hardcoded credentials. Migrate to Kubernetes Secrets objects, then consider an external secrets management solution.
2. Enable etcd encryption at rest to protect Secret data stored in the cluster datastore.
3. Review and restrict hostPath mounts. Any mount of sensitive host paths should have a documented justification and be gated by Pod Security Admission or an equivalent admission controller.
4. Define NetworkPolicies. Start with a default-deny baseline policy and explicitly allow only the traffic your application requires. Use a CNI plugin that enforces NetworkPolicy (e.g., Calico, Cilium, Weave Net).
5. Integrate security scanning into your CI/CD pipeline. Static analysis of Kubernetes manifests can catch many of these issues before deployment. Tools in this space include open-source options such as Trivy, kube-bench, kube-score, and Checkov, as well as commercial products.
6. Apply RBAC rigorously. Limit which identities can read Secrets, access the API server, or modify workload specs.

Context and Caveats

This analysis dates from mid-2019 — an early period in the mainstream adoption of Kubernetes in enterprise environments. The Kubernetes ecosystem and the security tooling around it have matured considerably since then. However, the underlying security hygiene gaps identified — plaintext secrets, over-permissive host mounts, and absent network policies — remain common findings in Kubernetes audits years later, making the core lessons of this report still relevant for teams at any stage of their Kubernetes journey.

Note: Alcide was subsequently acquired by Rapid7 in 2021. Alcide Advisor may no longer be available as a standalone product.

As companies try to keep up with the pace of customer demands in software development and deployment, successful companies are adopting DevOps strategies. This brings together software developers with IT tech operations teams to work as a single unit to speed up the time to market.

In today’s hyper-competitive market, getting quality code out fast is key. Equipping DevOps teams with the right software and automation provide a variety of advantages:
1. Increased team flexibility (and agility)
2. Cross-skill and cross-functional development
3. Better collaboration
4. Improved support
5. Faster bug detection and remediation
6. More efficient processes
7. Automation of many manual tasks to help avoid manual errors
8. More engaged team members

Teams are using a variety of DevOps tools that integrate with each other as they create agile software environments.

Popular DevOps Tools

Here are some of the most popular DevOps tools chosen by successful teams.

1. Git

Git is in use across the software development industry. A distributed source code management tool, it allows you to track development progress. It creates milestones that allow you to save different versions of source code. When needed, you can go back to previous builds. It allows for the creation of separate “branches” which fosters experimentation and no-risk innovation.

2. JFrog Artifactory

JFrog Artifact Repository is one of the most popular choices among high-performing DevOps teams. In fact, it is used by 70% of Fortune 100 teams. As an artifact repository manager, it is able to integrate with any tech and supports software in different languages, including DevOps tools such as the Docker Registry. It supports total end-to-end management. It is the choice for companies looking to accelerate their continuous product delivery cycle. It also tracks artifacts from development through production to deployment.

3. Docker

Docker is one of the top container platforms. It isolates applications and chunks into different containers. This makes them portable and secure. When coupled with automation services, it can significantly improve workflow delivery while allowing containers to be moved from one location to different ones. Docker integrates with both Jenkins and Bamboo. For cloud migrations, major providers such as Google Cloud and AWS feature Docker support.

4. Jenkins

Jenkins can run right out of the box on Windows, Mac OS X, and Linux. It features an easy install and an intuitive interface in an open source solution. Jenkins allows you to automate different phases of the delivery pipeline and automate many manual tasks to free up your team for bigger projects. There are more than 1,000 plugins that can integrate with other DevOps tools, such as Docker or Puppet. It allows you to customize your CI/CD pipeline to your specifications.

5. Bamboo

Bamboo can automate your delivery pipeline by taking projects from builds to deployment. Bamboo boasts more built-in options, which means fewer (if any) plugins are needed than found in open source solutions. Many functions that need to be set up manually in products like Jenkins are pre-built in Bamboo. It works with other products from the Atlassian company line, including Bitbucket and Jira.

6. Puppet

An open source software solution, Puppet’s config management, and deployment tool are used to manage multiple application services. A unified platform allows for customization of configurations to aid with automated remediation. It can work with applications, network devices, and cloud-based servers. Puppet also integrates with many popular DevOps tools and feature more than 5,000 modules.

7. Vagrant

Vagrant allows the building and managing of virtual machine environments rolled into a single workflow. It can help you reduce your environment setup time. Vagrant creates single files for projects and focuses on automation. Whether you are a developer, operator, designer, or part of an integrated DevOps team, it can describe all of your software requirements, packages, OS configs, and users.

