Summary Data is a team sport, but it's often difficult for everyone on the team to participate. For a long time the mantra of data tools has been "by developers, for developers", which automatically excludes a large portion of the business members who play a crucial role in the success of any data project. Quilt Data was created as an answer to make it easier for everyone to contribute to the data being used by an organization and collaborate on its application. In this episode Aneesh Karve shares the journey that Quilt has taken to provide an approachable interface for working with versioned data in S3 that empowers everyone to collaborate. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring (https://materialize.com/careers/) across all functions! Your host is Tobias Macey and today I'm interviewing Aneesh Karve about how Quilt Data helps you bring order to your chaotic data in S3 with transactional versioning and data discovery built in Interview Introduction How did you get involved in the area of data management? Can you describe what Quilt is and the story behind it? How have the goals and features of the Quilt platform changed since I spoke with Kevin in June of 2018? What are the main problems that users are trying to solve when they find Quilt? What are some of the alternative approaches/products that they are coming from? How does Quilt compare with options such as LakeFS, Unstruk, Pachyderm, etc.? Can you describe how Quilt is implemented? What are the types of tools and systems that Quilt gets integrated with? How do you manage the tension between supporting the lowest common denominator, while providing options for more advanced capabilities? What is a typical workflow for a team that is using Quilt to manage their data? What are the most interesting, innovative, or unexpected ways that you have seen Quilt used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Quilt? When is Quilt the wrong choice? What do you have planned for the future of Quilt? Contact Info LinkedIn (https://www.linkedin.com/in/aneeshkarve/) @akarve (https://twitter.com/akarve) on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Quilt Data (https://quiltdata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/quilt-data-with-kevin-moore-episode-37/) UW Madison (https://www.wisc.edu/) Docker Swarm (https://docs.docker.com/engine/swarm/) Kaggle (https://www.kaggle.com/) open.quiltdata.com (https://open.quiltdata.com/) FinOS Perspective (https://perspective.finos.org/) LakeFS (https://lakefs.io/) Podcast Episode (https://www.dataengineeringpodcast.com/lakefs-data-lake-versioning-episode-157/) Pachyderm (https://www.pachyderm.com/) Podcast Episode (https://www.dataengineeringpodcast.com/pachyderm-data-lineage-episode-82) Unstruk (https://www.unstruk.com/) Podcast Episode (https://www.dataengineeringpodcast.com/unstruk-unstructured-data-warehouse-episode-196/) Parquet (https://parquet.apache.org/) Avro (https://avro.apache.org/) ORC (https://orc.apache.org/) Cloudformation (https://aws.amazon.com/cloudformation/) Troposphere (https://github.com/cloudtools/troposphere) CDK == Cloud Development Kit (https://aws.amazon.com/cdk/) Shadow IT (https://en.wikipedia.org/wiki/Shadow_IT) Podcast Episode (https://www.dataengineeringpodcast.com/shadow-it-data-analytics-episode-121) Delta Lake (https://delta.io/) Podcast Episode (https://www.dataengineeringpodcast.com/delta-lake-data-lake-episode-85/) Apache Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Datasette (https://datasette.io/) Frictionless (https://frictionlessdata.io/) DVC (https://dvc.org/) Podcast.__init__ Episode (https://www.pythonpodcast.com/data-version-control-episode-206/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)