Apache Airflow vs Dagster — Comparison

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

Dagster

Dagster is the data orchestrator platform that helps you build, schedule, and monitor reliable data pipelines - fast, flexible, and built for teams.

Dagster Labs is the organization behind Dagster, the open-source project, and Dagster Cloud. We’re a small, well-funded, and collegial team with a proven track record of shipping open-source software with global adoption. We are fortunate to be able to partner with some of the best venture capital investors in the business. We are a team that is intrinsically driven and executes with fierce urgency. We think big, aim high and are here to be the best at what we do. We value grit, resilience, and are able to persevere to get to the best outcome. We play to win and we do not mistake motion for progress, striving to quickly focus in on what really matters and avoid work about work We hold ourselves to high standards and trust each other to do the same. We do not believe that quality and velocity are at odds with each other, and taking our craft seriously means we can move fast with excellence. We we do what we say we’re going to do. We work from first principles and solve fundamental problems. We provide continuous, direct, and thoughtful feedback to one another in order to improve. When failures happen, we learn from them as an opportunity to improve our future outcomes. Our workplace should reflect the full diversity of interests, backgrounds, and ideas of all of our employees. We invest in creating experiences to foster meaningful connections and encourage everyone to connect genuinely with colleagues. Building is hard and we believe it will be more sustainable, and we will have more fun when we engage authentically and inject some levity into our daily interactions. We optimize for the group, the company, and not just for the individual. We have a mutual responsibility to support one another to succeed and multiply our impact beyond the sum of our individual parts. We sometimes put aside the work that’s most important within our focus area to help with higher-priority work in other areas. We empower people to have sufficient context across the company to be able to work cross-functionally. We sometimes operate outside of our defined responsibility and never say that something is “not our job”. We act as owners, roll our sleeves up to pitch in, and fix problems and gaps that we see. We started off as an OSS project - our community has been with us the entire journey and they are the reason Dagster Labs exists. The developer experience at Dagster Labs is everyone’s responsibility. We are dedicated to doing everything we can to improve their experience working with data platforms. This means that everyone is invested in our community, their success and their sentiment towards our products. Nick is the founder of Dagster Labs. Prior to that, he was a Principal Engineer and Director at Facebook between 2009-17, where he founded the Product Infrastructure team and co-created GraphQL. Pete previously led teams at Twitter, co-founded Smyte, and was a member of the early React team at Facebook. Yuhan was a senior software engineer and tech lead o

Apache Airflow

Dagster

Apache Airflow

Dagster

Apache Airflow

Dagster