Which DevOps Tools Are Right For You?

Those listed above are just a few of the most popular choices. Deciding which tools are right for you will take some testing. Open source tools will save you money, but they will take more time to set up and configure. Most of the commercial tools have free-to-try versions to allow for evaluation.
In the end, it boils down to three key things:
1. The functionality you need
2. The budget you have (both time and money
3. How your team works best

Source – darkreading.com

The adoption of DevOps continues to grow rapidly, and security teams are still trying to keep up. A natural starting point has been to focus on application security and securing the code itself. Although this is definitely an important piece of the puzzle, DevOps today has moved beyond just building application code into binaries into building complete system infrastructure in containers and virtual machines.

With this increased scope of DevOps comes all of the risks of the tens of thousands of known vulnerabilities and misconfiguration issues associated with the operating system, services, and system components included in the build. Just performing security assessments on the source code alone is not sufficient to identify these risks.

With the focus on speed and providing customers a constant stream of new features, DevOps teams typically haven’t prioritized security first. Security is widely believed to be an inhibitor to agility, and using traditional security methods can be a bottleneck to DevOps agility. Security and IT operations teams understand that it’s not sustainable to leave security out of the picture completely, but if security doesn’t work at the speed of DevOps, it will continue to be pushed aside. This is why integrating security into the actual DevOps life cycle is key to managing risk effectively in the world of rapid development.

When looking to implement security at the speed of DevOps, one should understand what DevOps teams mean by the “CI/CD pipeline” and what that looks like. (CI refers to continuous integration, and CD refers to continuous delivery.)

Continuous integration is the concept that developers should be checking-in new code on a frequent basis (this could be several times a day). Code is checked in to a source code repository like GitHub, where an automatic build system, such as Jenkins, compiles the code and checks the new build to ensure the code didn’t break anything.

The new build then continues onto the continuous delivery stage, where it is automatically deployed into testing environments for more involved end-to-end, load, performance, and integration tests. If everything passes, the new build is ready to be deployed to production.

When deploying the new build, the DevOps team needs to define the right environment to run the applications and then ensure that all components are configured correctly. Tools such as Ansible, Chef, and Puppet are used for this.

Integrating Security into the Process
By understanding the CI/CD pipeline, security teams can see where it makes sense to put security controls in place and how they can match up to the existing DevOps workflow.

As mentioned, checking the source code being committed into the CI is a good first measure. However, many organizations leave it at that when they should go on to address the overall application infrastructure as it moves through the pipeline. It’s not just code going through CI/CD process; operating systems, third-party components, middleware, and databases are being built and deployed along with that code.

The CI/CD process involves a DevOps toolchain, a set of automated tools facilitating the building, testing, environment configuration, and deployment of these systems. By integrating security into this toolchain, organizations can add effective quality gates that fit within the existing process to assess for vulnerabilities, configurations, and compliance with frameworks and/or organizational standards. It’s important have quality gates and assessments at the different stages of building, testing, and deploying because many changes can happen between the code committing and the system deploying. It’s often more costly and frustrating to reject applications at the end of the CI/CD versus having visibility and addressing issues along each stage.

The most effective way to do this is to integrate security tools with CI/CD build tools (e.g., Jenkins, TeamCity, and Bamboo) and CI/CD configuration tools (e.g., Puppet, Chef, Ansible, and Salt) to fully automate these assessments. The assessments can then either stop issues from continuing down the pipeline or at least provide visibility into the risk the business is accepting. To ensure vulnerabilities aren’t slipping through the cracks, having the additional ability to dynamically test systems in a live sandbox would be a great benefit.

Just as important as preventing issues from going into production, organizations will want to monitor and maintain the integrity of their production environments. Several, perhaps hundreds, of production environments were configured specifically to make the DevOps-produced applications work, so once you’ve set it up correctly, you want to monitor for any configuration changes.

DevOps will continue to grow, so organizations must implement security solutions that work at the speed of DevOps. DevOps is the new world of developing, releasing, and updating applications for a growing number of enterprises; therefore, security teams need to let go of the traditional security stack, stop fighting DevOps teams, and instead jump in right beside them.

Weaveworks Brings a Free Tier of Weave Cloud to Google Cloud Platform

Weaveworks announced a free tier of Weave Cloud for Google Cloud Platform (GCP) users, with a specific focus on teams running workloads on Google Kubernetes Engine (GKE). The platform is designed to reduce the operational complexity of developing, deploying, and monitoring container-based applications in production.

