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 information utilizing software program. Airflow gave the impression to be an ideal match, however Koka observed the open-source venture’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, comparable 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 venture 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. In contrast to 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 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 many years of expertise within the information and software-engineering house, he related with individuals locally 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 Development and Neighborhood Enlargement
The discharge served as a vital turning level for the venture. Downloads from its GitHub repository elevated, and extra enterprises adopted the software program. Inspired by this progress, the staff envisioned the subsequent 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 gadgets and deal with event-driven and advert hoc eventualities along with scheduled duties. The staff delivered on this imaginative and prescient with the launch of Airflow 3.0 final April.
“It was wonderful that we managed to ‘rebuild the aircraft whereas flying it’ after we labored on Airflow 3—even when we had some non permanent 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 loads of items of the software program whereas retaining 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 thirty days on GitHub, “now we’re averaging someplace between 35 to 40 million downloads a month,” he says. The venture’s group additionally soared, with greater than 3,000 builders of all talent 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 staff was one of many early adopters of Airflow, utilizing the software program to orchestrate exams for the corporate’s automated driving methods.
Scheffler was impressed by the openness and responsiveness of Airflow members to his requests for steering and assist, so he thought of “giving again one thing to the group—a contribution of code.” He submitted a number of patches at first, then applied an thought for a function that will profit not solely his staff however different Airflow customers as nicely. Scheffler additionally found different departments inside Bosch using Airflow, in order that they’ve fashioned a small in-house group “so we are able to 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 bettering the software program. The Airflow staff has established a system that permits builders to contribute progressively, beginning with documentation after which progressing to small points and bug fixes earlier than tackling bigger options. The staff additionally makes it a degree to swiftly reply and supply constructive suggestions.
“For many people locally, [Airflow] is an adopted little one. None of us have been the unique creators, however we would like extra individuals feeling they’ve additionally adopted it,” says Koka. “We’re in several organizations, in several nations, communicate completely different languages, however we’re nonetheless in a position to come collectively towards a sure mission. I like having the ability to try this.”
The Airflow staff is already planning future options. This consists of instruments to jot down duties in programming languages apart from Python, human-in-the-loop capabilities to assessment and approve duties at sure checkpoints, and assist 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 an important issues within the IT business, and there’s a nice have to make all these workloads—from coaching to inference and agentic processing—sturdy, dependable, scalable, and usually have a rock-solid basis they’ll run on,” Potiuk says. “I see Airflow as such a basis.”
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