As IT is a vast and growing sector, it always demands skillful professionals with some certifications on their related tools or working courses that give a great advantage to both organizations and professionals. A Certification can give you more skills that can boost your resume and career as well that can offer you more lucrative salaries and opportunities. Certifications have always been playing a key role to increase the value of your skills and getting an advantage in getting a job in your particular desired domain.

Some IT certifications.

Master in DevOps engineering – As DevOps has beaten all the traditional SDLC processes, it has become a more demanding and favorite process.

It has been seen DevOps is growing market day by day and it will be continued in upcoming years as well. This master certification is really important as it offers to learn all the open-source major toolsets which can boost your knowledge as well as career.

So do check this certification as it is still in demand and is continuously growing and offers a good salary package.

Site reliability engineering (SRE) – SRE is founded byGoogle Company and also works the same as DevOps, but the only difference is the main focus of SRE is to enhance the reliability and better performance of applications, with automation and continuous integration and delivery.

SRE has also earned its value in the market and come in demand. It has also toolsets like DevOps to automate their work. You can learn everything during the certification of SRE. As per the 2021 report, 22% of organizations have adopted SRE, and it is expecting more growth in 2022.

DevSecOps – The main focus of DevSecOps is security but it stands for all development, security, and operations.

It believes security is everyone’s priority. Everyone should work by keeping security in mind, so the security level won’t compromise.

DevSecOps certifications are essential as the security of software is very important.

This certification will teach you the way to keep the security in all the parts of work and how security matters when it comes to deployment in the market.

It takes the responsibility to save the software from all the loopholes and people like hackers as much as possible. It also has its toolsets to automate the work and to remove manual errors. It is also achieving very nice growth and it will be continued in 2022 as well.

Docker and Kubernetes – Docker and kubKubernetesth are essential to each other as Docker is a container and Kubernetes is the management tool of those containers. That’s why both certificates are necessary if you thinking to do one. Some company uses Docker Swarm instead of Kubernetes. Both works are the same but the thing is most of the companies prefer Kubernetes, so go with this. Kubernetes is vaster than Docker Swarm. As both had images in the market it is certain both the tools will keep continuing their growth in the market and also both are important to the DevOps process, so yes it comes under important IT certification of 2022.

Artificial Intelligence for IT Operations (AIOPS) –

AIOPS is the application of AI, machine learning, natural language processing, etc to traditional IT Ops activities and tasks. AIOps combine big data and machine learning to automate IT operations processes, which comprises event correlation, anomaly detection, and causality determination.

It is hard to manage today’s dynamic and constantly changing IT environments with yesterday’s tools. Microservices and containerization make it necessary to modernize. The evolution of IT infrastructure is a must. So the certification of AIOPS is also important because AIOPS is in trend and we can see this in 2022 as well.

Gitops –

GitOps is a method to manage infrastructure and application configurations using Git.  GitOps is built around the developer experience and help teams in managing infrastructure using the same tools and processes they use for software development.

Gitops certification enables you to leverage so many benefits like:-

Enhance developer productivity

An enhanced version experience for DevOps teams

Reduce failed deployments

Recover from outages quickly

Mlops –

MLOps certification is a set of practices that intends to deploy and maintain machine learning models in production.

MLOps allow the continuous delivery of high-quality ML applications into production at scale.

MLOps is the communication between data scientists and the operations team. It is deeply collaborative and designed to eliminate waste, automate as much as possible, and produce more richer and consistent insights through machine learning. Machine learning is a game-changer for business as its environment is changing from manual to automation.

MLOps have all the capabilities that are required from data science, product teams, and IT to deploy, operate, govern, and secure models in production. It comprises the following components that enable an automated ML pipeline as well that maximizes your ML performance and ROI:                                                                                                                                                          

Model serving and pipelining

Model service catalog(s) for all models in production

Model version control

Infrastructure management

Monitoring

Security

Governance

Connections with all data sources and best-in-class tools for model training, development, infrastructure, and compliance.

Artificial intelligence (AI) –

Artificial intelligence is a program that is designed to work like human brains such as thinking, learning, making decisions on their own.

Artificial intelligence has been introduced in 1956 at a conference by John McCarthy and this is the first person who thought about artificial intelligence. That’s why we call him the father of AI.

As per the report, it will be approx 58 million job roles in the AI sector in 2022. So AI is present as well as future because we are already using AI technologies like Alexa, Siri on iPhone, etc.

DataOps –

DataOps is designed to solve problems organizations challenges associated with inefficiencies in accessing, integrating, and making data available. It is an orchestration of people, processes, technology to deliver high-quality data to his data citizens fast. DataOps is focused on collaboration to deliver agility, speed, and new data initiatives at scale.

DataOps has 3 objectives –

Know your data

Trust your data

Use your data

This course is part of the Data Engineering Specialization which provides learners with the specialized skills required to be a Data Engineer. It has been estimated millions of jobs will be discovered in 2022 for this role as it is in demand. So its certification is going to play an important role for the complete knowledge and to get a job at good organizations.

Machine learning – Machine learning is a kind of intelligence of system to learn things without taking human help. It is an important part of artificial intelligence to use as a brain that uses data and algorithms to learn new things as well as take decisions.

The Machine Learning certification will help you to master the skills required to become an expert in this domain. Master skills such as Python, ML algorithms, statistics, supervised and unsupervised learning, etc. to become a successful professional in this popular technology.

As the market is changing it has been seen vast growth in this field. In 2022 as well as in the rest of the upcoming years a machine learning will dominate the market.

Training Place

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

DevOpsSchool is one of the best platforms which provides good training as well as a free atmosphere where you get so many benefits to learn.

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

GitLab today took the wraps off a major update to its continuous integration/continuous delivery (CI/CD) platform that embeds analytics tools in workflows to identify the root cause of inefficiencies, among other additional capabilities.

David DeSanto, senior director for product management at GitLab, said that capability is part of an ongoing effort to include value stream analytics within the core GitLab 14 platform as an alternative to acquiring and deploying a separate platform. There are also additional charts and dashboards that enable DevOps teams to uncover bottlenecks over time.

In addition, GitLab announced it is extending the security capabilities of the platform by adding integrations with open source Semgrep and Trivy vulnerability scanning tools as part of an effort to enable organizations to embrace DevSecOps best practices within workflows. GitLab has also added support for the fuzz testing tool it gained via the acquisitions of Fuzzit and Peach Tech to enable DevOps teams to test application security more thoroughly along with making available additional dashboards to track security metrics.

As part of an effort to enable organizations to better secure their software supply chains, GitLab has also added support for immutable pipeline definitions that can’t be altered without specific levels of permissions being granted. That capability also enables DevOps teams to more easily achieve compliance with any number of regulatory frameworks, noted DeSanto.

The company is also adding a pipeline editor that provides access to a visual tool for creating, managing and visualizing pipelines. That tool is designed to both make DevOps more accessible to novices and provide experienced DevOps teams with an alternative tool for managing pipelines in a way that doesn’t require them to write additional code.

Finally, as part of its comprehensive approach to DevOps, GitLab is also making available agent software for Kubernetes clusters to eliminate the need for organizations to acquire a separate DevOps platform to build and deploy cloud-native applications based on microservices constructed using containers. The company now provides IT organizations with the option of crafting a GitOps workflow for Kubernetes using either an agentless or agent-based approach.

Gitlab is betting that, as DevOps continues to evolve and mature, more organizations will prefer to replace the various tools they need to integrate themselves with a platform that lowers the total cost of DevOps. Gartner is forecasting that, by 2023, 40% of organizations will have switched from multiple point solutions to a DevOps value stream delivery platform to streamline application delivery, versus less than 10% using such a platform in 2020. Today, only 10% of organizations are employing such a platform, according to Gartner.

There should also be many more organizations employing DevOps workflows to build applications. Organizations of all sizes now realize how dependent they are on software to drive a wide range of digital business transformation initiatives. The rate at which that software can now be securely developed and updated, in many cases, goes to the heart of their ability to compete. The issue is finding a way to achieve that goal without constructing a platform that ultimately takes time and resources away from building secure software.

PLEASANTON, Calif.–(BUSINESS WIRE)–Accurics, the cloud cyber resilience specialist, today announced a technology partnership with GitLab, a single application for the DevOps lifecycle, as well as the general availability of its integration with GitLab’s Static Application Security Testing (SAST) solution. Accurics leverages the integration with GitLab to provide DevSecOps teams with a holistic, contextualized view of application and infrastructure risks. Organizations can now establish and programmatically enforce consistent risk management policies throughout the Software Development Lifecycle (SDLC) while minimizing the effort and expense of manual triage and investigation.

Cloud infrastructure and applications are traditionally deployed from two separate pipelines, which dissociates application security vulnerabilities from Infrastructure as Code (IaC) misconfigurations. As a result, developers are often left with a long list of vulnerabilities and misconfigurations to fix without the context required to prioritize remediation of those vulnerabilities and misconfigurations that could actually be exploited.

“The most effective innovation is often incremental – for example, new capabilities and additional functionality accompanied by relevant security advances,” said Om Moolchandani, Co-founder, CTO & CISO at Accurics. “In this environment, we see diverse and largely unconnected vulnerabilities and misconfigurations, collectively producing a level of noise that makes identifying the most serious risks vital but difficult. The partnership with GitLab serves to add greater context to every layer of code and strengthens the security risk posture throughout the extended development lifecycle.”

The integration with GitLab helps Accurics users overcome these challenges by correlating IaC, cloud, and SAST vulnerabilities to help mitigate risk throughout the SDLC and generate a threat score. This threat score can be used by policy guardrails established with Policy as Code, blocking the riskiest builds from being deployed into production while providing insight into less risky problems that don’t warrant breaking the build. As a result, developers are able to focus resources on remediating the most immediate threats first.

