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

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April 7th, 2024

56 mins 23 secs

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About this Episode

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?

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

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