Tekton Reviews

It is an open-source tool initially developed by Google for internal use, later open-sourced, and widely adopted for building and deploying applications in Kubernetes environments. When deployed in a Kubernetes cluster, Tekton seamlessly integrates with the environment, streamlining the application pipeline delivery process. It automatically triggers when code is merged into the main branch, operating natively within Kubernetes without requiring additional external components. The automated pipeline, initiated by Tekton, builds the application, deploys it to specified container registries, and then to the Kubernetes cluster. Tekton's versatility shines in multi-environment setups like staging, testing, and production, efficiently managing the continuous integration (CI) part by triggering processes linked to code merges. Users often integrate Argo CD to complement Tekton in the continuous deployment (CD) phase. Argo CD pulls and deploys the latest application image within the Kubernetes cluster, creating a comprehensive and automated CI/CD workflow.

Its user-friendly features include a clean and sleek dashboard. This dashboard provides detailed logs and stages of the pipeline, offering transparency into each step of the process. In case of any failures at a specific stage, the dashboard provides comprehensive information about the issue, enabling quick identification and resolution. Another valuable aspect is its lightweight nature. There's no need for additional deployments or reliance on external vendors for CI/CD, resulting in significant cost savings. Its seamless integration with Kubernetes, being built on top of it and utilizing Custom Resource Definitions, ensures a smooth experience within Kubernetes environments exclusively. Its capability to build entire applications within containers contributes to a seamless and efficient workflow. It stands out for its customizability and scalability, allowing users to undertake diverse tasks.

It tends to occupy a significant amount of disk space on the node, which could potentially pose challenges. This aspect could be enhanced for better efficiency. Additionally, the build time, particularly for larger applications, seems a bit extended, ranging from five to ten minutes in some cases. There's room for improvement to streamline and minimize the build time.

I have been using it for the last six months.

The product appears to be stable, I haven't encountered any noticeable bugs or errors. It has proven reliable in automatically executing tasks once triggered, making it a dependable tool.

The developers' team, consisting of over thirty or forty individuals, actively uses it.

The initial setup was quite straightforward since it comprises various components that are defined separately and then combined to create a pipeline.

Deploying it was easy and seamless, but creating a pipeline involved a bit more effort, requiring attention to various details and taking some time. Despite the complexity of creating pipelines, the overall deployment and readiness of the tool for use were smooth and uncomplicated. Understanding the architecture of this technology is crucial. For newcomers, there might be a learning curve initially, making it a bit more challenging. I am the one responsible for configuring, deploying, and creating pipelines for the development team. I have established a read-only dashboard that provides a clear and concise overview of the pipeline statuses when triggered.

It is entirely open source and free of charge.

While other options like Jenkins were available, Tekton's native compatibility and endorsement by a skilled team of Google developers influenced my decision. It is a stable tool from Google, providing authentication validation for applications. Additionally, I appreciated the seamless integration with Kubernetes, making it a native and well-developed tool for CI/CD processes.

I highly recommend that you operate in a Kubernetes-based environment closely integrated with Kubernetes. However, I suggest deploying it in a separate cluster, not where your primary workloads run, to avoid potential disruptions to production cluster resources. I also recommend it for users who are already familiar with the tool. While it offers significant capabilities, harnessing its full potential requires a certain level of understanding. Overall, I would rate it seven out of ten.

Tekton is the orchestration engine within OpenShift, which is our on-premise platform. Since we are not on the cloud yet, OpenShift plays a strategic role, and Tekton is a significant part of it. It serves as an orchestrator.

In my experience of the last two years using Tekton and OpenShift pipelines, I haven't encountered many issues. As an orchestrator, Tekton works best. It's just one component of the larger OpenShift platform. Tekton consists of multiple components like events, trigger bindings, and more. However, when it comes to the overall OpenShift platform, being a platform as a service, most aspects are taken care of. 

Tekton plays a primary role as an orchestrator. When we receive a webhook from any Git repository, such as Azure Git or GitLab, Tekton triggers the pipeline and performs tasks like code retrieval, running SonarQube or Fortify tasks, and creating and deploying images to multiple environments.

So we can have multiple promoted environments, starting from dev to SIT, then to UAT, and finally to production. We follow a continuous flow branching approach, allowing us to promote changes from smaller environments to larger ones like dev to SIT, SIT to UAT, and UAT to production, which is our master branch. This helps us maintain a smooth workflow and ensures reliable deployment.

Tekton is an orchestrator. It provides seamless integration for our pipelines. It offers robust support for executing tasks within the pipeline, allowing us to set up and run pipelines quickly.