“The growing adoption of GitOps practices and Infrastructure as Code necessitates scalable risk management tools,” said Nima Badiey, Vice President, Global Alliances at GitLab. “The integration between GitLab and Accurics will help customers to programmatically define infrastructure and risk management policies more effectively throughout the software development lifecycle.”

At Accurics™, we envision a world where organizations can innovate in the cloud with confidence. Our mission is to enable organizations to achieve cloud cyber resilience with developer-first security that self-heals the cloud. The Accurics platform programmatically detects infrastructure misconfigurations and contextualizes the findings with application risks to identify breach paths throughout the development lifecycle. It automatically generates code to remediate issues and applies the fix in the development pipeline. Accurics enables organizations of all sizes to achieve developer-driven cloud cyber resilience through cloud-based and open source tools such as Terrascan

Weaveworks has added observability tools to its GitOps platform for Kubernetes environments with no additional configuration effort required.

Company COO Steve George says the addition of an observability portal to the Weave Kubernetes Platform (WKP) to monitor the health of clusters and overall system performance is a natural extension, given the scope of the technology.

In addition, with the release of version 2.5 of WKP, Weaveworks is enabling IT teams to troubleshoot entire fleets of clusters on which the company’s GitOps platform is deployed across. Support for standard metrics and logs along with aggregation of alerts are all included, says George.

IT teams can also leverage open source Prometheus monitoring software to collect endpoint data from other platforms running on Kubernetes in addition to the alerts and cluster events generated by Prometheus.

Finally, Weaveworks has added support for single sign-on capabilities and deepened the level of integration it provides with the Amazon Elastic Kubernetes Service (EKS).

Weaveworks is making the case for a GitOps platform based on Flux, an open source tool that automatically ensures the state of a cluster matches the configuration stored in a Git repository. It uses an operator in the cluster dubbed Flagger to trigger application deployments to Kubernetes without requiring IT teams to acquire and deploy a dedicated continuous delivery platform. Flux monitors all image repositories, detects new images, triggers deployments and updates configurations accordingly.

WKP adds on top of that core platform Team Workspaces, a workflow application for tracking changes to Git-based deployments that can be used by multiple DevOps teams. Each workspace can span multiple Kubernetes clusters to simplify the rollout of applications across a fleet of Kubernetes clusters.

That approach provides the added benefit of improving security and auditing because each pull request made from the repository needs to be authorized and logged, notes George. That audit trail also accelerates troubleshooting, he adds, by making it easier to discover the root cause of an issue across the full stack of a Kubernetes environment that otherwise would be challenging to diagnose.

It’s not clear at what rate organizations are adopting GitOps as a more opinionated instance of a DevOps workflow. However, it’s apparent there is a high correlation between the adoption of GitOps and the transitions organizations are making to Kubernetes. Given the rapid pace of change to microservices-based applications, organizations are attempting to leverage the standard set of application programming interfaces (APIs) that Kubernetes clusters present to automate continuous delivery especially.

IT teams will require observability tools that provide more context than traditional IT monitoring tools to achieve that goal. Each IT team will need to decide for themselves to what degree they want to employ observability tools for a specific application deployment environment versus relying on platforms that observe both legacy IT and more modern Kubernetes environments. Regardless of the path chosen, one way or another observability as a core tenet of a best DevOps workflow is finally becoming a lot easier to achieve.

In June, July and August, Heise offers a webinar series DevOps in practice a practical introduction to the world of modern container technology. In a total of 20 hours, participants learn how to use continuous delivery pipelines. In addition, you will learn how to implement test automation, create the right corporate culture for DevOps, integrate security into the development process via DevSecOps and implement continuous deployment with GitOps.

The webinars are headed by the renowned experts Konstantin Diener, Christian Kühn, Anja Kammer, Simon Harrer, Sven Johann and Nicolas Byl, who playfully convey their years of practical experience in the areas of DevOps and Continuous Delivery to the participants. Each webinar is structured like online training and offers plenty of space for interaction and practical exercises.

The webinars have a running time of four hours each and take place from 9 a.m. to 1 p.m. Participation in a webinar costs € 139. If you book all five appointments at the same time, you can save a lot with a discounted combination ticket for € 429 compared to individual sales. All participants can not only look forward to a lot of practice and interaction, but also have the opportunity to repeat and deepen what they have learned with all the records and materials afterwards.

We’re building a continuous delivery pipeline
The start of the webinar series makes Konstantin Diener (cosee) on June 29th. In the webinar he teaches participants why continuous integration and continuous delivery make sense at all. With practical exercises and examples, they master the principles of the techniques and put them into practice. Together they work out the advantages of continuous delivery and develop their own CD pipeline, after which they are able to apply the techniques for their own projects

SANTA CLARA, Calif., May 27, 2021 /PRNewswire/ — Palo Alto Networks (NYSE: PANW), the global cybersecurity leader, today announced the release of Yor, an open-source tool that automatically tags cloud resources within infrastructure as code (IaC) frameworks Terraform, AWS CloudFormation, and Serverless Framework YAML. Yor automates the tedious work of manually tagging cloud resources, helps security teams trace security misconfigurations from code to cloud, and enables highly effective GitOps across all major cloud providers.

“Effective infrastructure tagging is critical to tracking cost allocation, access control, operations, and of course security in the cloud,” said Barak Schoster, chief architect at Palo Alto Networks. “To date, this has been an all-too-manual process for developers, with each cloud provider and organization having different standards and naming conventions. By automating standardized tagging, Yor provides visibility and traceability from IaC configuration to cloud resources in production.”

Organizations can run Yor across all infrastructure resources to retroactively assign ownership and other meaningful tags based on IaC and git history data. Yor can also be built into the continuous integration and continuous delivery (CI/CD) lifecycle for improved traceability as infrastructure is modified and created. Having consistent tagging will make it simple to trace any misconfigurations back to the original code owners and editors, reducing the time to patch.

The Cloud Security Alliance noted in its recent report The State of Cloud Security Concerns, Challenges, and Incidents that misconfigurations were among the leading causes of breaches and outages as public cloud adoption doubled over the past two years. If a security team identifies a misconfiguration, having the tags for the developer owner simplifies triaging, so the ticket can be automatically assigned to the right developer. Yor’s use cases also extend beyond security by making it easier to tag resources to allocate costs from a finance and budgeting perspective.

“DevSecOps is about breaking down silos and improving productivity,” said Ismail Yenigul,open-source contributor and DevSecOps expert. “Imagine there is a SEV0 security incident — the last thing you want to do is spend hours identifying what caused a misconfiguration or track down the developer who wrote or modified the infrastructure code that is managed in Terraform, CloudFormation, or Serverless. Yor makes it possible to get answers to those questions immediately, for much more effective collaboration and faster mean time to resolution of incidents.”

Yor was built by Bridgecrew, the team behind the popular open-source IaC scanner Checkov which has been downloaded over 2 million times by developers. Bridgecrew was acquired by Palo Alto Networks in March 2021 and together they continue to invest in new and existing open source projects.

Yor is an open-source tool that automatically and consistently tags resources across infrastructure as code (IaC) frameworks (e.g., Terraform, CloudFormation, Kubernetes, and Serverless Framework). Yor enables version-controlled owner assignment and resource tracing-based git history. It also extends tag enforcement logic by loading external tagging logic into your CI/CD pipeline. For more information, visit www.yor.io.

Palo Alto Networks

Palo Alto Networks, the global cybersecurity leader, is shaping the cloud-centric future with technology that is transforming the way people and organizations operate. Our mission is to be the cybersecurity partner of choice, protecting our digital way of life. We help address the world’s greatest security challenges with continuous innovation that seizes the latest breakthroughs in artificial intelligence, analytics, automation, and orchestration. By delivering an integrated platform and empowering a growing ecosystem of partners, we are at the forefront of protecting tens of thousands of organizations across clouds, networks, and mobile devices. Our vision is a world where each day is safer and more secure than the one before. For more information, visit www.paloaltonetworks.com.

Palo Alto Networks, Prisma, and the Palo Alto Networks logo are trademarks of Palo Alto Networks, Inc. in the United States and in jurisdictions throughout the world. All other trademarks, trade names, or service marks used or mentioned herein belong to their respective owners.

Harness today announced it has updated the community edition of its continuous integration (CI) platform with, among other updates, a revamped user interface.

Formerly known as Drone.io, the latest release also adds pipeline execution visualizations, governance and security controls and live CI pipeline debugging capabilities. Harness last year acquired Drone.io, an open source CI project, to extend its existing continuous delivery (CD) platform into the realm of CI. Harness claims there are now more than 50,000 active monthly users of the community edition of Harness CI.

The update to the community edition of Harness CI comes at a time when the debate over how tightly integrated CI and CD platforms need to be is heating up. Harness launched a namesake CD platform before it acquired Drone.io to add a CI capability. While most DevOps teams are proficient with CI, the CD side of the DevOps equation has proven more elusive. There are now GitOps advocates that contend the rise of Kubernetes makes it more advantageous to enable a separate CD platform to pull updates from a Git repository, rather than having them pushed out by a CI/CD platform. The challenge with CD has always been that each platform on which code is deployed is unique. Kubernetes, however, now provides a de facto standard layer of abstraction that runs atop multiple platforms.

Rydzewski said the integrated CI/CD approach will ultimately prevail, because it enables DevOps teams to more easily integrate a range of processes, including testing, that are required to consistently build and deploy applications at scale. Harness is, essentially, making a case for solving the CD challenge using a platform that encourages – but doesn’t require – a DevOps team to replace their existing CI platform.

Regardless of how DevOps evolves, the CD issue needs to be addressed. As the enterprise becomes more extended with the rise of both cloud and edge computing, the number of platforms a DevOps team needs to support continuously expands.

