Vikram Koka stumbled upon Apache Airflow in late 2019. He was working within the Internet of Things business and trying to find an answer to orchestrate sensor knowledge utilizing software program. Airflow appeared to be an ideal match, however Koka observed the open-source mission’s stagnant state. Thus started a journey to breathe a second life into this dying software program.
Airflow was the brainchild of Airbnb. The corporate created the system to automate and manage its data-related workflows, akin to cleansing and organizing datasets in its knowledge warehouse and calculating metrics round host and visitor engagement. In 2015, Airbnb released the software as open source. Then, 4 years later, Airflow transitioned right into a top-level project on the Apache Software Foundation, a number one developer and steward of open-source software program.
What was as soon as a thriving mission had stalled, nevertheless, with flat downloads and an absence of model updates. Management was divided, with some maintainers specializing in different endeavors.
But Koka believed within the software program’s potential. Not like static configuration information, Airflow follows the precept of “configuration as code.” Workflows are represented as directed acyclic graphs of duties—a graph with directed edges and no loops. Builders can code these duties within the Python programming language, permitting them to import libraries and different dependencies that may assist them higher outline duties. Akin to a musical conductor, Airflow orchestrates the symphony of duties and manages the scheduling, execution, and monitoring of workflows.
This flexibility is what caught Koka’s eye. “I fell in love with the idea of code-first pipelines—pipelines which might really be deployed in code,” he says. “The entire notion of programmatic workflows actually appealed to me.”
Koka began work righting the Airflow ship. As an open-source contributor with a long time of expertise within the knowledge and software-engineering house, he linked with folks in the neighborhood to repair bugs round reliability and craft different enhancements. It took a yr, however Airflow 2.0 was launched in December 2020.
Airflow’s Progress and Group Enlargement
The discharge served as a vital turning level for the mission. Downloads from its GitHub repository elevated, and extra enterprises adopted the software program. Inspired by this progress, the workforce envisioned the following technology of Airflow: a modular structure, a extra trendy user interface, and a “run anyplace, anytime” characteristic, enabling it to function on premises, within the cloud, or on edge units and deal with event-driven and advert hoc situations along with scheduled duties. The workforce delivered on this imaginative and prescient with the launch of Airflow 3.0 final April.
“It was superb that we managed to ‘rebuild the airplane whereas flying it’ once we labored on Airflow 3—even when we had some momentary points and glitches,” says Jarek Potiuk, one of many foremost contributors to Airflow and now a member of its project-management committee. “We needed to refactor and transfer a variety of items of the software program whereas maintaining Airflow 2 operating and offering some bug fixes for it.”
In contrast with Airflow’s second model, which Koka says had only some hundred to a thousand downloads per 30 days on GitHub, “now we’re averaging someplace between 35 to 40 million downloads a month,” he says. The mission’s group additionally soared, with greater than 3,000 builders of all ability ranges from around the globe contributing to Airflow.
Jens Scheffler is an lively a part of that group. As a technical architect of digital testing automation at Bosch, his workforce was one of many early adopters of Airflow, utilizing the software program to orchestrate assessments for the corporate’s automated driving programs.
Scheffler was impressed by the openness and responsiveness of Airflow members to his requests for steerage and assist, so he thought of “giving again one thing to the group—a contribution of code.” He submitted just a few patches at first, then carried out an concept for a characteristic that might profit not solely his workforce however different Airflow customers as nicely. Scheffler additionally found different departments inside Bosch using Airflow, so that they’ve shaped a small in-house group “so we will alternate data and communicate.”
Koka, who can also be a member of Airflow’s project-management committee and a chief technique officer on the data-operations platform Astronomer, notes that managing an enormous group of contributors is difficult, however nurturing that community is as important as enhancing the software program. The Airflow workforce has established a system that allows builders to contribute step by step, beginning with documentation after which progressing to small points and bug fixes earlier than tackling bigger options. The workforce additionally makes it a degree to swiftly reply and supply constructive suggestions.
“For many people in the neighborhood, [Airflow] is an adopted little one. None of us had been the unique creators, however we would like extra folks feeling they’ve additionally adopted it,” says Koka. “We’re in several organizations, in several international locations, communicate completely different languages, however we’re nonetheless in a position to come collectively towards a sure mission. I really like with the ability to do this.”
The Airflow workforce is already planning future options. This consists of instruments to write down duties in programming languages apart from Python, human-in-the-loop capabilities to evaluate and approve duties at sure checkpoints, and assist for artificial intelligence (AI) and machine learning workflows. Based on Airflow’s 2024 survey, the software program has a rising variety of use instances in machine studying operations (MLOps) and generative AI.
“We’re at a pivotal second the place AI and ML workloads are crucial issues within the IT business, and there’s a nice must make all these workloads—from coaching to inference and agentic processing—strong, dependable, scalable, and customarily have a rock-solid basis they will run on,” Potiuk says. “I see Airflow as such a basis.”
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