SQL is the most widely used language for working with data, and yet the tools available for writing and collaborating on it are still clunky and inefficient. Frustrated with the lack of a modern IDE and collaborative workflow for managing the SQL queries and analysis of their big data environments, the team at Pinterest created Querybook. In this episode Justin Mejorada-Pier and Charlie Gu share the story of how the initial prototype for a data catalog ended up as one of their most widely used interfaces to their analytical data. They also discuss the unique combination of features that it offers, how it is implemented, and the path to releasing it as open source. Querybook is an impressive and unique piece of technology that is well worth exploring, so listen and try it out today.
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Your host is Tobias Macey and today I’m interviewing Justin Mejorada-Pier and Charlie Gu about Querybook, an open source IDE for your big data projects
How did you get involved in the area of data management?
Can you describe what Querybook is and the story behind it?
What are the main use cases or workflows that Querybook is designed for?
What are the shortcomings of dashboarding/BI tools that make something like Querybook necessary?
The tag line calls out the fact that Querybook is an IDE for “big data”. What are the manifestations of that focus in the feature set and user experience?
Who are the target users of Querybook and how does that inform the feature priorities and user experience?
Can you describe how Querybook is architected?
How have the goals and design changed or evolved since you first began working on it?
What were some of the assumptions or design choices that you had to unwind in the process of open sourcing it?
What is the workflow for someone building a DataDoc with Querybook?
What is the experience of working as a collaborator on an analysis?
How do you handle lifecycle management of query results?
What are your thoughts on the potential for extending Querybook beyond SQL-oriented analysis and integrating something like Jupyter kernels?
What are the most interesting, innovative, or unexpected ways that you have seen Querybook used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Querybook?
When is Querybook the wrong choice?
What do you have planned for the future of Querybook?
czgu on GitHub
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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