Despite the fact that DevOps teams employ CI/CD platforms, it is clear most of those organizations are not overly committed to the way they currently manage CD. It may be years before most organizations finally implement a consistent set of processes for automating application deployment.

In the meantime, the number of organizations just starting out on their DevOps journey continues to increase. There are still plenty of instances where organizations have yet to deploy any type of CI/CD platform. It’s also worth noting that, in many cases, different DevOps teams within the same enterprise have decided to deploy different CI/CD platforms.

Regardless of how DevOps evolves the pressure to automate is rising. The challenge many DevOps teams face today is the current platform they are relying on to automate the building and deployment of applications requires a small army of specialists relying on all too often manual processes to maintain.

Red Hat, Inc., the world’s leading provider of open source solutions, and GitHub, the software collaboration platform home to more than 50 million developers, today announced extended collaboration between the two companies, emphasizing Red Hat OpenShift through GitHub Actions and more. Red Hat is adding Red Hat GitHub Actions to the GitHub Marketplace, bringing GitHub’s DevOps, continuous integration/continuous development (CI/CD) and developer workflow automation tools to Red Hat OpenShift. This further refines the application development capabilities of the world’s leading enterprise Kubernetes platform with GitHub Actions, adding greater freedom to how developers can build and deploy applications on Red Hat OpenShift across the open hybrid cloud.

GitHub has become nearly synonymous with developer tools and serves as the home for many popular software projects, including open source communities like the Linux kernel and Kubernetes. Using GitHub’s built-in powerful, flexible CI/CD solution, GitHub Actions puts automation directly in the developer path, making it possible for nearly any event in a GitHub repository, like a pull-request or issue comment, to trigger workflows that can build and deploy applications across an IT environment and automate nearly any process in the software lifecycle. Organizations often want to bring the familiar, collaborative experience of GitHub to their developers, as well as provide a more secure, common platform for working with open source communities. This makes the availability of tooling like Actions on enterprise Kubernetes platforms a crucial component for the future of IT.

Red Hat OpenShift now supports GitHub Actions, enabling organizations to standardize and scale their use of open, standardized developer toolchain components like Quay, Buildah, or Source-to-Image (s2i). This helps to meet developers where they are and provides greater choice and flexibility to OpenShift customers in how they build and deploy applications. The new GitHub Actions for Red Hat OpenShift, along with existing actions on GitHub Marketplace and action workflows, make it possible to achieve simple as well as complex application workflows on Red Hat’s enterprise Kubernetes platform using an extensive array of standards-based tools.

As part of the collaboration, GitHub has also joined OpenShift Commons, a community that helps drive connections and collaboration across the OpenShift ecosystem. Beyond Actions and GitHub Marketplace, Red Hat and GitHub are also exploring self-hosted GitHub runners for OpenShift. A runner is the combined application and server that hosts a job and carries out the steps for an Action workflow. Self-hosting runners gives IT teams more control and flexibility over the hardware and software included as part of their environment. This means that end users can increase memory size, enable GPUs, or install software that may only be available locally as part of a tailored application development experience.

The addition of GitHub Actions builds on Red Hat OpenShift’s robust developer experience, which includes OpenShift GitOps (based on ArgoCD) and OpenShift Pipelines (based on Tekton). OpenShift is now able to provide a complete solution for DevOps and GitOps practitioners as they seek to build, deploy, and maintain cloud-native applications.

Availability

GitHub Actions on Red Hat OpenShift are available now via GitHub Marketplace.

Supporting Quotes

Joe Fernandes, vice president, Products, Cloud Platforms, Red Hat

“Red Hat OpenShift is more than a Kubernetes platform for deploying cloud-native applications; it’s a powerful, flexible foundation for developers to build the latest and greatest applications. By adding GitHub Actions to our existing set of DevOps and GitOps capabilities and by working with GitHub to further refine and expand the developer experience, we aim to make Red Hat OpenShift the most complete cloud-native development platform available, one built on the open standards of Kubernetes and Linux containers and backed by the vast expertise of Red Hat.”

Jeremy Epling, vice president of Product Management, GitHub

“GitHub is the home for all developers, and we’re excited to expand our collaboration with Red Hat to accelerate software development within the enterprise. Combining Red Hat OpenShift with GitHub Actions will help our customers more securely automate nearly all their cloud-native development and DevOps workflows, providing a unified experience across the hybrid cloud that is exceptionally friendly to developers, security and operations teams. We’re looking forward to working more closely with Red Hat, and helping our customers deliver better and faster with open source software and standards.”

Originally developed by DeployHub, Ortelius was created to provide a means to automate configuration at the application level. It also provides a central catalog of services that makes it easier to consume and deploy services across clusters that also tracks application versions and dependencies as updates to services are made.

DeployHub CEO Tracy Ragan said Ortelius is now being donated to the CD Foundation, an arm of the Linux Foundation, which is working to create de facto continuous delivery standards. As such, Ortelius will benefit from contributions from other members of the consortium that includes Google, CloudBees, CircleCI, JFrog, IBM, GitLab, Netflix and DeployHub.

The core CF Foundation projects are the open source Jenkins continuous integration/continuous delivery (CI/CD) platform; Jenkins X, an open source CI/CD solution on Kubernetes; Spinnaker, an open source CD platform; and the Tekton specification for building CI/CD components.

Ortelius already integrates with CD orchestration tools and deployment tools such as Helm, Ansible and Spinnaker. The opportunity now is to address the entire application development life cycle of cloud-native applications using a GitOps methodology, said Ragan.

While in theory the rise of Kubernetes should make it easier to automate CD processes across multiple platforms, Ragan noted that in practice there are still substantial differences between instances of Kubernetes that will require tools to automate the rollout of microservices across multiple clusters. Ortelius allows IT teams to logically manage groups for microservices as a single entity in much the same way IT teams manage monolithic applications as a single entity, she said.

Ragan also noted that not all microservices are of equal value, so different approaches to managing them are required based on the number of dependencies they have and the value of the microservices to the business. As it becomes apparent IT teams will soon be managing perhaps thousands of microservices, the need for a standard approach that integrates with multiple CD platforms becomes more acute. In fact, she said, IT teams are approaching a new era when chaos engineering principles will need to be applied to the management of microservices.

Longer-term, Ragan said Ortelius will also make it feasible to employ machine learning algorithms to provide predictive analytics that can be used to optimize the microservices environment. That capability eventually will provide a critical capability for managing microservices at scale. It may also serve to further the adoption of best CD practices that thus far have proven elusive to achieve in IT environments where each platform has unique attributes that make automating application delivery challenging regardless of what type of application is involved.

In the meantime, IT organizations are being invited to contribute to the Ortelius project, now that it’s part of the CD Foundation. The more contributions that are made, the sooner the Ortelius platform graduates, which can’t come soon enough for many organizations that have already invested in microservices.

The widespread adoption of the Kubernetes standard for container orchestration has redefined how organizations manage computing deployments at the edge. In turn, more innovative use cases have emerged for lightweight Kubernetes distributions like K3s, designed for small footprint workloads.

These Kubernetes deployments can range in size from 500 to 600 single-node clusters to 15,000 to 20,000 clusters, with an average size of 1,700 clusters. As an example, an oil and gas support services company, and Rancher Labs customer, intends to support a mobile fleet of more than 600 vans that act as mini data centers. Traveling to remote oil wells, these vans use Kubernetes to conduct sensor analysis, measuring everything from drill efficiency to flagging potential gas hot spots.

Another organization has plans to use Kubernetes across its 100 factories. Single-node clusters each manage purpose-built, programmable applications that run robotics and machine components in production lines.

Single-Node Kubernetes Clusters, Multiple Issues
But the proliferation of single-node clusters creates three primary infrastructure management challenges: security, heterogeneous architectures and limited connectivity. To manage clusters on a massive scale, organizations need to overcome these challenges.

Securing the Edge
Security is a fundamental concern as enterprises push compute and storage outside the data center into hostile environments where there are no physical security barriers.

For example, thousands of people might visit a retail environment every day. There is nothing stopping someone from reaching over the counter, unplugging a small footprint device and walking away with it.

Security starts at day zero when the machine is built using a bill of materials, shipped to a location and installed. It must remain secure in transit and not be tampered with before being switched on. Once activated, the machine must verify that no rogue software has been installed. Only then can it securely register with its management system.

The day-two problem centers on the continuous secure management of these devices. Imagine the logistics if an organization has 1,700 of these clusters deployed across a continent, many in remote locations with limited or intermittent connectivity. It is simply not practical to send people to manage and verify the security of each device.

There are several open source projects that help in this regard.

The Intel Secure Device Onboard (SDO) solution provides a similar plug-and-play experience to that of connecting a USB device to a laptop. With SDO, an edge device can be securely and automatically onboarded in a zero-touch manner into the cloud environment in less than a minute.

Another open source project is Keylime, MIT’s remote attestation based on the Trusted Platform Module (TPM). This creates a Hardware Root of Trust from the TPM chip built into a device all the way through to the data center. Using the TPM cryptographic hash, Keylime conducts trusted verification of the device to ensure it has not been tampered with on a hardware or software level.

Managing Heterogeneous Architecture
An organization with a large number of edge devices might use a mix of ARM and Intel-based architectures, making multi-operating system support essential. In these cases, enterprises need to support continuous integration pipelines for each device type. Some use cases also require graphics processing units (GPUs) to support the workloads.

For instance, a fast-food chain might use GPUs for visual analysis to count the number of cars at a drive-thru. These GPUs can also be used to process languages such as the conversation between the customer at the drive-thru and the person taking the order inside the restaurant. Using GPU-backed conversational AI, interactions can then be analyzed on a national level to determine whether employees understand customers and identify areas where training can enhance the experience.

