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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
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 with the amount of professionalism, communication, and speed of delivery.
They have been great at adjusting and improving as we have worked together.
I was impressed at how quickly they were able to handle new tasks at a high quality and value.
They are very knowledgeable in their area of expertise.
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.
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.
Nguyen is a champ. He's fast and has great communication. Well done!
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.
You guys are really a bunch of talented geniuses and it's a pleasure and a privilege to work with you.
Apache Airflow is an open-source platform designed to automate, schedule, and monitor complex workflows or data pipelines. It allows users to create, manage, and execute workflows using a Python-based programming language. Airflow provides a graphical interface that enables users to define and visualize their workflows as Directed Acyclic Graphs (DAGs), which represent the relationships and dependencies between tasks.
With Apache Airflow, users can schedule and run tasks at specific time intervals, monitor and track the progress of workflows, handle task dependencies and retries, and manage the execution of operations such as data extraction, transformation, and loading (ETL) processes. It also supports various integrations with external systems and tools, allowing seamless interaction with databases, cloud providers, and other data processing platforms.
Apache Airflow has gained popularity due to its flexibility, scalability, and rich set of features. It is widely used in data engineering, data science, and DevOps workflows for orchestrating and automating complex data pipelines and 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
