Fast And Flexible Headless Data Analytics With Cube.JS
Data Engineering Podcast
00:00
Building Data Aggregations on Cubs Platform, How Do You Think About It?
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Summary
One of the perennial challenges of data analytics is having a consistent set of definitions, along with a flexible and performant API endpoint for querying them. In this episode Artom Keydunov and Pavel Tiunov share their work on Cube.js and the various ways that it is being used in the open source community.
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 Artyom Keydunov and Pavel Tiunov about Cube.js a framework for building analytics APIs to power your applications and BI dashboards
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Cube is and the story behind it?
- What are the main use cases and platform architectures that you are focused on?
- Who are the target personas that will be using and managing Cube.js?
- The name comes from the concept of an OLAP cube. Can you discuss the applications of OLAP cubes and their role in the current state of the data ecosystem?
- How does the idea of an OLAP cube compare to the recent focus on a dedicated metrics layer?
- What are the pieces of a data platform that might be replaced by Cube.js?
- Can you describe the design and architecture of the Cube platform?
- How has the focus and target use case for the Cube platform evolved since you first started working on it?
- One of the perpetually hard problems in computer science is cache management. How have you approached that challenge in the pre-aggregation layer of the Cube framework?
- What is your overarching design philosophy for the API of the Cube system?
- Can you talk through the workflow of someone building a cube and querying it from a downstream system?
- What do the iteration cycles look like as you go from initial proof of concept to a more sophisticated usage of Cube.js?
- What are some of the data modeling steps that are needed in the source systems?
- The perennial problem of embedding SQL into another host language or DSL is how to deal with validation and developer tooling. What are the utilities that you and the community have built to reduce friction while writing the definitions of a cube?
- What are the methods available for maintaining visibility across all of the cubes defined within and across installations of Cube.js?
- What are the opportunities for composing multiple cubes together to form a higher level aggregation?
- What are the most interesting, innovative, or unexpected ways that you have seen Cube.js used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cube?
- When is Cube the wrong choice?
- What do you have planned for the future of Cube?
Contact Info
- Artom
- Pavel
- @paveltiunov87 on Twitter
- paveltiunov 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.
- 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 hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Links
- Cube.js
- Statsbot
- chart.js
- Highcharts
- D3
- OLAP Cube
- dbt
- Superset
- Streamlit
- Parquet
- Hasura
- kSQLDB
- Materialize
- Meltano
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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