To optimize capital expense (CapEx), organizations must first define the use case for the Kubernetes clusters. Working backward from workload requirements, the appropriate hardware bill of materials is specified to support that use case. In the example of the fast-food restaurant, GPU support is critical. At scale, these Kubernetes clusters (many of which are single-node clusters) can then be engineered in a purpose-driven model so that overprovisioned hardware doesn’t go to waste.

Kubernetes must therefore be architecture-agnostic. This is critical to meet both the business demands for the customer as well as the application use case, whatever that might be.

Connecting the Edge
Kubernetes deployments also face challenges in limited or intermittent connectivity environments. This means container sizes cannot be so large as to clog the pipeline and limit how quickly updates can be delivered to the edge.

The proliferation of Kubernetes typically sees most clusters purpose-built with small hardware profiles without much memory or storage space. For example, a machine might only have 2GB of RAM, one reserved for infrastructure and the remaining memory footprint to run workloads. Development teams must therefore build applications that target small file sizes. This is also important when considering how long it might take for an update to reach a container in limited connectivity conditions. Containers must, therefore, be kept as small as possible.

This is where the GitOps operating model comes into play. It provides a set of best practices to join deployment, management and monitoring for containerized clusters and applications. Within that, Kubernetes can leverage a pull model that sees the edge “phoning home” for updates when there is connectivity in place. At scale, having tens of thousands of clusters do this is significantly more effective than trying to push down updates when there is no reliable connectivity.

A Standardized Approach for Kubernetes
These edge use cases scratch the surface of the potential for CNCF (Cloud Native Computing Foundation)-certified Kubernetes distributions to enable the internet of things.

The standardized Kubernetes API (application programming interface) is perhaps the most powerful aspect that comes from Kubernetes. This means that every Kubernetes cluster can link to any cloud environment, regardless of the service provider. With one API guiding everything, the cloud ecosystem now has a standardized way of managing all its infrastructure.

The notion of a hybrid cloud is behind us. Using a single API that can point and connect to wherever the cloud is for an edge device creates an environment where Kubernetes can be everywhere and run everything.

Gitpod, the first open-source developer platform to automate the provisioning of ready-to-code development environment has announced a $3 million seed funding from Crane Venture Partners, Speedinvest, and Vertex Ventures US, as well as a partnership and native integration with GitLab, the DevOps platform delivered as a single application.

Gitpod is an open-source GitOps solution that leverages the version control system as the canonical source of truth to automate everything a developer needs to start coding. Through automation. Gitpod frees engineering teams from the friction of manually setting-up local dev environments, Gitpod is open-source and can be self-hosted as well be used for automatically spinning up ready-to-code development environments with code-hosting platforms.

“This is an exciting time in Gitpod’s history teaming up with experienced investors in the world of developer tools and open-source software as well as working together with GitLab to provide developers with critical cloud-based dev environment automation. Gitpod’s first-mover advantage and vibrant open-source community enable developers today to treat development environments as automated (yet customisable) resources, which they can spin up whenever they need them and close down and forget about when they are finished with their task,” said Sven Efftinge, Co-Founder and CEO of Gitpod.

The native integration allows developers on GitLab to automatically spin up ready-to-code development environments via Gitpod’s open-source platform.

“Gitpod’s team has a track record of creating multiple open-source developer tools used by millions of engineers. With software engineering moving to remote but collaborative work, we see Gitpod securely allowing multiplayer mode for developers while saving VPs of Engineering hundreds of hours of tool setup-time. This will be an integral part of any professional software delivery pipeline.” said Sandeep Bhadra, Partner at Vertex Ventures US.

How Could This Be Helpful For Developers?

With GitOps, there is automation embedded in continuous delivery pipelines in software development. With automation, whenever you make changes into your Git, there is a continuous integration build and infrastructure as code in order to deploy the different environments. Once the software is running, developers automatically deploy to production.

But despite this automation, there is one piece where engineers start being creative, and modify the software with coding and debugging to try to you know to hunt down bugs or add innovative new features. The issue is that even today there still is so much friction and manual steps. Here, developers pull down the source code on their powerful machine locally and install numerous plugins in their IDEs in order to get productive.

Gitpod wants to give developers a development environment, and without much complexity and manual work, software dev environments should be automated. So, when developers want to code in an enterprise setting, they should just click a button and be able to code no matter what device they have. So in terms of dev environments, you have tools like the SDKs, compilers, runtimes, application server, database, etc. Also, there are other tools for running tests and so on.

Setting up and maintaining dev environments is tedious and requires lots of time. With Gitpod’s integration into GitLab, code reviews, bug fixes, building new features and exploring new projects is frictionless while increasing development productivity, efficiency and velocity. Developers can easily launch fresh dev environments for each new task directly from the GitLab interface via a new dropdown option from any project or merge request.

Gitpod is a true open-source solution powered by the community, spins up magically fast, can be self-hosted and is the only solution which works with almost any cloud provider and code-hosting platform including GitLab, GitHub Enterprise, and Bitbucket.

“GitLab is excited to build a partnership with Gitpod,” said Brandon Jung, vice president of alliances at GitLab. “Gitpod’s expertise in automating cloud-based dev environments on GitLab combined with their focus on cloud-native development workflows makes them a great fit for our customers helping enable them on their digital transformations.”

The Linux Foundation Releases Jenkins Essentials Course (LFS267)

The Linux Foundation, in collaboration with the Continuous Delivery Foundation (CDF), announced the availability of LFS267 – Jenkins Essentials, a new training course targeting DevOps engineers, Quality Assurance personnel, Site Reliability Engineers (SREs), software developers, and architects who want to deepen their expertise with Jenkins for CI/CD workflows.

Why Jenkins Still Matters

Jenkins is an open-source automation server that offers hundreds of plugins supporting the building, deploying, and automation of virtually any software project. First released in 2004, Jenkins has maintained its position as one of the most widely adopted CI servers in the world. Its extensibility and large community make it a common choice for teams building CI/CD pipelines at scale.

What You Will Learn in LFS267

The course covers a broad range of practical, production-relevant Jenkins skills. By the end of the course, students will be able to:

• Deploy Jenkins on Kubernetes — set up and manage Jenkins within a Kubernetes cluster for containerized workloads.
• Automate Jenkins installation with IaC — apply Infrastructure-as-Code principles to provision and configure Jenkins reliably.
• Configure a highly available Jenkins server — architect Jenkins for resilience and uptime in production environments.
• Scale with static and dynamic build agents — expand Jenkins infrastructure horizontally to handle varying build loads.
• Automate routine tasks via CLI and REST API — script and automate common Jenkins administration and job management tasks.
• Implement fine-grained security controls — secure Jenkins jobs and resources with role-based access and security policies.
• Set up effective CI/CD feedback loops — integrate notifications and reporting so teams get actionable build feedback quickly.
• Build complex multi-branch pipelines — create pipelines that mirror real-world branching strategies and release flows.
• Automate pull request workflows with GitOps — integrate Jenkins with Git repositories to trigger and validate PRs automatically.

These competencies map directly to SRE, DevOps engineer, and release engineer roles.

Prerequisites

Before enrolling in LFS267, students should have a working familiarity with Jenkins, Kubernetes, and Docker. Those who are new to any of these technologies can first complete free introductory courses from The Linux Foundation:

• Introduction to Jenkins (LFS167x)
• Introduction to Cloud Infrastructure Technologies
• Introduction to Kubernetes

About the Course Authors

LFS267 was developed by Himanshu Gautam and Deepika Gautam, co-founders of Aplima Inc., a CI/CD solutions provider.

Himanshu Gautam is an author, trainer, speaker, and software engineer with over 20 years of industry experience. He focuses on cloud-native architecture and DevOps practices, and has also authored the CI/CD with Jenkins X course (LFS268) for the Linux Foundation.

Deepika Gautam is an author, trainer, speaker, and DevOps evangelist with nearly two decades of software industry experience. She specializes in implementing DevOps toolchains across multi-cloud environments and authored the Introduction to Jenkins course (LFS167x).

About the Continuous Delivery Foundation

The Continuous Delivery Foundation (CDF) is a vendor-neutral organization under the Linux Foundation that works to establish best practices for software delivery automation, promote adoption of CD tooling, and encourage collaboration across the ecosystem. The CDF hosts several prominent open-source projects including Jenkins, Jenkins X, Tekton, and Spinnaker.

About the Linux Foundation

Founded in 2000, the Linux Foundation supports open-source collaboration across software, standards, data, and hardware. It is home to critical infrastructure projects including Linux, Kubernetes, and Node.js, and is supported by more than 1,000 member organizations worldwide.

Linux Foundation Releases Jenkins Essentials Training Course (LFS267)

The Linux Foundation, in collaboration with the Continuous Delivery Foundation (CDF), announced the availability of a new instructor-developed training course: LFS267 – Jenkins Essentials. The course targets DevOps engineers, Quality Assurance (QA) professionals, Site Reliability Engineers (SREs), software developers, and architects who want to build practical expertise with Jenkins for continuous integration and continuous delivery (CI/CD) workflows.

Why Jenkins Still Matters

Jenkins is an open-source automation server that has been a cornerstone of software delivery pipelines since its inception in 2004. With hundreds of plugins covering build, test, deploy, and notification workflows, it remains one of the most widely adopted CI servers in the industry. As organizations migrate workloads to Kubernetes and adopt GitOps practices, the need to operate Jenkins at scale—and to integrate it cleanly into cloud-native environments—has grown significantly.