What Is Weave Cloud?

Weave Cloud is a software-as-a-service (SaaS) platform aimed at application developers and DevOps teams working with containers. Its core goal is to address the friction points that arise in the container production pipeline — from initial cluster provisioning all the way through to continuous delivery and observability.

The platform supports integration with several major container orchestration and scheduling ecosystems, including:

• CNCF Kubernetes (including GKE)
• Docker Swarm / UCP
• AWS ECS
• Apache Mesos and Mesosphere DC/OS

The Problem: Kubernetes Complexity

While Kubernetes provides powerful container orchestration capabilities, adopting it in a production environment typically requires DevOps teams to assemble and integrate a broad set of tools manually. These include cluster provisioning, service discovery, continuous integration, continuous delivery pipelines, monitoring, and logging — each often sourced from different vendors or open-source projects.

Weave Cloud for GKE is positioned as a pre-integrated solution that consolidates these concerns into a single platform, backed by unified dashboards covering the full application lifecycle.

Key Features

The GKE-focused release introduced several capabilities relevant to production Kubernetes operations:

• Cluster management: Simplified setup and ongoing management of Kubernetes clusters.
• Release automation: Automated deployment workflows integrated with the CI/CD pipeline.
• Rollback support: The ability to revert deployments through the CI/CD process when issues arise.
• Full-stack Kubernetes troubleshooting: Dashboards and tooling to diagnose issues across the entire stack.
• GitOps workflow: Using Git repositories and pull requests as the source of truth for infrastructure and application configuration changes, providing an auditable, reviewable, and repeatable deployment process.
• Multi-cloud portability: Support for migrating workloads across different cloud environments.

GitOps: Operations by Pull Request

A central concept promoted by Weaveworks in this release is GitOps — the practice of using Git as the single source of truth for both application code and infrastructure configuration. Under this model, all changes to a running system are made via pull requests, providing auditability, peer review, and the ability to roll back by reverting a commit.

“The philosophy of ‘operations by pull request’, known as GitOps, is very powerful and completely in line with Kubernetes design and makes it even easier for developers to start with Kubernetes today.”

— Aparna Sinha, Group Product Manager for Kubernetes, Google

Weaveworks CEO Alexis Richardson elaborated on the vision behind the integration:

“Customers want the power of Kubernetes, but they want to spend less time and effort getting there. With Weave Cloud on GKE any developer can connect up their Git repos and have an operations-ready application in minutes. We call it GitOps – the simplicity and speed of joining up build, deployment, monitoring and analytics in one place. Kubernetes is the future of application delivery and this is the missing piece.”

— Alexis Richardson, CEO, Weaveworks

Free Tier vs. Paid Subscription

At the time of the announcement, Weave Cloud was available in two tiers for GCP users:

• Free tier: Targeted at developers and teams getting started with Kubernetes on GKE, providing core cluster and pipeline capabilities.
• Upgrade subscription: Aimed at DevOps teams running applications in production, offering larger data storage, additional functionality, and deeper integration with development tooling.

Why This Matters for DevOps Teams

The announcement reflected a broader trend in the Kubernetes ecosystem at the time: the gap between Kubernetes’ technical power and the operational effort required to use it effectively in production. Pre-integrated platforms like Weave Cloud aimed to lower the barrier to entry by providing opinionated, ready-to-use toolchains rather than requiring teams to build their own from scratch.

The GitOps methodology promoted by Weaveworks has since gained significant traction in the Kubernetes community and continues to influence how teams manage cluster state, configuration drift, and continuous delivery pipelines today.

Integrate and Automate Security in Your Build, Ship, & Run Processes

As enterprises move quickly to deploy containers and microservices with a continuous integration and delivery (CI/CD) pipeline, security often becomes an afterthought. DevOps and security teams should also strive to achieve continuous container security in the pipeline. The starting point for container security is during the Build phase, making sure applications don’t introduce vulnerabilities and containers are hardened to reduce the attack surface. But by far the most critical phase is Run-time, where securing the production environment and doing real-time monitoring and security is required.

Security has traditionally been a separate process implemented by a different team. But as application delivery becomes more automated and faster paced, security processes will also need to become integrated with the CI/CD pipeline. As security continues to “shift-left” to DevOps and development teams, security technology will need to have more application intelligence built-in.

