Apache Airflow
Platform created by the community to programmatically author, schedule and monitor workflows.
Apache Airflow® has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow™ is ready to scale to infinity. Apache Airflow® pipelines are defined in Python, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically. Easily define your own operators and extend libraries to fit the level of abstraction that suits your environment. Apache Airflow® pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine. No more command-line or XML black-magic! Use standard Python features to create your workflows, including date time formats for scheduling and loops to dynamically generate tasks. This allows you to maintain full flexibility when building your workflows. Monitor, schedule and manage your workflows via a robust and modern web application. No need to learn old, cron-like interfaces. You always have full insight into the status and logs of completed and ongoing tasks. Apache Airflow® provides many plug-and-play operators that are ready to execute your tasks on Google Cloud Platform, Amazon Web Services, Microsoft Azure and many other third-party services. This makes Airflow easy to apply to current infrastructure and extend to next-gen technologies. Anyone with Python knowledge can deploy a workflow. Apache Airflow® does not limit the scope of your pipelines; you can use it to build ML models, transfer data, manage your infrastructure, and more. Wherever you want to share your improvement you can do this by opening a PR. It’s simple as that, no barriers, no prolonged procedures. Airflow has many active users who willingly share their experiences. Have any questions? Check out our buzzing slack. Today we re launching the Apache Airflow Registry — a searchable catalog of every official Airflow provider and its modules, live at … The interactive report is hosted by Astronomer. The Apache Airflow community thanks Astronomer for running this survey, for sponsoring it … We are thrilled to announce the first major release of airflowctl 0.1.0, the new secure, API-driven command-line interface (CLI) for Apache … Apache Airflow Core, which includes webserver, scheduler, CLI and other components that are needed for minimal Airflow installation. Read the documentation Apache Airflow CTL (airflowctl) is a command-line interface (CLI) for Apache Airflow that interacts exclusively with the Airflow REST API. It provides a secure, auditable, and consistent way to manage Airflow deployments — without direct access to the metadata database. Read the documentation The Task SDK provides python-native interfaces for defining DAGs, executing tasks in isolated subprocesses and interacting with Airflow resources (e.g., Connections, Variables, XComs, Metrics, Logs, and OpenLineage events) at runtime. The goal of task-sdk is to decouple DAG authoring from Airflow internals (Scheduler, API Server, etc.), provid
Neon
The database you love, on a serverless platform designed to help you build reliable and scalable applications faster.
Neon AI is recognized for its advanced AI capabilities and seamless integration into various workflows, making it popular among tech-savvy users. However, some users have reported occasional inaccuracies, like confusing elemental configurations, which raises concerns about reliability in certain contexts. Despite limited direct pricing discussions, there is an implied appreciation for its value relative to its functionality. Overall, Neon AI holds a positive reputation, especially for developers and tech enthusiasts seeking innovative AI solutions.
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Apache Airflow
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Apache Airflow
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