What You Will Learn in LFS267

The course is structured to take learners from foundational Jenkins operations through advanced, production-grade configurations. Key topics include:

• Running Jenkins on Kubernetes – deploying and managing Jenkins controller and agents inside a Kubernetes cluster.
• Infrastructure-as-Code (IaC) automation – automating Jenkins installation and configuration using IaC principles to eliminate manual, error-prone setup.
• High availability – architecting a highly available Jenkins environment to minimize downtime.
• Scaling with static and dynamic agents – leveraging both pre-provisioned static build agents and on-demand dynamic agents to match workload requirements.
• CLI and REST API automation – scripting routine Jenkins administrative tasks to reduce toil.
• Fine-grained security controls – implementing role-based access and job-level security to protect CI/CD pipelines.
• Effective CI/CD feedback loops – configuring notifications and reporting so teams get actionable build and test feedback quickly.
• Multi-branch pipelines – building complex, real-world pipelines that handle feature branches, release branches, and hotfixes simultaneously.
• GitOps-driven pull request workflows – automating PR validation and merge gates using GitOps principles.

Together, these skills map directly to SRE, DevOps engineer, and release engineer job responsibilities.

Prerequisites

Students should arrive with a working understanding of Jenkins, Kubernetes, and Docker before enrolling. If you need to fill gaps in any of those areas, the Linux Foundation offers free introductory courses that can serve as on-ramps:

• Introduction to Jenkins (LFS167x) – free foundational Jenkins course.
• Introduction to Cloud Infrastructure Technologies – covers core cloud and container concepts.
• Introduction to Kubernetes – covers Kubernetes fundamentals needed to follow the cluster-deployment labs.

Course Authors

LFS267 was developed by Himanshu Gautam and Deepika Gautam, co-founders of Aplima Inc., a CI/CD solutions provider.

Himanshu brings more than 20 years of software engineering experience. He is a practitioner focused on cloud-native architecture and DevOps culture, and also authored the CI/CD with Jenkins X (LFS268) course for the Linux Foundation.

Deepika has nearly two decades of software industry experience specializing in DevOps toolchain implementation across multi-cloud environments. She authored the Introduction to Jenkins (LFS167x) course.

Pricing and Access

At the time of announcement, LFS267 was priced at $299, which includes unlimited access to all course content and hands-on labs for one year. The course was available to enroll immediately upon announcement.

Note: Pricing and availability may have changed since the original October 2020 announcement. Check the Linux Foundation Training catalog for current details.

About the Linux Foundation and the Continuous Delivery Foundation

Founded in 2000, the Linux Foundation is a nonprofit organization that hosts collaborative development of critical open-source infrastructure, including Linux, Kubernetes, and Node.js, and is supported by more than 1,000 member organizations.

The Continuous Delivery Foundation (CDF) is a vendor-neutral, Linux Foundation-hosted organization dedicated to improving software delivery through security and speed. The CDF stewards some of the fastest-growing CD projects, including Jenkins, Jenkins X, Tekton, and Spinnaker, and drives best-practice education and cross-project collaboration across the CD ecosystem.

How DevOps Gave Rise to GitOps

DevOps emerged as the industry’s first systematic attempt to create a fluid pipeline from development to operations. Its core promise was straightforward: pass application characteristics, configuration requirements, and deployment instructions from one team to the next in a structured, repeatable way. Most early DevOps tooling focused on converting developer-authored instructions into deployment steps — scripts, configuration files, and pipeline definitions that operations teams could execute reliably.

Two pressures complicated that model as software architectures evolved:

• Increasing componentization. Applications decomposed into services and microservices introduced complex multi-component relationships that flat pipeline tools struggled to manage.
• Faster release cadences. CI/CD meant that a single “release” was no longer a monolithic event but a stream of component-level changes, each moving through the pipeline at its own pace.

The logical response to both challenges was a shared, version-controlled repository as a single source of truth — one that could anchor both development activity and operations configuration. Git was already the dominant choice for storing version-controlled code, so building an operations practice around it, and connecting development and operations through Git-based exchanges, was a natural step. That is the origin of GitOps.

Core Philosophical Differences

DevOps: Operations-Centric and Prescriptive

DevOps is fundamentally an operations-centric discipline. Its earliest tools were largely prescriptive — script-driven workflows that spelled out exactly what should happen at each deployment step. This approach suits environments where applications change slowly, infrastructure configuration changes frequently, and hardware issues are a daily operational concern.

DevOps accommodates both declarative and imperative (prescriptive) styles, and it works equally well with monolithic applications, partially componentized apps, virtual machines, and bare-metal servers. Its versatility is one of its most valuable attributes — particularly for organizations managing heterogeneous infrastructure or navigating a gradual cloud migration.

DevOps tooling has expanded over time to cover monitoring, configuration management, and infrastructure as code. What it has not historically provided is strong, native support for the development process itself — a gap that becomes more visible as CI/CD practices push development and operations closer together.

GitOps: Developer-Centric and Declarative

GitOps starts from a different premise. Rather than prescribing a sequence of deployment steps, GitOps declares a desired state — the contents of a Git repository describe what the system should look like — and then relies on automation to reconcile the actual system state with that declared state. This declarative model aligns naturally with cloud-native architectures built around services and microservices.

Because it is developer-centric and built around the repository as the source of truth, GitOps fits naturally into CI/CD workflows. It allows IT administrators to manage the coordinated deployment of multi-component applications where each component evolves on its own schedule. Changes are made through pull requests; the repository becomes the audit trail, the rollback mechanism, and the deployment trigger all in one.

GitOps vs. DevOps with Containers and Kubernetes

Containers and Kubernetes have sharpened the distinction between the two approaches. In practice, most DevOps users do not exclusively deploy containerized workloads — DevOps tools are designed to span a wide range of infrastructure models. GitOps, by contrast, has largely evolved within the container ecosystem, and the overwhelming majority of GitOps practitioners are running containerized applications.

Kubernetes is the de facto orchestration platform for containers, and it is a natural fit for the GitOps model: Kubernetes already operates declaratively, managing cluster state against a defined specification. GitOps tooling — such as Flux and Argo CD — builds on that foundation by treating the Git repository as the authoritative specification for cluster state, triggering reconciliation whenever the repository changes.

In this context, DevOps tooling can be adapted to containerized environments, but doing so requires reconciling imperative pipeline logic with Kubernetes’ declarative ecosystem — and the friction can be significant. GitOps sidesteps much of that friction because its model was designed with containers in mind from the start.

How GitOps and DevOps Relate to Each Other

GitOps is not a subset of DevOps, nor does it replace it entirely. The relationship is better understood as follows:

• In a pure GitOps model, DevOps tooling may still handle aspects of the CI pipeline — building images, running tests — while GitOps tooling takes responsibility for the CD side, watching the repository and reconciling cluster state.
• GitOps can feed inputs into a DevOps pipeline, but the reverse is less natural. A DevOps pipeline is not designed to manage the kind of continuous reconciliation loop that GitOps relies on.
• For teams committed to containerized, cloud-native application models, GitOps and the Kubernetes ecosystem together can fulfill many of the roles traditionally occupied by DevOps tooling — meaning DevOps is not always a required layer.

Which Approach Is Right for Your Team?

The right answer depends on where your organization sits in its infrastructure journey:

Organizations that are still running predominantly VM or bare-metal workloads, or that have not yet committed to containerization, will find DevOps tooling more immediately applicable. Organizations that are building new systems on Kubernetes and want a clear audit trail, fast rollbacks, and a developer-friendly deployment model should seriously evaluate a GitOps approach.

For many teams, the answer will be a hybrid: DevOps tooling handling CI — code build, test, and image publish — while GitOps tooling manages the CD phase, watching the repository and keeping cluster state aligned with the declared configuration.

Looking Ahead

If the long-term trajectory of enterprise infrastructure is toward containers and cloud-native architectures, then the declarative, repository-centric model at the heart of GitOps is well-positioned to become the dominant paradigm for application delivery. DevOps practices and tooling are not going away — their breadth and maturity are genuine strengths — but the gravitational pull of Kubernetes and cloud-native development is steadily narrowing the gap between the two approaches, and in some environments, GitOps is already the more natural choice.

D2iQ Expands Into CI/CD With Dispatch

D2iQ — the company formerly known as Mesosphere — extended its Kubernetes portfolio by releasing Dispatch, a cloud-native CI/CD platform integrated directly into its Konvoy Kubernetes distribution. The move signals D2iQ’s intent to offer a more complete platform story, covering not just cluster management but also the continuous integration and delivery pipelines that run on top of those clusters.

Core Design Philosophy: Separation of Concerns and GitOps

Dispatch is designed around two guiding principles: a clear separation of responsibilities between developers and operators, and a GitOps-first approach to pipeline and application configuration. In practice, this means that both pipeline definitions and deployment configuration live in a Git repository, giving every stakeholder a single, version-controlled source of truth for the state of the system.

This GitOps model is increasingly common in the cloud-native ecosystem, but Dispatch’s integration with D2iQ’s existing toolchain — including Konvoy and the Kommander cluster management layer — gives it a tighter operational context than standalone CI/CD tools typically provide.

Multi-Language Pipeline Configuration

One of Dispatch’s headline differentiators is its support for four domain-specific languages for writing pipeline configuration:

• JSON — familiar to most developers and widely tooled
• YAML — the de facto standard in Kubernetes-adjacent tooling
• CUE — a configuration language derived from JSON with added constraints and data validation
• Starlark — a Python-like scripting language originally developed for the Bazel build system, designed to be deterministic and embeddable

Most competing CI/CD platforms in the cloud-native space default to YAML exclusively. Offering CUE and Starlark alongside the more common formats gives teams with complex pipeline logic more expressive options, and reduces the friction of adopting the tool for teams already working in those languages.

