Build Your Analytics With A Collaborative And Expressive SQL IDE Using Querybook

Data Engineering Podcast

Tobias Macey에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Tobias Macey 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

3y ago 52:35

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Summary

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.

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 Justin Mejorada-Pier and Charlie Gu about Querybook, an open source IDE for your big data projects

Interview

Introduction
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?

Contact Info

Justin
- LinkedIn
- Website
Charlie
- czgu on GitHub

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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

354 에피소드

#Data Science #Tech #Science #Tobias Macey #Big Data