Package Management And Distribution For Your Data Using Quilt with Kevin Moore - Episode 37

Episode • Jun 25, 2018 • 41m

Summary

Collaboration, distribution, and installation of software projects is largely a solved problem, but the same cannot be said of data. Every data team has a bespoke means of sharing data sets, versioning them, tracking related metadata and changes, and publishing them for use in the software systems that rely on them. The CEO and founder of Quilt Data, Kevin Moore, was sufficiently frustrated by this problem to create a platform that attempts to be the means by which data can be as collaborative and easy to work with as GitHub and your favorite programming language. In this episode he explains how the project came to be, how it works, and the many ways that you can start using it today.

Preamble

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Your host is Tobias Macey and today I’m interviewing Kevin Moore about Quilt Data, a platform and tooling for packaging, distributing, and versioning data

Interview

Introduction
How did you get involved in the area of data management?
What is the intended use case for Quilt and how did the project get started?
Can you step through a typical workflow of someone using Quilt?
- How does that change as you go from a single user to a team of data engineers and data scientists?

Can you describe the elements of what a data package consists of?
- What was your criteria for the file formats that you chose?

How is Quilt architected and what have been the most significant changes or evolutions since you first started?

How is the data registry implemented?
- What are the limitations or edge cases that you have run into?
- What optimizations have you made to accelerate synchronization of the data to and from the repository?

What are the limitations in terms of data volume, format, or usage?

What is your goal with the business that you have built around the project?

What are your plans for the future of Quilt?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

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