Built on Tekton and Argo CD

Dispatch does not build its execution engine from scratch. According to D2iQ, it is designed to work alongside Tekton (a Kubernetes-native CI/CD framework originally from Google) and Argo CD (a declarative GitOps continuous delivery tool for Kubernetes). This means Dispatch acts partly as an orchestration and configuration layer on top of established CNCF-ecosystem components, rather than reinventing the underlying pipeline execution model.

Multi-Tenancy via Kubernetes Namespaces

Dispatch leverages Kubernetes’ native namespace model to deliver built-in multi-tenancy. Teams can run their pipelines in isolated namespaces, providing resource separation and access control without requiring a separate tenancy management system. This is a practical advantage in enterprise environments where multiple teams share the same cluster infrastructure.

Context: A Crowded CI/CD Landscape

Dispatch arrived into an already busy space. The cloud-native CI/CD problem was prominently discussed at KubeCon Europe 2018 in Copenhagen, which prompted a wave of new projects across the ecosystem. By the time Dispatch launched, the CI/CD section of the cloud-native landscape included dozens of projects at varying stages of maturity.

This level of competition cuts both ways. It validates the problem space, but it also raises the bar for differentiation. Dispatch’s primary advantages — GitOps integration, multi-language support, and tight coupling with D2iQ’s Konvoy and Kommander toolchain — are most compelling for teams already invested in the D2iQ ecosystem.

Availability and Trial

At the time of this report, Dispatch was available to try for free as part of the Konvoy trial offering. The trial bundle also included:

• A declarative universal operator for managing Kubernetes workloads
• Kommander — D2iQ’s tool for federated, cross-cluster management

This bundled approach reflects D2iQ’s broader strategy: positioning Konvoy not just as a Kubernetes distribution, but as an integrated platform for running production workloads end-to-end, from cluster provisioning through to continuous delivery.

Summary

Dispatch represents D2iQ’s entry into the cloud-native CI/CD market, with a design that prioritizes GitOps workflows, multi-language flexibility, and Kubernetes-native multi-tenancy. Its reliance on Tekton and Argo CD as underlying components gives it a foundation in proven open-source tooling, while its integration with Konvoy and Kommander makes it a natural fit for existing D2iQ customers. Teams outside the D2iQ ecosystem will need to weigh those integration benefits against the crowded field of alternatives.

D2iQ Brings GitOps-Driven CI/CD to Enterprise Cloud-Native Workloads

D2iQ, a provider of enterprise-grade cloud platforms focused on Day 2 operations, announced the general availability of Dispatch on March 4, 2020. Dispatch is an integrated GitOps platform built to help engineering teams rapidly build, test, and deploy cloud-native, microservices-based applications at scale.

The platform targets a core challenge many organizations face when modernizing their software delivery pipelines: reducing the complexity of getting new code into production consistently and repeatably, without slowing down development velocity.

The Problem Dispatch Aims to Solve

As software teams face mounting pressure to ship features faster, the operational overhead of managing deployment workflows can become a significant bottleneck. Organizations adopting microservices architectures often struggle with:

• Coordinating responsibility between application developers and platform operators
• Maintaining consistency across automated deployment pipelines
• Reducing the manual steps required to promote code from development to production
• Managing the full application lifecycle beyond initial deployment — what D2iQ refers to as Day 2 operations

Traditional CI/CD toolchains often require significant custom integration work and can blur the lines between developer and operator responsibilities, introducing both risk and toil.

What Is Dispatch?

Dispatch is an integrated CI/CD platform built on open-source technologies that enables a GitOps approach to application delivery. In a GitOps model, Git repositories serve as the single source of truth for both application code and infrastructure configuration. Changes to the desired state of a system are made through pull requests, and automated processes reconcile the live environment to match the declared state in Git.

Key characteristics of Dispatch as described at launch:

• Separation of concerns: Dispatch enforces a clear division between developer workflows (continuous integration, building and testing code) and operator workflows (continuous delivery, managing deployments).
• Open-source foundations: The platform is built on prevailing open-source technologies, giving organizations flexibility in tooling choices and reducing vendor lock-in.
• Full CI/CD lifecycle: Dispatch covers continuous integration through to continuous delivery, providing end-to-end automation for the application delivery pipeline.
• Cloud-native focus: Designed specifically for microservices-based applications running on modern cloud infrastructure.
• Day 2 readiness: Beyond initial deployment, Dispatch is positioned to support ongoing operational management — patching, scaling, and lifecycle management after an application is in production.

GitOps and Day 2 Operations: Why They Matter Together

D2iQ has long emphasized Day 2 operations — the work that happens after initial deployment — as the real challenge in enterprise infrastructure. Most teams can get software deployed; keeping it running reliably, securely, and efficiently at scale is where complexity compounds.

Combining GitOps workflows with Day 2 operational tooling addresses this by:

• Providing auditable, version-controlled records of every change to application and infrastructure state
• Enabling rollbacks through standard Git operations (reverting a commit) rather than manual intervention
• Allowing operators to manage application configurations declaratively, reducing configuration drift
• Supporting automated reconciliation so the live environment stays consistent with the desired state defined in Git

D2iQ Leadership Perspective

“Moving from legacy to cloud native applications is becoming a requirement for organizations that want to keep pace with the digital economy. Dispatch leverages D2iQ’s expertise in building distributed systems for some of the largest global organizations to deliver business logic that enables enterprises to accelerate their cloud native CI/CD pipeline through GitOps processes. This empowers organizations with a clear path to success and business impact as their cloud native journey continues to Day 2 operations.”

— Ben Hindman, Co-founder and Chief Product Officer, D2iQ

Context: D2iQ’s Position in the Cloud-Native Ecosystem

D2iQ (formerly Mesosphere) built its reputation on distributed systems tooling, including the DC/OS platform and Kubernetes-based products. Dispatch represents the company’s push to extend its platform capabilities to cover the full software delivery lifecycle, not just cluster management and orchestration.

The launch of Dispatch placed D2iQ in direct competition with a growing field of CI/CD and GitOps tooling, including projects like Argo CD, Flux, Tekton, and Jenkins X — all of which are also built on open-source Kubernetes-native foundations.

Key Takeaways for DevOps Teams

• Dispatch targets enterprises that need a structured, opinionated GitOps platform rather than assembling their own CI/CD toolchain from scratch.
• The emphasis on separating developer and operator responsibilities can help larger organizations scale DevOps practices without creating cross-team bottlenecks.
• Being built on open-source technologies means teams are not entirely locked in, but the degree of openness and extensibility would require hands-on evaluation.
• The Day 2 operations focus differentiates Dispatch from tools that address only the initial deployment problem.

This article is based on D2iQ’s March 4, 2020 press release announcing the general availability of Dispatch. For current product information, consult D2iQ’s official documentation and release notes directly.

Overview

Google introduced Application Manager for Google Kubernetes Engine (GKE) as a beta feature aimed at simplifying GitOps workflows for Kubernetes deployments. The tool, centered around a component Google calls Application Delivery, is designed to attract developers who want code-oriented, repository-driven deployment automation without stitching together multiple third-party tools.

What Is GitOps?

GitOps is an operational model in which the desired state of infrastructure and application deployments is stored declaratively in a Git repository, alongside application source code. Key principles include:

• Declarative configuration: Infrastructure requirements are described in version-controlled files, typically YAML.
• Pull-request-driven changes: Any change to infrastructure or configuration goes through a standard Git review and merge process.
• Automated reconciliation: A controller or agent continuously ensures the live environment matches the state described in the repository.
• Full audit trail: Git history provides a complete, tamper-evident record of every change.

GitOps has become increasingly popular for Kubernetes environments because Kubernetes itself is API-driven and configuration is naturally expressed in YAML manifests, making it a natural fit for repository-based management.

How Google’s Application Manager Works

Application Manager runs as an add-on inside a GKE cluster and is controlled through a command-line interface invoked with the appctl binary. Here is the general workflow:

1. Repository Setup

Repositories must be hosted on either GitHub or GitLab. After installing Application Manager into a GKE cluster, developers initialize their project with appctl, which provisions two default repositories:

• One for application source code
• One for deployment configuration

Separating configuration from code is a common GitOps best practice, as it allows deployment state to evolve independently of the application’s release cycle.

2. Defining Kubernetes Configuration with Kustomize

Developers describe their Kubernetes deployment requirements in YAML using a Kustomize configuration. Kustomize is a Kubernetes-native configuration management tool that allows base manifests to be patched for different environments (e.g., staging vs. production) without duplicating YAML files.

3. Adding Environments

Using appctl commands, developers register named environments such as testing, staging, and production. Each environment maps to a distinct Kubernetes namespace or cluster target, and the configuration for each is tracked in the deployment repository.

4. Preparing and Reviewing a Release

When a developer is ready to deploy, they run:

appctl prepare

This command tags the application version and automatically opens a pull request in the configuration repository. The team can review infrastructure and configuration changes exactly as they would review code changes. This is the core GitOps loop: changes are proposed, reviewed, and approved before anything reaches the cluster.

5. Applying the Deployment

Once the pull request is approved and merged, the developer runs:

appctl apply

This creates or updates the target environment and deploys the application to the GKE cluster according to the approved configuration.

6. Promoting and Rolling Back

appctl prepare can also be used to promote a verified deployment from one environment to another — for example, from staging to production. Rollbacks are also supported, leveraging Git history to revert to a previously known-good state.

Observability and the Google Cloud Console

Application Manager surfaces deployment metadata in the Google Cloud Console, including:

• Current deployed version
• Revision history
• Application health status

This gives teams a consolidated view of deployment state without needing to query the cluster directly or consult the Git log manually.

Google Cloud Marketplace Integration

Google also designed Application Manager with third-party applications in mind. Applications installed from the Google Cloud Marketplace could potentially use Application Manager for version updates, allowing cluster operators to trigger upgrades of marketplace applications directly from the Cloud Console with an Update button, rather than performing manual Helm or kubectl operations.

