.avif)
%20(2).avif)
.avif)
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.
They are very knowledgeable in their area of expertise.
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.
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.
You guys are really a bunch of talented geniuses and it's a pleasure and a privilege to work with you.
Nguyen is a champ. He's fast and has great communication. Well done!
I was impressed with the amount of professionalism, communication, and speed of delivery.
We were impressed with their commitment to the project.
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.
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.
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.
⏳
❌
📉
💸
🏗️
💥
🔍
🐢
🤯
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?
Apache Airflow is an open-source workflow orchestration platform for defining, scheduling, and monitoring data pipelines as code, originally created at Airbnb and now maintained by the Apache Software Foundation. It uses Python-based DAGs (Directed Acyclic Graphs) to model dependencies and run complex workflows with clear execution semantics, retries, SLAs, and rich observability through its UI and logs. Airflow supports a wide ecosystem of operators and integrations for common systems (e.g., databases, warehouses, object storage, Spark, Kubernetes, and cloud services), enabling use cases such as ETL/ELT orchestration, ML pipeline scheduling, and cross-system batch automation. Common capabilities include: dependency management and backfills; task-level retries and alerting; extensibility via custom operators/sensors/hooks; and scalable execution with executors such as Celery, Kubernetes, or managed offerings. For more details, see the official Apache Airflow documentation.
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
Apache Airflow is an open-source workflow orchestration platform used to define, schedule, and monitor data pipelines as code. It is commonly chosen when teams need reliable dependency management, observability, and operational control for complex ETL and batch workflows.
- Python-based DAG authoring enables pipelines to be versioned, tested, and reviewed like application code.
- Explicit dependency management models complex multi-step workflows and enforces correct execution order.
- Built-in scheduling supports cron-like intervals, backfills, retries, and SLAs for predictable operations.
- Rich observability includes task-level logs, run history, and a UI for debugging failures and bottlenecks.
- Scales execution via multiple executors (Local, Celery, Kubernetes) to match workload size and isolation needs.
- Extensible operators and hooks integrate with common systems such as databases, warehouses, object storage, and APIs.
- Dynamic workflows allow parameterization and programmatic generation of tasks for large or variable pipelines.
- Strong operational controls support retries, timeouts, alerting callbacks, and idempotent reruns for reliability.
- Role-based access control and audit-friendly metadata help govern who can run, modify, and view pipelines.
- Large ecosystem and community provide reusable patterns, providers, and operational guidance.
Airflow is best suited for batch-oriented orchestration and dependency-heavy pipelines. It is not a streaming engine, and teams should plan for operational overhead such as scheduler tuning, metadata database management, and DAG design discipline. For background on core concepts and architecture, see Apache Airflow documentation.
Common alternatives include Prefect, Dagster, and Argo Workflows, with trade-offs in developer experience, deployment model, and orchestration scope.
Our experience with Apache Airflow helped us build repeatable patterns, guardrails, and operational tooling that make workflow orchestration easier to run in production across data engineering and MLOps teams.
Some of the things we did include:
- Reviewed existing Airflow estates (DAG quality, scheduling strategy, retries/timeouts, SLAs) and delivered prioritized remediation plans focused on reliability and maintainability.
- Designed and deployed Airflow on Kubernetes using the Helm chart, including resource sizing, node affinity, and safe upgrade procedures.
- Migrated teams from CeleryExecutor to KubernetesExecutor (and Kubernetes-native patterns), reducing operational overhead and improving workload isolation.
- Implemented CI/CD for DAGs with GitHub Actions, including linting, unit tests, packaging, and promotion across environments with consistent variables and connections.
- Standardized DAG authoring with modular Python utilities, shared operators, and conventions for idempotency, backfills, and dataset-aware scheduling.
- Integrated pipelines with dbt for ELT orchestration, including artifact handling, run result parsing, and failure triage workflows.
- Built observability around Airflow using Prometheus metrics and structured logging, enabling alerting on scheduler health, queue depth, and task-level error rates.
- Hardened security with secret management, RBAC, least-privilege service accounts, and network policies; reduced credential sprawl across environments.
- Improved performance and cost by tuning parallelism/concurrency, controlling backfill behavior, and optimizing worker autoscaling for bursty schedules.
- Implemented HA/DR considerations (metadata database sizing, scheduler redundancy, backup/restore runbooks) and validated recovery procedures during upgrades.
- Delivered enablement sessions for engineers and operators covering DAG design patterns, operational troubleshooting, and safe release practices.
This delivery work helped us accumulate significant knowledge across multiple use-cases—from ETL to ML orchestration—and enables us to deliver high-quality Apache Airflow setups that are easier to operate, safer to change, and resilient under real production load.
Some of the things we can help you do with Apache Airflow include:
- Assess your current Airflow environment and deliver a prioritized findings report covering reliability, maintainability, and scaling risks.
- Create an adoption roadmap for standardized DAG patterns, dependency management, and promotion workflows across dev/test/prod.
- Implement and productionize Airflow (self-managed or managed) with HA architecture, executor selection, and resilient scheduling.
- Automate deployments with Infrastructure as Code, CI/CD, and GitOps-style workflows to reduce release risk and drift.
- Harden security with RBAC, secrets management, network controls, and compliance guardrails aligned to your data policies.
- Optimize cost and performance through right-sized workers, autoscaling strategies, efficient task design, and queue/concurrency tuning.
- Improve observability with metrics, logs, alerting, and SLOs so teams can detect failures quickly and reduce pipeline downtime.
- Refactor and troubleshoot DAGs and operators to eliminate bottlenecks, reduce retries, and improve data freshness.
- Enable teams with hands-on training, best-practice playbooks, and code reviews for maintainable, testable pipeline development.
- Provide ongoing operations support for upgrades, plugin governance, incident response, and reliability improvements over time.