The Developer–QA Tension Problem

Development and QA teams are working toward the same goal — shipping a reliable product — but they are often evaluated in ways that put them at odds. Developers are pushed to ship faster; testers are pushed to find more bugs. Without a shared process and shared tooling, the feedback loop between the two groups becomes slow, adversarial, and expensive.

The core problem is organizational: when testing is a gate that happens after development rather than a continuous activity that runs alongside it, defects are found late, rework is costly, and release dates slip. CI/CD methodology reframes testing as an integral part of the development process rather than a final inspection.

The Device Fragmentation Challenge

Mobile testing has an additional layer of complexity that desktop software does not: the sheer number of distinct hardware and software combinations your app must support. Different manufacturers ship different CPUs, GPU drivers, screen densities, and peripheral implementations. Operating system versions further multiply the matrix. Testing only on simulators or a handful of physical devices in a developer’s drawer is not sufficient for a production release. Apps must be validated on real devices running real OS versions before they reach users.

What CI/CD Actually Means

Before jumping to the tips, it helps to be precise about the terminology.

Continuous Integration (CI)

CI is the practice of merging developer code changes into a shared mainline frequently — ideally multiple times per day. Each merge triggers an automated build and a suite of automated tests. The goal is to detect integration problems early, when they are cheap to fix, rather than at the end of a sprint or release cycle when large bodies of divergent code must be reconciled. Think of it as verifying each Lego brick fits correctly as you build, rather than discovering at the end that sections don’t connect.

Continuous Delivery (CD)

CD extends CI by ensuring the application is always in a deployable state. Beyond unit and integration tests, CD pipelines typically include end-to-end tests, regression suites, performance tests, and non-functional checks — anything that could affect user experience. The output is a release candidate that can be deployed to production at any time with confidence.

The CI/CD Pipeline

The pipeline is the automated sequence of stages that code passes through from commit to deployment. Each stage validates the change from a different angle — compilation, unit tests, integration tests, UI tests, performance benchmarks — and provides fast feedback. If a stage fails, the pipeline stops and the team is notified immediately. This gives every stakeholder visibility into the state of the codebase at all times.

Agile methodology, first articulated in the Agile Manifesto (2001), created the collaborative foundation that CI/CD builds on. DevOps extended that collaboration beyond engineering teams to include operations, security, and business stakeholders, making continuous delivery the natural end state of a mature Agile practice.

6 CI/CD Best Practices for Mobile App Testing

1. Build and Deploy an On-Premises Mobile Device Cloud

Individual testers connecting devices directly to their workstations creates an unscalable, inconsistent testing environment. A better approach is to centralize physical devices into a shared device lab — sometimes called a private device cloud — that all developers and testers can access remotely over the network.

• New devices and OS versions can be added to the lab without disrupting existing workflows.
• Automated test jobs can be scheduled to run on any device in the pool.
• Remote manual testing sessions give QA engineers hands-on access without physically handling hardware.
• Utilization data helps justify hardware investment and identify bottlenecks.

Cloud-based device services (public device clouds) are an alternative when an on-premises lab is not practical, offering access to a broad range of real devices on demand.

2. Use Collaborative Tooling to Create a Shared Testing Environment

The device lab is only as useful as the tools connected to it. Developers and testers need a common environment where test scripts can be written, shared, and executed without friction.

• Integrate the device lab with IDEs such as Xcode, Android Studio, and Eclipse so tests can be triggered directly from the development workflow.
• Support both UI-level testing frameworks (Appium, Espresso, XCUITest) and unit testing frameworks so coverage is broad.
• Make open-source frameworks available and runnable on every device in the lab to avoid licensing lock-in and maximize flexibility.
• Use shared repositories for test code so both developers and QA engineers contribute to and maintain the test suite.

3. Connect CI Tools and Test Case Management for End-to-End Automation

CI orchestration servers are the engine that keeps the pipeline running automatically. Tools such as Jenkins, TeamCity, and CircleCI trigger builds and test runs on every code commit, report results back to the team in real time, and block merges when quality gates are not met.

• Choose a CI server that integrates with your version control system, device lab, and notification channels.
• Pair it with a test case management system to track test coverage, historical pass/fail trends, and traceability back to requirements.
• Configure quality gates — minimum pass rates, maximum acceptable build times — so the pipeline enforces standards automatically rather than relying on manual review.