Additionally, Tekton's underlying architecture with OpenShift enables us to create, implement, and run end-to-end pipelines. We can integrate various automation tools like Fortify or SonarQube for testing, code scanning, regression testing, and more. All these tasks can be executed within the pipeline using Tekton.

There might be occasional issues with storage or cluster-level logging, which can affect production. But as a component, Tekton performs flawlessly.

As an orchestrator, Tekton effectively executes most tasks. However, there are instances where we feel that YAML files, which Tekton reads, could benefit from increased flexibility. You see, in OpenShift, everything revolves around YAML. We have different components specified in YAML files, and when we put them together in an OpenShift pipeline, it generally works fine. However, occasionally we encounter difficulties when editing these YAML files.

I have been working with Tekton since we implemented it in 2020, so it's been almost two years.

We haven't encountered any stability issues with it. It has been reliable and available.

Tekton is scalable.

Red Hat's support has been quite excellent. We have a close partnership with Red Hat, as our DevOps strategy heavily relies on OpenShift as a core component. 

Since our entire architecture is on-premise, we have made significant investments in OpenShift. Setting up the OpenShift cluster and configuring different components, including Tekton, has been smooth and hassle-free for us, thanks to Red Hat's support.

It's not solely about Tekton itself. We chose OpenShift as a platform as a service because we opted for on-premise implementation instead of the cloud. The implementation of OpenShift includes the incorporation of Tekton.

The initial setup is actually easy. Tekton is just one of the underlying components in OpenShift pipelines. It's a technology and engine with a straightforward architecture, so the setup process is quite simple.

We have a command-line setup where we use the OpenShift client to connect to Tekton. It's like talking to the cluster, and Tekton executes the tasks on that specific cluster. It's an efficient and streamlined process.

The entire OpenShift platform is supported by just two DevOps engineers.

But we might need to expand the team in the future. Two resources are not sufficient considering the workload and stress we handle.

The pricing is based on OpenShift's vCPU licenses. We pay according to the number of virtual CPUs, which can be costly. 

However, it's important to note that Tekton is just one of the underlying components in OpenShift. Therefore, the pricing and licensing considerations are more related to OpenShift as a whole rather than Tekton alone.

We have evaluated multiple vendors, including Red Hat, whose DevOps architecture includes Tekton as an underlying component. However, other vendors also offer similar orchestration components in their architectures. 

So, there are various tools available from different vendors that serve the same purpose as Tekton.

There are several vendors in the market who provide their own versions of orchestration components for DevOps architectures, apart from Red Hat. They implement their own approaches and name their components accordingly, but the purpose is similar to Tekton.

I would recommend Tekton as an orchestrator because it works well within the OpenShift environment. While there may be similar orchestrator components offered by other vendors in different DevOps architectures, Tekton's integration with other OpenShift components makes it a strong choice.

I would rate Tekton a seven. The only drawback I've experienced is the difficulty of modifying YAML files on the fly and making changes, as it doesn't work well in that aspect. However, apart from that, Tekton performs well in other areas.

I am using Tekton for continuous integration in our project.

The feature that I like most about Tekton is that it is built for Kubernetes, so we can utilize our resources effectively.

Tekton should have more accessibility for some rare use cases so that we can have more references when applying some techniques to our pipeline.

I have been using Tekton since February.

If we set it up correctly and know exactly what we have done, it is very stable because it runs on top of Kubernetes.

I can't give an opinion right now because I haven't applied the scale to our deployment. It's only in our staging environment.

I haven't contacted support yet since most of my errors are addressed in their GitHub repository.

Positive

I had some experience using Tekton when my company migrated the CI panel from another platform.

The initial setup is rather easy because the tutorial is easy to follow.

Tekton is free to use. It's open source.

I would recommend Tekton to my coworkers if they use Kubernetes most at work. However, if my colleagues don't use Kubernetes, I wouldn't recommend Tekton to them because it is very difficult to understand Tekton if you aren't familiar with Kubernetes.

I'd rate the solution eight out of ten.

The CI solution of the team I worked for was based on Tekton. Since Tekton is a Kubernetes native framework, we can easily configure it within a Kubernetes cluster, which has many benefits.

I would say the customization ability that Tekton provides is good. As an example, if we look at the pipeline structure of Tekton, a Tekton Pipeline was built with Tekton Tasks. We can specify a Tekton Task within the pipeline YAML. Otherwise, we can specify the task in a separate YAML and add it as a reference within the pipeline so that we can use the Tekton Task as a reusable component in other pipelines as well. Another thing is that we can execute a Tekton Task based on conditions such as "when" expressions. Furthermore, Tekton provides Tekton Triggers. We use Tekton Triggers to dynamically execute a pipeline based on an event, which is also a very useful feature for us. These customization features of Tekton made it more valuable and flexible to use for us.

