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Free Project Planning: We dive into your goals and current state to prepare before a kickoff.
Pay-as-you-go: Use our capacity when you need it, none of that retainer nonsense.
Experts On-Demand: Get new experts from our team when you need specific knowledge or consultation.
We Don't Sleep: Just kidding we do sleep, but we can flexibly hop on calls when you need.
Ad-hoc Calls: When a video call works better than a chat, we hop on a call together.
How it works?
Testimonials
Working with MeteorOps was exactly the solution we looked for. We met a professional, involved, problem solving DevOps team, that gave us an impact in a short term period.
From my experience, working with MeteorOps brings high value to any company at almost any stage. They are uncompromising professionals, who achieve their goal no matter what.
Thanks to MeteorOps, infrastructure changes have been completed without any errors. They provide excellent ideas, manage tasks efficiently, and deliver on time. They communicate through virtual meetings, email, and a messaging app. Overall, their experience in Kubernetes and AWS is impressive.
I was impressed at how quickly they were able to handle new tasks at a high quality and value.
They have been great at adjusting and improving as we have worked together.
They are very knowledgeable in their area of expertise.
You guys are really a bunch of talented geniuses and it's a pleasure and a privilege to work with you.
Good consultants execute on task and deliver as planned. Better consultants overdeliver on their tasks. Great consultants become full technology partners and provide expertise beyond their scope.
I am happy to call MeteorOps my technology partners as they overdelivered, provide high-level expertise and I recommend their services as a very happy customer.
Nguyen is a champ. He's fast and has great communication. Well done!
We got to meet Michael from MeteorOps through one of our employees. We needed DevOps help and guidance and Michael and the team provided all of it from the very beginning. They did everything from dev support to infrastructure design and configuration to helping during Production incidents like any one of our own employees. They actually became an integral part of our organization which says a lot about their personal attitude and dedication.
I was impressed with the amount of professionalism, communication, and speed of delivery.
Argo Workflows is an open-source container-native workflow engine for orchestrating parallel jobs on Kubernetes.
It is part of Argo Project, which is a collection of Kubernetes-native tools for running and managing jobs and applications on Kubernetes.
Argo Workflows is designed to run large-scale and complex workflows and data pipelines in Kubernetes. It allows you to stitch together jobs using a directed acyclic graph (DAG) or steps. Argo also supports more complex workflows such as loops, recursion, conditionals (e.g., if/else), dynamic workflows, and more.
Here are some key features of Argo Workflows:
- DAG or Step based declaration: You can define tasks and their dependencies using a directed acyclic graph (DAG) or step-based workflows.
- Artifacts: Argo Workflows allows passing of artifacts from one step to the next.
- Parallelism & Concurrency: You can run tasks in parallel to speed up processing. You can also limit the level of concurrency for your tasks.
- Scheduling: Argo Workflows supports scheduling and also includes an optional controller for workflow cron jobs.
- Scripting: Argo allows you to include scripts directly in your YAML definition files, providing flexibility in your workflows.
- Loops, Conditionals, Recursion, and Retries: Argo Workflows has built-in support for loops, conditional execution, recursion, and retrying failed steps, providing robust error handling.
- UI and CLI: Argo provides a web-based user interface and a command-line interface for managing and visualizing workflows.
- Event-driven: You can create workflows that are triggered by events, like on successful completion of another workflow or on a schedule.
Argo Workflows is designed for large-scale, compute-intensive jobs and is particularly suited for data processing tasks, batch jobs, ETL jobs, and machine learning workflows.
Orchestration systems decide where and when workloads run on a cluster of machines (physical or virtual). On top of that, orchestration systems usually help manage the lifecycle of the workloads running on them. Nowadays, these systems are usually used to orchestrate containers, with the most popular one being Kubernetes.
There are many advantages to using Orchestration tools:
- Improve the utilization of CPU, memory, and storage usage by running many processes on a single machine
- Manage the entire lifecycle of the orchestrated workloads: pre & post initialization & termination
- Control the scale of workloads and the scale of their underlying infrastructure separately
- Centralized management of workloads and infrastructure
