Establish A Single Source Of Truth For Your Data Consumers With A Semantic Layer

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.

4M ago 56:23

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Summary

Maintaining a single source of truth for your data is the biggest challenge in data engineering. Different roles and tasks in the business need their own ways to access and analyze the data in the organization. In order to enable this use case, while maintaining a single point of access, the semantic layer has evolved as a technological solution to the problem. In this episode Artyom Keydunov, creator of Cube, discusses the evolution and applications of the semantic layer as a component of your data platform, and how Cube provides speed and cost optimization for your data consumers.

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 about the role of the semantic layer in your data platform

Interview

Introduction
How did you get involved in the area of data management?
Can you start by outlining the technical elements of what it means to have a "semantic layer"?
In the past couple of years there was a rapid hype cycle around the "metrics layer" and "headless BI", which has largely faded. Can you give your assessment of the current state of the industry around the adoption/implementation of these concepts?
What are the benefits of having a discrete service that offers the business metrics/semantic mappings as opposed to implementing those concepts as part of a more general system? (e.g. dbt, BI, warehouse marts, etc.)
- At what point does it become necessary/beneficial for a team to adopt such a service?
- What are the challenges involved in retrofitting a semantic layer into a production data system?
evolution of requirements/usage patterns
technical complexities/performance and cost optimization
What are the most interesting, innovative, or unexpected ways that you have seen Cube used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cube?
When is Cube/a semantic layer the wrong choice?
What do you have planned for the future of Cube?

Contact Info

LinkedIn
keydunov 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 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

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