The product's documentation is an area of concern, making it an area where improvements are required.

Apart from the documentation, there is one more thing that I believe needs to improve in Tekton. In the Tekton pipeline structure, there is an optional feature called finally section, where we can add all the core Tekton Tasks, which have to be executed even in a pipeline failure scenario. But the issue is that we cannot give a sequence for the tasks in the finally section, so all the tasks are running simultaneously. I believe if we had a feature to help users add a sequence for the tasks in the finally section, then it would make our lives easier.

The scalability could improve a lot in the future.

I have been using Tekton for a year. I am a user of the tool.

Sometimes, we face some version compatibility issues, but when we upgrade the version, it gets rectified. Stability-wise, I rate the solution an eight out of ten.

I know that there are more than 20 people using Tekton within our company. As the CI solution in our company is based on Tekton, many people, including new joiners, also have to learn Tekton. Scalability-wise, I rate the solution a seven out of ten.

When our company faced challenges with the tool, we had to do our own trials and errors to find a solution. Sometimes, we had to go through some GitHub issues related to the challenges we faced, but most of the time, we couldn't find an exact solution by looking at the GitHub issues. So we had to try to resolve the issues we faced in Tekton with the knowledge we had. We did not contact Tekton's support team.

Tekton's integration capabilities have helped our company's development workflow. Tekton is a Kubernetes native framework, so most of the command lines that we use in Kubernetes could also be used within Tekton framework. Basically, configuring Tekton Pipelines within our Kubernetes cluster was very easy compared to using other solutions. Overall, the development workflow was easy when using Tekton.

As a beginner who is trying to use Tekton, it will take some effort to learn it because the documentation in Tekton has to improve a lot. There are no tutorials to learn about Tekton because I personally learned it from my seniors and my experience and even when we faced challenges in the development, we couldn't find a solution on the internet. We had to do our own trial and error to find a solution, which was one of the main challenges while using Tekton.

I recommend the tool to others as using Tekton makes our lives easier. As far as I know, many companies don't know about Tekton. If they knew that there is something called Tekton, they would definitely adjust to it. 

I rate the tool a nine out of ten.

Our primary use case is for cloud integration and cloud deployment. Tekton is a cloud-native tool that we utilize in Kubernetes and OpenShift. It helps us with code integrations and code deployment. We use it for automating various processes and integrating it with our DevOps environment.

Tekton has improved our performance by allowing us to define metrics and resource limits, enhancing the performance of our whole pipeline. The ability to customize tasks and use custom containers has also allowed us to tailor our workflows to our needs.

The customization is valuable. We can make a lot of customizations, and data sharing between tasks using workspaces is very convenient. Moreover, the flexibility and ease of use in creating and attaching new tasks make it very user-friendly. The Tekton UI and user interactions are very good.

Tekton could improve by adding better documentation. The documentation needs improvement to clarify supported features and required integrations. Additionally, while it is currently a cloud-native solution limited to Kubernetes, an option to run it locally on our own systems would be very useful. Security integrations and third-party integrations also need enhancement.

We have been using the Tekton solution for nearly two years.

Tekton is stable and performs well. However, it would benefit from adding new features quickly to stay relevant and helpful.

Tekton is highly scalable due to its cloud-native nature.

We have not escalated any queries to Tekton itself. Since Tekton comes as an integral part of Red Hat, we rely on existing documentation and haven't had any issues requiring direct technical support.

Neutral

In the past, we used Jenkins for CI/CD. Each tool has its own use case and pros and cons, but Tekton's cloud-native capabilities and ease of deployment and maintenance make it a better fit for our needs.

The initial setup of Tekton is very straightforward. We install it as an operator in our cluster, making maintenance tasks like auto-upgrades and updates easier.

There is no specific cost related to resource optimization provided by Tekton. We need to manage the resource limits and requests ourselves to prevent resource exhaustion, especially when running on cloud platforms like AWS, Azure, or GCP.

I recommend Tekton to anyone closely interacting with Kubernetes. It is particularly useful for larger teams with complex application deployment requirements. However, for smaller teams or simpler use cases, it may not be necessary.

I'd rate the solution six out of ten.

I use Tekton for DevOps work, where developed applications will be deployed into a cloud Kubernetes or any Kubernetes platform. This process includes all the Docker builds and deployment into the cluster and the notification after deployment or any triggers.

Tekton has been used for a project where the client wanted their application to be deployed in a Kubernetes cluster, and Tekton is the tool that enables this deployment process.

I like the setup of the dashboard. We can see all the tasks, the pipelines, and all the triggers in one place. That's the main use case that I like. Also, the Tekton Hub was very useful with a huge community. It was very useful for sourcing tasks and pipelines.