4. Build a Continuous Testing Environment That Covers All Test Types

With a centralized device lab and a CI server in place, the next step is ensuring that tests run continuously and cover the full spectrum of quality attributes.

• Parallel execution: Run tests simultaneously across multiple devices rather than sequentially. This dramatically compresses testing cycle time.
• 24/7 scheduling: Overnight and weekend test runs make use of otherwise idle hardware and ensure results are ready when the team starts work in the morning.
• Test type coverage: Ensure the pipeline includes unit tests, integration tests, UI/functional tests, regression tests, performance tests, and network condition simulations (low bandwidth, high latency, offline modes).

5. Manage Devices, Apps, Projects, and Users from a Central Dashboard

A shared device lab creates operational overhead that must be managed deliberately. Assigning a dedicated cloud administrator — or embedding lab management into a DevOps platform with self-service capabilities — frees developers and testers from the day-to-day logistics of device maintenance.

• A central dashboard gives visibility into device availability, charge state, OS versions, and current test assignments.
• Role-based access control ensures that the right teams can access the right devices and projects without conflict.
• Automated alerting notifies teams when devices go offline, tests fail, or new OS updates need to be applied to lab devices.
• Application version management in the lab ensures testers always have access to the correct build for their test run.

6. Use End-to-End QA Analytics for Rapid Root Cause Analysis

Test execution generates large volumes of data — logs, screenshots, video recordings, crash reports, performance traces. Without a consolidated analytics layer, this data is difficult to act on quickly.

• Centralized reporting aggregates results across all devices, test types, and pipeline runs into a single view.
• Trend analysis highlights which test cases are flaky, which device/OS combinations produce the most failures, and whether overall quality is improving or degrading over time.
• Root cause analysis tools correlate failures with specific code commits, device configurations, or OS versions, enabling engineers to isolate and fix issues faster.
• Risk-based reporting flags high-priority failures — those affecting the most users or the most critical user flows — so teams can triage effectively under time pressure.

Putting It Together

These six practices are mutually reinforcing. A device lab without CI integration produces underutilized hardware. A CI pipeline without real-device coverage misses real-world failures. Analytics without centralized test execution produces incomplete data. The value comes from treating them as a connected system rather than independent initiatives.

Adopting CI/CD for mobile development is not a one-time project — it is an ongoing investment in the feedback loops, tooling, and team collaboration that make fast, reliable releases possible. Teams that build this infrastructure systematically are consistently better positioned to respond to competitive pressure without sacrificing the quality their users expect.

Why a Mature CD Pipeline Matters

Organizations that invest in mature CI/CD pipelines typically realise benefits across the entire software delivery lifecycle:

• Reduced manual effort: Automating repetitive tasks frees engineers to focus on higher-value work and accelerates release cycles.
• Improved release quality: Automated testing and promotion gates catch defects earlier, reducing rollbacks in production.
• Greater visibility and accountability: A shared pipeline dashboard gives every team—development, QA, support, and operations—a single source of truth for release state.
• Lower deployment and support costs: Consistent, scripted deployments reduce the cost of incidents and the time spent on post-release fire-fighting.
• Cross-team collaboration: Shared tooling and processes break down silos between development and operations.

The Core Practices of a Mature CD Pipeline

1. Maintain a Single, Authoritative Code Repository

Every artifact required to build and deploy the product—source code, configuration, dependency manifests, and infrastructure definitions—should live in a version-controlled repository. Revision control is non-negotiable. Prefer trunk-based development or short-lived feature branches over long-running per-developer branches. Long-running branches increase integration risk and delay feedback. Git is the de facto standard for distributed version control and pairs well with code-review tooling.

2. Automate the Build Process

Use a build tool—such as Maven, Gradle, Make, or Bazel—to standardise how the project is compiled and packaged. A single command should be sufficient to produce all distributable artifacts: binaries, packages (RPM, DEB, MSI), container images, or tarballs. Key rules for build automation:

• Builds must be repeatable: the same source commit should always produce the same artifact.
• Builds must be fast: break large monolithic build jobs into smaller, parallelisable jobs to keep feedback loops short.
• Builds must be environment-independent: no dependency on a developer’s local machine state.

3. Run Automated Tests on Every Commit

A CI server (Jenkins, GitLab CI, GitHub Actions, TeamCity, etc.) should monitor the repository and trigger a build-and-test pipeline automatically for every commit or pull request. This ensures a stable, verified baseline at all times. For code-review workflows, integrate your CI server with a review tool such as Gerrit or a pull-request system so that test results are visible to reviewers before a merge decision is made.

