Your data workflows are the heartbeat of your business. If your ETL jobs, data pipelines, and reporting dependencies are slow, unreliable, or impossible to manage, your entire data strategy suffers. Airflow gives you the power to define, schedule, and monitor complex directed acyclic graphs (DAGs). Kamatera gives you the high-performance infrastructure to run it flawlessly.
We provide the dedicated cloud environment engineered for the demanding, burstable workloads of Airflow schedulers, workers, and metadata stores. Get instant deployment, blazing-fast SSDs, and the flexibility to customize your machine exactly how Airflow needs it.
Why choose Kamatera to host your Apache Airflow?
You choose the exact OS and resource allocation for your Airflow deployment, whether it’s a standard executor or a complex Kubernetes executor setup.- Running data pipelines means running demanding tasks. Our global network of Tier-1 data centers and powerful, current-generation hardware eliminate the lag.
- Need to add a dozen workers for a massive end-of-quarter load? Scale instantly. Need to reduce resources once peak processing is complete? Scale down just as fast.
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Frequently asked questions
Apache Airflow is an open-source platform used to programmatically author, schedule, and monitor workflows. It allows users to define data pipelines as Directed Acyclic Graphs (DAGs) of tasks, ensuring they run in the correct order, at the right time, and can be easily managed and observed from a single, centralized interface. It’s the standard tool for modern ETL/ELT and data orchestration.
Airflow® is tested with:
Python: 3.10, 3.11, 3.12, 3.13
Databases:
PostgreSQL: 13, 14, 15, 16, 17
MySQL: 8.0, Innovation
SQLite: 3.15.0+
Kubernetes: 1.30, 1.31, 1.32, 1.33
While we recommend a minimum of 4GB of memory for Airflow, the actual requirements heavily depend on your chosen deployment.
For more information, refer to the Apache Airflow prerequisites.
Apache Airflow’s primary function is to serve as a robust platform for defining and executing Directed Acyclic Graphs (DAGs), which represent sequential data workflows. Its most common use case is the construction and management of ETL (Extract, Transform, Load) and ELT processes, ensuring that data is reliably ingested, cleaned, and transformed before being loaded into data warehouses or analytical databases. Airflow automatically handles complex task dependencies, provides visual monitoring of runs, and offers retry logic for failed steps, making it indispensable for maintaining data quality and timeliness in production environments.
Beyond core data movement, Airflow is widely adopted for orchestrating advanced processes across disparate systems. This includes scheduling and automating entire machine learning pipelines, from model training and evaluation to deployment and monitoring. It is also frequently utilized for general infrastructure automation, such as coordinating system backups, generating reports on a schedule, or syncing data across various cloud services, APIs, and on-premises resources, centralizing operational control over all computational tasks.
Kamatera offers a free 30-day trial period. This free trial offers services worth up to $100 on one server. After signing up, you can use the management console to deploy a server and test our infrastructure. You can select a data center, operating system, CPU, RAM, storage, and other system preferences.
Our system accepts credit/debit cards issued by your local bank branch with a cardholder’s name. We also accept payments through PayPal.
Yes. Kamatera includes comprehensive, always-on anti-DDoS protection for all cloud servers to protect your orchestration engine from external attacks.
With Kamatera’s flexible infrastructure, you can use the management console to add more CPU/RAM to a single node or deploy additional servers to act as remote workers using the CeleryExecutor or KubernetesExecutor.
Kamatera offers monthly or hourly billing, meaning you only pay for the resources your Airflow instance consumes. This is ideal for testing new DAGs or running periodic batch jobs.