CI/CD Pipeline Integration

Application Manager is designed to complement existing CI/CD tooling rather than replace it. It is intended to integrate with:

• Jenkins — a widely used open-source CI/CD server
• Tekton Pipelines — a Kubernetes-native CI/CD framework that Google has contributed to and champions

For teams that want a leaner setup, Application Manager also provides a self-contained GitOps workflow without requiring additional orchestration tools.

Limitations to Be Aware Of

Application Manager simplifies the automation of Kubernetes deployments, but it does not simplify the design of those deployments. Teams still need to:

• Understand Kubernetes resource types (Deployments, Services, Ingress, etc.)
• Write correct and appropriately scoped YAML manifests
• Configure Kustomize overlays for each environment
• Manage secrets and RBAC policies separately

The tool assumes you already know what to deploy; it automates how that deployment is tracked, reviewed, and executed.

Strategic Significance for Google Cloud

Developer experience is a key competitive differentiator among major cloud providers. By reducing friction in the GitOps setup for GKE, Google is lowering the barrier for development teams to adopt repeatable, auditable deployment practices on its platform. For organizations already invested in GitHub or GitLab and Kubernetes, Application Manager offers a tightly integrated path to GitOps without requiring teams to evaluate and integrate standalone tools such as Argo CD or Flux independently.

Whether Application Manager has matured significantly since its 2020 beta launch, or whether Google has evolved its GitOps strategy (for example, through Config Sync or other GKE features), warrants a check against current Google Cloud documentation before making adoption decisions.

Scott Van Kalken, systems engineer at F5 Networks and organizer of major DevOps events in Australia, has observed this pattern repeatedly. Drawing on a career that spans both development and operations roles, he argues that at least two foundational elements are required to break through those obstacles: collaborative team structures and a safe place to fail.

Collaboration: Tearing Down the Wall

One of the core principles of DevOps is that software does not get thrown over the wall between functions. Development, testing, security, and operations teams must understand they are working toward the same outcome—not competing for ownership of a pipeline stage.

Building that shared understanding is harder than it looks. Van Kalken points to a financial services company he worked with that was moving to a GitOps model. The service management team initially viewed GitOps as “uncontrolled chaos”—a reasonable concern from people responsible for change governance.

The breakthrough came through a series of workshops that demonstrated the underlying change management processes had not changed—only the tooling had. Approvals that used to happen on paper now happened in the repository. Service management remained the final gatekeeper; the mechanism for gatekeeping simply became version-controlled and auditable.

The lesson: cross-functional buy-in rarely comes from mandates. It comes from showing people that their legitimate concerns have been accounted for in the new model.

Creating a Safe Place to Fail

Psychological safety—the confidence that raising problems or making mistakes will not result in punishment—is a prerequisite for the iterative experimentation that DevOps depends on. Without it, teams optimize for avoiding blame rather than improving systems.

Van Kalken frames this as an organizational maturity question. A mature engineering culture acknowledges that change carries risk, then puts practices in place to manage that risk—rather than trying to eliminate change altogether.

Several DevOps practices make “safe failure” operational rather than aspirational:

• GitOps and version-controlled deployments: If a release introduces a problem, rolling back to a known-good state is a straightforward, auditable operation rather than a scramble.
• Smaller, more frequent releases: Incremental changes have a smaller blast radius when something goes wrong. A two-line configuration change that causes an incident is far easier to diagnose and revert than a three-month feature release.
• Canary deployments: Rolling a change out to a subset of users before full release limits exposure and provides real-world signal beyond what pre-production testing can offer. Van Kalken notes this technique is equally applicable in DevSecOps contexts, where limiting the blast radius of a security-related release is critical.

On release cadence, Van Kalken makes a practical point that is often missed: increasing frequency does not necessarily mean multiple deployments per day. For some organizations, moving from two releases per year to three or four is a significant and valuable step. The right cadence depends on the organization, its users, and its risk appetite.

Chaos Engineering: The Ultimate Test of Cultural Readiness

Chaos engineering—deliberately injecting failures such as killing containers or saturating network links—is arguably the most demanding test of whether an organization has genuinely embraced failure as a learning mechanism.

The technical objective is to verify that production systems can tolerate partial failures gracefully, providing confidence that goes beyond what pre-deployment testing alone can offer. But the cultural prerequisite is equally demanding: every stakeholder—developers, security teams, business owners—must understand that the goal is resilience, and that provoking controlled failures in a test context is how resilience gets proven.

“Your culture has to be OK with this iterative approach,” Van Kalken said. Organizations that still treat failure as inherently bad will struggle to get chaos engineering off the ground—and will likely struggle with DevOps adoption more broadly.

His prescription for building that culture combines education, awareness, and accumulated experience. People who have seen a fast rollback prevent a major outage, or watched a canary deployment catch a regression before it hit all users, develop a different relationship with risk than people who have only ever experienced failure as a crisis.

Measuring the Right Things

Collaboration and psychological safety can be undermined by the wrong metrics. Van Kalken describes metrics as “probably the biggest things organizations need to change” when adopting DevOps.

Traditional IT metrics—system uptime percentages, ticket closure rates, mean time between failures—measure the health of internal processes. They do not necessarily correlate with what users and the business actually care about.

He uses a straightforward illustration: a customer who ordered a pizza does not care whether the order management system maintained a 99.9% uptime SLA. They care whether the pizza arrived on time. When it did not, the right response is a collaborative, analytical investigation—what specifically failed, why, and what can be changed to prevent recurrence—not a hunt for whose uptime metric to penalize.

Metrics that reward teams for protecting their own numbers create exactly the adversarial dynamics that DevOps is designed to dismantle. Metrics that measure outcomes customers care about give cross-functional teams a common target to rally around.

Practical Steps for Engineering Leaders

Van Kalken’s practical guidance for teams trying to build the right culture can be summarized as follows:

• Get people together early. Bringing stakeholders in at the start of a project—rather than handing off between silos—reduces the risk of teams developing adversarial narratives about each other. Early involvement also increases buy-in: people who helped shape a process are more likely to support its implementation.
• Keep communication open. “If people aren’t talking, you have a bad culture,” Van Kalken said. Breaking the ice between teams with a history of limited engagement can be difficult, but it is a necessary step. Workshops, joint retrospectives, and shared on-call rotations are all mechanisms for building those connections.
• Respond to failure analytically, not punitively. When an incident occurs, the organization’s response signals its actual values more clearly than any policy document. A blameless post-mortem culture, where the goal is systemic improvement rather than individual accountability, reinforces the psychological safety that DevOps requires.
• Align metrics to outcomes. Review whether current performance indicators measure internal process health or external value delivery. Where there is a mismatch, work to introduce metrics that give teams a shared, customer-focused definition of success.

“DevOps is really just a methodology to achieve a better outcome,” Van Kalken said. The tools and automation are means to that end. The culture—collaborative, iterative, analytically honest about risk—is what makes those tools effective.

This article is based on commentary by Scott Van Kalken, systems engineer at F5 Networks, originally reported by devops.com.

What Is GitOps?

At its core, GitOps is the practice of defining infrastructure and operational procedures as code, stored and managed in a Git repository. Git becomes the single source of truth for everything running in production—and in staging and development environments as well.

Declarative tools such as Terraform, AWS CloudFormation, and Kubernetes make this practical. Because these tools describe the desired state of a system rather than a sequence of imperative commands, the state of your infrastructure can be expressed in files that live in source control just like application code.

However, GitOps is more than Infrastructure as Code. True operational practice also requires a structured process for planning, reviewing, approving, and releasing changes—whether those changes are to application deployments or to foundational infrastructure such as networking and security controls. GitOps covers that full lifecycle. When implemented well, every change to infrastructure follows the same pull-request-based workflow used for application code: propose, review, approve, merge, and deploy.

The practical results of this model are significant:

• Full audit trail: Every change is recorded in Git history, making compliance and forensic investigation straightforward.
• Easy rollback: Reverting a problematic change is as simple as reverting a commit or merge request.
• Repeatability: Infrastructure defined in code can be reliably reproduced across environments.
• Visibility: The Git repository answers the “what changed and when?” question without requiring anyone to hunt through change logs or ask colleagues.

What Technology Does GitOps Require?

Adopting GitOps requires assembling a toolchain that spans version control, declarative infrastructure provisioning, Kubernetes orchestration, and a CI/CD platform to tie them together.

Version Control

A Git-based repository is the non-negotiable foundation. All infrastructure definitions, runbooks, and configuration live here. GitHub, GitLab, Bitbucket, and self-hosted Gitea are all viable options, though the surrounding workflow tooling matters as much as the repository itself.

Declarative Infrastructure Tools

Terraform is widely used for provisioning cloud infrastructure (compute, networking, databases, DNS, and so on) across providers. Because Terraform state is declarative, diffs between the current state and desired state can be reviewed before applying changes—a natural fit for pull-request-based workflows.

Kubernetes manages containerized application workloads declaratively using YAML manifests. Tools such as Helm (for packaging Kubernetes manifests as versioned charts) and Flux or Argo CD (for continuous reconciliation of the cluster state against a Git repository) extend Kubernetes into a full GitOps delivery platform.

CI/CD and Workflow Platform

A platform that integrates issue tracking, merge requests, and CI/CD pipelines is what wraps the operational process around the infrastructure tooling. GitLab is one example of an end-to-end solution that can manage the backlog of infrastructure requests, route them to the appropriate engineer, facilitate review and approval, and trigger automated execution of changes through its built-in CI/CD system. Other teams assemble equivalent workflows from separate tools—GitHub Actions for CI/CD, Jira for issue tracking, and dedicated GitOps controllers such as Flux or Argo CD for Kubernetes reconciliation.