Test suites should be organised into fast feedback layers:

• Unit tests – run on every commit; must complete in seconds.
• Integration tests – run against assembled components; may take minutes.
• End-to-end / acceptance tests – run against a fully deployed environment; may take longer but should be parallelised where possible.

4. Enforce Static Code Analysis

Code quality problems accumulate into technical debt that slows future development. Integrate static analysis tools (SonarQube, Checkstyle, ESLint, SpotBugs, etc.) directly into the CI pipeline. Define quality gates—minimum code coverage thresholds, maximum critical violation counts—and fail the build when those thresholds are not met. Treating code quality as a first-class pipeline concern prevents debt from silently accumulating.

5. Commit Frequently to the Mainline

Developers should integrate their changes into the shared baseline frequently—ideally multiple times per day. Small, frequent commits reduce merge conflicts, make automated test failures easier to diagnose, and keep every developer aware of the real-time state of the codebase. Large, infrequent merges are one of the most common sources of integration pain in software projects.

6. Test in Production-Equivalent Environments

A defect that is only reproducible in production is a defect that was not caught in testing. Test environments should match production as closely as possible: same operating system versions and patches, same library versions, same network topology, and same configuration values (with secrets handled separately). Use configuration management tools—Ansible, Chef, Puppet, or Terraform/cloud-init for infrastructure—to provision and maintain environments in a consistent, documented, repeatable way. Infrastructure-as-code stored in version control ensures environment drift is detectable and correctable.

7. Define a Clear Build Promotion Process

Promotion is the act of deciding that a build artifact is ready to advance to the next stage of the pipeline (e.g., from CI → QA → pre-production → production). Document explicit promotion criteria: which automated test gates must pass, which manual approvals are required, and who has authority to approve each stage. Most CI/CD platforms support first-class promotion workflows. A well-defined promotion process is especially important for distributed teams where informal communication cannot substitute for a shared, visible process.

8. Manage Release Artifacts with a Dedicated Repository Manager

Large organisations quickly accumulate hundreds or thousands of versioned artifacts: release candidates, GA builds, client-specific builds, patches. A binary repository manager such as JFrog Artifactory or Sonatype Nexus provides a governed, searchable store for all artifact types (Maven, npm, Docker images, Helm charts, raw files). Using a dedicated artifact repository ensures that the exact binary promoted through QA is the binary deployed to production—no rebuilding from source at deployment time.

9. Automate Deployments

Manual deployments are error-prone, slow, and difficult to audit. All deployments—to every environment—should be executed by scripts or pipeline jobs, never by a human typing commands manually. Deployment automation should include:

• Pre-deployment checks (health of target environment, dependency availability).
• Deployment execution (artifact retrieval, configuration injection, service restart).
• Post-deployment smoke tests to verify the new version is serving traffic correctly.
• Automated rollback on smoke test failure.

With sufficient test coverage and deployment automation in place, teams can consider enabling Continuous Deployment—automatically deploying every commit that passes all pipeline gates directly to production without manual approval.

10. Use Configuration Management Across All Environments

Modern software stacks span virtualised infrastructure, public cloud, private cloud, and multi-datacenter deployments. Configuration management tools (Ansible, Chef, Puppet) or infrastructure-as-code platforms (Terraform, Pulumi, AWS CDK) enable teams to define environment state declaratively, apply it repeatably, and detect and remediate drift automatically. This is especially important for hybrid or multi-datacenter deployments where manual configuration of each host is not feasible.

11. Publish Build Status and Test Results to the Whole Team

A broken build that nobody knows about defeats the purpose of CI. Configure your CI/CD platform to notify developers immediately—via email, Slack, Microsoft Teams, or a build radiator dashboard—when a build or test fails. Notifications should identify the commit and author that introduced the failure so the right person can act quickly. Visibility into pipeline health drives accountability and gives teams the data they need to improve their processes over time.

Summary

A mature Continuous Delivery pipeline is not a single tool or a one-time project—it is an evolving set of practices that collectively reduce risk, increase deployment frequency, and improve software quality. The eleven practices described above represent a proven baseline: single repository, automated builds, per-commit testing, static analysis, frequent commits, production-equivalent test environments, defined promotion processes, artifact management, deployment automation, configuration management, and team-wide build visibility. Organisations that consistently apply these practices report faster release cycles, lower defect rates, and better collaboration between development and operations teams.