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.
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- Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen
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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
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Quilt Data
- Reproducible Data Dependencies in Jupyter
- Reproducible Machine Learning with Jupyter and Quilt
- Allen Institute: Programmatic Data Access with Quilt
- Quilt Example: MissingNo
- Merkle Tree
- Allen Institute for Cell Science
- Quilt Teams
- Hive Metastore
- Netflix Iceberg