Speaker
Description
Every open source project relies on a few people who know how things really work. When too much knowledge is concentrated in just one or two people, it can become a real risk for the project.
In this talk, I’ll look at how we can use data engineering to better understand that risk. I’ll use public GitLab data from GNOME modules, looking at things like who is contributing code, reviewing changes, and helping with merges over time.
I’ll walk through a simple, fully open-source pipeline that collects and processes this data and turns it into a dashboard. The idea is to get a clearer picture of which modules have a healthy spread of contributors and which ones may depend heavily on a small number of people.
This isn’t about ranking or calling out individual contributors. Instead, the goal is to give maintainers and the GNOME Foundation a practical way to spot areas where additional maintainers, mentoring, or knowledge sharing might be useful.
By the end of the talk, attendees will have a reference architecture they can adapt for their own open source projects, along with some practical lessons on working with community data and focusing on useful metrics rather than numbers that simply look good.
Talk Description
Outline:
-
What does “bus factor” mean, and why does it matter?
A quick introduction to the bus factor and why having knowledge concentrated among a few people can be a problem for an open source project. (5 min) -
What data can we use?
Looking at the information available in GNOME’s public GitLab, including commits, merge requests, and reviews. (5 min) -
Building the pipeline
How we collect and organize the data and calculate a bus factor score for each module. (10 min) -
Dashboard walkthrough
A look at the dashboard and how it shows contributor activity across different modules. (10 min) -
Using the results responsibly
How to focus on the health of the project rather than individual contributors, and how the results can help identify areas where more knowledge sharing or backup maintainers may be needed. (5 min) -
Q&A
Audience takeaway:
Attendees will get a practical, open source approach that they can use to look at maintainer concentration in their own FOSS projects. The talk will also show how data engineering practices such as schema design, incremental data collection, and choosing useful metrics can be applied to open source community data.
Technical level:
Intermediate. Some basic knowledge of Git and GitLab will be useful, but the talk is also suitable for people interested in open source project health and governance without a strong technical background.
Author(s) Bio
Prasis is a data engineer with 3.5+ years of experience building and optimizing data pipelines for healthcare platforms at Converging Health. He works with large-scale claims, eligibility, member, and pharmacy data across multiple clients.
His work includes AWS services such as S3, Lambda, and SQS, along with Snowflake and ClickHouse. He has experience with data ingestion, ETL pipelines, data modeling, performance tuning, partitioning, event-driven architectures, and data quality.
This is his first time speaking at GNOME Asia. The idea for this talk came from a simple realization: many of the questions he asks when working with data pipelines, such as where the bottleneck is, where the single point of failure is, and what happens if one part breaks, also apply to the people who keep an open source project running.
Prasis can be reached anangdembeprasis@gmail.com.
| Category | Project organization and Governance |
|---|---|
| Pronouns | His |
| Where are you located? | Kathmandu, Napel |
| Do you need travel sponsorship from GNOME Foundation in order to join our event? | Yes |