The key requirement is not a specific product but a coherent workflow: every change should originate as an issue or request, be implemented as a code change in Git, pass through peer review, and be deployed automatically or semi-automatically by the pipeline—never applied manually in production.

What Are the Benefits of GitOps?

GitOps is one of the few DevOps practices that delivers concrete improvements for both developers and operations engineers simultaneously.

Collaboration Replaces Isolation

Traditional operations work is often performed by a single engineer working in isolation—making changes directly on servers or in cloud consoles that nobody else can see. GitOps makes every infrastructure change visible and collaborative. Pull requests invite review from peers, architects, and security teams before anything reaches production.

Consistency and Repeatability

Because all infrastructure is defined in code, environments can be reproduced reliably. The “it works on my machine” or “I set that up manually six months ago” problems are eliminated. New environments—for testing, disaster recovery, or regional expansion—can be spun up from the same Git repository that drives production.

Faster Recovery

When something goes wrong, GitOps shortens the mean time to recovery. The Git history shows exactly what changed and when. Rolling back is a revert commit away. And because the desired state is always expressed in code, automated reconciliation tools can detect and correct drift from that state.

Built-in Compliance and Auditability

Regulated industries that require change-management documentation benefit directly from GitOps. Every merge request serves as an auditable record of who proposed a change, who reviewed it, who approved it, and when it was deployed. This can satisfy audit requirements that previously demanded separate change-management systems.

Continuous Delivery for Infrastructure

GitOps enables teams to apply infrastructure updates continuously—at the cadence of application delivery rather than in infrequent, high-risk maintenance windows. Smaller, more frequent changes are easier to test, easier to review, and much easier to roll back.

Possible Challenges

GitOps is not without friction, and organisations that underestimate the cultural and process changes involved often struggle with adoption.

Discipline Is Non-Negotiable

GitOps only works if the team commits to using it for every change. The temptation to make a quick manual fix directly in production—”cowboy engineering”—is real, especially under time pressure. Every manual change that bypasses Git undermines the audit trail, creates configuration drift, and erodes the trust that the repository is actually the source of truth. Teams adopting GitOps need explicit agreement and cultural reinforcement that the Git workflow is the only path to production.

Documentation and Written Communication

GitOps requires engineers to write things down: in merge request descriptions, in issue comments, in runbooks stored alongside the infrastructure code. For teams with a strong oral or ad-hoc culture, this is a genuine adjustment. The value of the audit trail is directly proportional to the quality of the written context surrounding each change.

Toolchain Complexity

Assembling and maintaining a GitOps toolchain—Git repository, Terraform, Kubernetes, Helm, a GitOps controller such as Flux or Argo CD, and a CI/CD platform—introduces its own operational overhead. Teams without existing Kubernetes expertise may find the learning curve steep. Starting with a smaller scope (see below) helps manage this complexity.

Secret Management

Storing everything in Git creates an obvious tension with secrets management. Credentials, API keys, and certificates must never be committed to a repository in plain text. GitOps workflows require a complementary secrets management strategy—using tools such as HashiCorp Vault, AWS Secrets Manager, or Kubernetes-native solutions like Sealed Secrets or External Secrets Operator—to keep sensitive values out of Git while still enabling automated deployments.

Getting Started: Start Small and Iterate

If GitOps sounds like a fit for your organisation, the practical advice is to resist the urge to roll it out everywhere at once. Pick a small, well-defined area—a single service, a single environment, or a single piece of infrastructure—and implement the full GitOps workflow there first. Get a working example that the team can learn from, demonstrate the value, and then expand from that foundation.

This iterative approach reduces risk, builds the team’s confidence and skills with the toolchain, and surfaces process problems at a manageable scale before they affect critical systems.

Summary

GitOps is not a silver bullet, but for teams already comfortable with Git-based workflows for application development, extending those same habits to infrastructure management offers a coherent, auditable, and repeatable approach to operations that scales with organisational complexity.

The term GitOps was coined by Alexis Richardson at Weaveworks, initially in the context of running Kubernetes-based infrastructure. But the concept is broader than Kubernetes: it’s a philosophical and operational approach to software development and deployment applicable to any organization running its own code on its own infrastructure.

This article draws on a piece originally published at thenextweb.com and expands on the core concepts for a DevOps engineering audience.

What Is GitOps?

GitOps is the culmination of several trends in modern software engineering — version control, infrastructure-as-code, and CI/CD automation — taken to their logical conclusion. Together, these practices create a workflow where:

• The entire system state is declared in a Git repository.
• Automated tooling continuously reconciles the live environment to match what is declared in Git.
• Every change to the system goes through a pull request, creating a complete audit trail.

In short: Git is the source of truth, and the system automatically converges to match it.

The Three Pillars of GitOps

1. Single Source of Truth

The first and most fundamental GitOps principle is that Git — or any version control system you use — is the single source of truth for application code, infrastructure definitions, and configuration. This means:

• The complete state of your system can be understood by reading the repository.
• There are no hidden settings changed manually on a server somewhere.
• If something is wrong, the problem exists in the repo — and it’s in the commit history.

This principle eliminates the classic problem of configuration drift, where the live environment silently diverges from what anyone intended.

2. Everything as Code

GitOps extends “infrastructure as code” to cover the full system description:

• Application code — your services and their dependencies.
• Configuration as code — environment configs stored in YAML or similar formats in the repo, not in a database or configuration management UI.
• Infrastructure as code — compute resources, networking, and cluster definitions expressed as code (e.g., Terraform, Kubernetes manifests, Docker Compose files).

When all three are present in the repository, the repo becomes a complete, human-readable description of the system.

3. CI/CD Automation

This is where the model becomes powerful. Automated CI/CD pipelines watch the repository and act on changes:

• A merged pull request triggers a pipeline.
• The pipeline tests, builds, and deploys the system to match the declared state in Git.
• If the live system drifts from the declared state (due to hardware failure, manual intervention, or any other cause), reconciliation tooling detects the divergence and corrects it.

Nick Young, Principal Engineer at Atlassian, described this elegantly as “declaring the desired state of things and then waiting for the system to bring the world into line with the desired state.”

Why Adopt GitOps?

GitOps delivers benefits at both the philosophical and practical level.

Auditability and Reversibility

Every change to the system passes through a pull request. This gives you:

• A full audit trail of who changed what and why.
• Instant rollback capability — revert the commit, and the automation restores the previous state.
• Peer review as a standard gate before any change reaches production.

Consistency and Reduced Context-Switching

Instead of bouncing between cloud console UIs, configuration management dashboards, and code editors, developers work entirely in one tool: Git. Managing application code and managing infrastructure use the same workflow.

Faster Recovery

When an incident occurs, the entire system state is readable in the repository. Debugging and remediation start from a known, documented position rather than archaeological investigation of manual changes.

New Challenges GitOps Introduces

GitOps is not without its complications. Understanding them before adoption saves significant pain.

Shifting Quality Gates Further Left

In a GitOps workflow, merging to the main branch can directly trigger a production deployment. This means:

• Pre-merge validation becomes critical. A typo in a YAML Ansible template or a Kubernetes manifest can break or expose a production system.
• Automated linting, schema validation, and policy checks (e.g., Open Policy Agent) on pull requests are essential safeguards, not optional extras.
• Developers must build habits around validating infrastructure code as rigorously as application code.

Secrets Management

Storing everything in Git raises an immediate question: what about secrets? Credentials, API keys, and certificates must never be stored in plaintext in a repository. GitOps workflows require a dedicated secrets management strategy — such as integrating with a secrets manager and referencing secrets by reference rather than value in the repository.

Drift Detection and Reconciliation

The live system can change in ways Git does not know about — hardware failures, emergency manual interventions, or misbehaving operators. A complete GitOps implementation requires tooling that continuously monitors the live system and alerts on — or automatically corrects — divergence from the declared state in the repository.

Progressive Delivery

Because changes can reach production quickly, GitOps workflows typically rely on canary deployments or feature flags to limit blast radius. A canary deployment routes only a small percentage of traffic to a new version, allowing teams to validate behavior before full rollout. This is not unique to GitOps, but it becomes more important when the pipeline between commit and production is short.

GitOps and Kubernetes

GitOps originated in the Kubernetes ecosystem, and Kubernetes is well-suited to it: Kubernetes is itself declarative by design, accepting YAML manifests that describe desired state and reconciling the cluster to match. Tools like Flux and Argo CD implement GitOps reconciliation loops specifically for Kubernetes clusters, continuously syncing cluster state to a Git repository.

That said, the underlying principles — version-controlled declarative configuration, automated deployment, drift detection — can be applied outside Kubernetes using other tooling.

Are You Already Doing GitOps?

Full GitOps is an aspiration for many organizations, but most modern teams are already practicing parts of it. Ask yourself:

• Do you store infrastructure definitions or configurations in Git?
• Do you use an automated CI/CD pipeline triggered by commits or pull requests?
• Do you require pull request reviews before merging changes that affect production?

If you answered yes to any of these, you’re already on the GitOps spectrum. The journey to fuller GitOps adoption is largely about closing the gaps: ensuring all configuration is in Git, automating more of the deployment loop, and adding drift detection.

Key Takeaways

• GitOps makes Git the single source of truth for code, configuration, and infrastructure.
• The system automatically converges to match what is declared in the repository.
• Every change goes through a pull request, providing auditability and rollback capability.
• Pre-merge validation is critical — automated linting and policy checks guard against bad merges reaching production.
• Secrets must be managed separately — never stored as plaintext in the repository.
• Drift detection tooling is required to handle unexpected live-system changes.
• Full GitOps adoption is incremental — start by codifying more of your configuration and infrastructure, and automate progressively.