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
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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.
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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