Combining The Simplicity Of Spreadsheets With The Power Of Modern Data Infrastructure At Canvas

Summary Data analysis is a valuable exercise that is often out of reach of non-technical users as a result of the complexity of data systems. In order to lower the barrier to entry Ryan Buick created the Canvas application with a spreadsheet oriented workflow that is understandable to a wide audience. In this episode Ryan explains how he and his team have designed their platform to bring everyone onto a level playing field and the benefits that it provides to the organization. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. 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Your host is Tobias Macey and today I’m interviewing Ryan Buick about Canvas, a spreadsheet interface for your data that lets everyone on your team explore data without having to learn SQL Interview Introduction How did you get involved in the area of data management? Can you describe what Canvas is and the story behind it? The "modern data stack" has enabled organizations to analyze unparalleled volumes of data. What are the shortcomings in the operating model that keeps business users dependent on engineers to answer their questions? Why is the spreadsheet such a popular and persistent metaphor for working with data? What are the biggest issues that existing spreadsheet software run up against as they scale both technically and organizationally? What are the new metaphors/design elements that you needed to develop to extend the existing capabilities and use cases of spreadsheets while keeping them familiar? Can you describe how the Canvas platform is implemented? How have the design and goals of the product changed/evolved since you started working on it? What is the workflow for a business user that is using Canvas to iterate on a series of questions? What are the collaborative features that you have built into Canvas and who are they for? (e.g. other business users, data engineers <-> business users, etc.) What are the situations where the spreadsheet abstraction starts to break down? What are the extension points/escape hatches that you have built into the product for when that happens? What are the most interesting, innovative, or unexpected ways that you have seen Canvas used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Canvas? When is Canvas the wrong choice? What do you have planned for the future of Canvas? Contact Info LinkedIn @ryanjbuick 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__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site 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 [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Canvas Flexport Podcast Episode about their data mesh implementation Excel Lightdash Podcast Episode dbt Podcast Episode Figma The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast