Vikram Koka stumbled upon Apache Airflow in late 2019. He was working within the Internet of Things trade and looking for an answer to orchestrate sensor information utilizing software program. Airflow gave the impression to be an ideal match, however Koka observed the open-source undertaking’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 information 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 undertaking 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 may truly 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 information and software-engineering area, he linked with individuals in the neighborhood to repair bugs round reliability and craft different enhancements. It took a 12 months, however Airflow 2.0 was launched in December 2020.
Airflow’s Progress and Group Growth
The discharge served as a vital turning level for the undertaking. Downloads from its GitHub repository elevated, and extra enterprises adopted the software program. Inspired by this development, the group envisioned the following technology of Airflow: a modular structure, a extra trendy user interface, and a “run anyplace, anytime” function, enabling it to function on premises, within the cloud, or on edge units and deal with event-driven and advert hoc eventualities along with scheduled duties. The group delivered on this imaginative and prescient with the launch of Airflow 3.0 final April.
“It was superb that we managed to ‘rebuild the aircraft whereas flying it’ after 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 lots 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 just a few 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 undertaking’s neighborhood additionally soared, with greater than 3,000 builders of all ability ranges from all over the world contributing to Airflow.
Jens Scheffler is an energetic a part of that neighborhood. As a technical architect of digital testing automation at Bosch, his group was one of many early adopters of Airflow, utilizing the software program to orchestrate exams for the corporate’s automated driving programs.
Scheffler was impressed by the openness and responsiveness of Airflow members to his requests for steering and help, so he thought-about “giving again one thing to the neighborhood—a contribution of code.” He submitted just a few patches at first, then applied an concept for a function that will profit not solely his group however different Airflow customers as effectively. Scheffler additionally found different departments inside Bosch using Airflow, so that they’ve fashioned a small in-house neighborhood “so we will alternate information and keep up a correspondence.”
Koka, who can 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 bettering the software program. The Airflow group has established a system that allows builders to contribute progressively, beginning with documentation after which progressing to small points and bug fixes earlier than tackling bigger options. The group additionally makes it a degree to swiftly reply and supply constructive suggestions.
“For many people in the neighborhood, [Airflow] is an adopted youngster. None of us had been the unique creators, however we wish extra individuals feeling they’ve additionally adopted it,” says Koka. “We’re in numerous organizations, in numerous international locations, communicate completely different languages, however we’re nonetheless capable of come collectively towards a sure mission. I like with the ability to do this.”
The Airflow group is already planning future options. This contains instruments to put in writing duties in programming languages aside from Python, human-in-the-loop capabilities to overview and approve duties at sure checkpoints, and help for artificial intelligence (AI) and machine learning workflows. In line with 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 trade, and there’s a nice have to make all these workloads—from coaching to inference and agentic processing—sturdy, dependable, scalable, and customarily have a rock-solid basis they’ll run on,” Potiuk says. “I see Airflow as such a basis.”
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