Data Engineering Podcast

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

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Apr 7, 2024

Guest

Artyom Keydunov

Artyom Keydunov, creator of Cube, discusses the evolution and applications of the semantic layer in data platforms. Topics include challenges in defining metrics, implementing a semantic layer, transitioning from DBT to Cube, and the evolution of CubeJS to Rust. The episode also explores AI-driven data discovery tools for business consumers.

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ADVICE

Model Normalized In Warehouse Then Denormalize In Semantic Layer

Keep normalized dimensional models in the warehouse and use the semantic layer as the denormalization point for measures and multidimensional marts.
Build staging models in dbt, import them to Cube as cubes, then define measures and use cases for denormalized marts.

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ADVICE

Migrate Code-First BI First For Fast Wins

Migration effort varies with tooling: moving Looker to Cube can be fast because both are code-first, while migrating many legacy workbooks or spreadsheets is slower.
Use code-based imports and blueprints to accelerate medium-size migrations and prioritize high-impact models first.

insights

INSIGHT

Pre-Aggregations Cut Cost And Speed Up Queries

Semantic-layer caching (pre-aggregations) boosts query speed and reduces warehouse costs by serving queries from optimized storage instead of raw tables.
Cube builds pre-aggregation via group-by in the warehouse, exports to object storage, ingests/optimizes it in Cube store, then serves aggregate-aware queries.

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Exploring the Role of the Semantic Layer in Data Platforms and Transition from Software Engineering to Data Projects

01:32 • 2min

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Evolution of Semantic Layers in Data Platforms and BI Tools

03:06 • 22min

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Technical Challenges in Implementing a Semantic Layer

25:30 • 4min

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Integrating Cube as a Materialization Transformation Layer for DBT Users

29:21 • 3min

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Evolution of CubeJS to Rust and Data Modeling in YAML and Python

31:55 • 21min

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Evolution of Catalog Tools and the Role of AI in Data Discovery for Business Consumers

52:34 • 4min

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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.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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

Model Normalized In Warehouse Then Denormalize In Semantic Layer

Migrate Code-First BI First For Fast Wins

Pre-Aggregations Cut Cost And Speed Up Queries

Summary

Announcements

Interview

Contact Info

Parting Question

Closing Announcements

Links