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
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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 optimiz