Building The Open Data Ecosystem For Music And More At Metabrainz

Episode • Aug 17, 2020 • 48m

Summary

The Musicbrainz project was an early entry in the movement to build an open data ecosystem. In recent years, the Metabrainz Foundation has fostered a growing ecosystem of projects to support the contribution of, and access to, metadata, listening habits, and review of music. The majority of those projects are written in Python, and in this episode Param Singh explains how they are built, how they fit together, and how they support the goals of the Metabrains Foundation. This was an interesting exporation of the work involved in building an ecosystem of open data, the challenges of making it sustainable, and the benefits of building for the long term rather than trying to achieve a quick win.

Announcements

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Your host as usual is Tobias Macey and today I’m interviewing Param Singh about the ways that Python is being used across the various Metabrainz projects

Interview

Introductions
How did you get introduced to Python?
Can you start by giving an overview of what the Metabrainz organization is and the various projects that it encompasses?
- What are the motivations for creating those projects and some of the origin story for Metabrainz?
The Musicbrainz server is the longest running project and is written in Perl. What was the reason for switching to Python for all of the other *brainz projects?
How does the MetaBrainz Foundation sustain itself? Where do the funds come from?
- How do you determine where and how to allocate the funding that you receive?
Which of the *brainz projects is the most complex or challenging to build, whether due to technical or sociological reasons?
How do you source and manage the information that powers all of the Metabrainz projects?
How is development of the various projects organized?
- How does that influence the amount of code sharing that is possible between them?
Of the projects that you have been involved in, how are they architected?
- What are the main ways that the projects differ in how they are implemented?
What are some of the ways that you are using Python in support of the various projects that you work on?
What are some of the most interesting, innovative, or unexpected ways that you have seen the projects or data built by Metabrainz being used?
What are some of the most interesting, unexpected, or challenging lessons that you have learned while working as a contributor and maintainer of the Metabrainz projects?
What is in store for the future of the existing Metabrainz projects?
What are the next domains that are being considered for building a Metabrainz platform for?

Building The Open Data Ecosystem For Music And More At Metabrainz

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