Tekton is a good product with all the features I need. There isn't anything that needs improvement.

I have been using Tekton for about one year.

The stability of Tekton depends on correct implementation. If implemented correctly, it's a very good product. I haven't faced any stability issues myself.

Most of the time, the community is really helpful. I didn't need to contact support because the Tekton Hub and its community provided adequate support.

Positive

We used Flux before. However, we do not really use it now. Now, we use ArgoCD to deploy applications across multiple systems.

The initial setup is straightforward. However, users need to follow the documented steps and version requirements.

Some colleagues in my office had prior experience with Tekton, so I had to help with its implementation.

We have documentation for Tekton content creation for the CNCF course that can be forwarded to help others start using Tekton. I'd rate the solution ten out of ten.

We use Tekton to build and deploy business apps, which include analytics programming in their strategy. Our DeepLab setup involves routers that trigger Tekton within our clusters to build and deploy their strategy as needed.

Tekton is also used in Flutter, eliminating the need for GitLab CI for our setup. While GitLab Runners can handle build and deployment tasks, Tekton offers strong integration with EKS and Kubernetes clusters. 

Tekton is useful for us, especially since we use it for all other deployments and in the cloud. In terms of build and deployment capabilities, it can function similarly to GitLab.

The Helm Chart for Tekton doesn't come with all the manifests needed for installation; you must apply them manually. I use ArgoCD to upgrade and manage Tekton, but manual updates to the Helm charts are still required.

I have been using Tekton for one year.

We have analytics that utilize a negative tone. Around 50 people use this in the cloud. When they need to deploy or release a strategy, they tag it and create a new Tekton task. Tekton then handles building and integrating its strategy within our clusters.

It is open source, like DeepLab Community Edition.

Tekton is easy to use, but updating from older versions can be challenging due to method changes with each release. I've spent a lot of time upgrading it. Once you start using Tekton, you can manage it with just a few files and apply them easily. 

The best documentation for Tekton is their CRD for the operator.

Overall, I rate the solution a six out of ten.

In my company, we use Tekton for the pipeline, specifically Bitbucket pipelines. We have some cluster setups as well. In order to run some testing of the cluster, we chose to handle pipelines via Tekton.

The best feature of Tekton is that it is a Kubernetes-native tool for CI/CD. The tool offers flexibility and compatibility. If we look under the Kubernetes-native area, Tekton is built to run natively on Kubernetes, so it leverages Kubernetes 'scalability, security, and robustness, making it an ideal cloud-native application. I can relate to the pipeline customization in Tekton. It provides a highly customizable and reusable pipeline component. You can define tasks and pipelines that suit your specific workflow needs and easily reuse them as a different service platform. If you see the interoperability, Tekton is designed to indicate that there are other tools and services in the Kubernetes ecosystem. It makes it easy to incorporate things into the existing workflow.

Earlier, I used to use Jenkins pipeline, but compared to Jenkins, Tekton is better. Tekton is quite a user-friendly tool. The tool offers good dashboard access, and it is extremely user-friendly. If you are a beginner, then accessing the flexibility part can be overwhelming. We think the learning curve of the tool is steep, especially for those who are not already familiar with Kubernetes. There is a need to improve the recommendations and create more tutorials for the tool. The tool is very user-friendly for advanced users and not for beginners. There are some limitations, especially since the tool primarily operates through YAML configurations and command lines with limited graphical user interface options. While there are some third-party tools that look like Tekton's dashboard, there are still basic and compact major systems like Jenkins or GitLab.With more advanced built-in CGI and GUI, as a user, we can make it easier to monitor and manage Python faster. We can also improve the data handling and debugging. The debugging of failed pipelines in Tekton can be challenging. The error messages are not very descriptive, and the tool has limited support for step-by-step debugging. So, the improvement in error handling and data handling for debugging needs to be improved so that it could significantly improve the developer experience.

I have been using Tekton for three years. In our company, we have been using the tool for more than eight months. I am just a user of the tool.

In our company, there are around more than 5,000 employees. There are actually many things under the cloud and platform delivery and infrastructure. We don't know the exact number of employees who are using the tool. I believe over 100 people in my company use the tool.

In my entire career, I never went to the support team for help. If I had any doubts or any kind of features, I was able to Google it or use ChatGPT.

The solution is deployed on the cloud.

Beginners should go through the product documentation. There are many tutorials available on the product that can help a person to configure the pipeline and task functions. Once you get a proper idea of the architecture of Tekton and how it works, only after knowing how to use the tool will a person get to apply the practical knowledge in the Tekton tools. I suggest that businesses get an idea about Python and how it works, as they need to understand the architecture of Tekton.

I rate the tool an eight out of ten.