From Data Discovery to AI: The Evolution of Semantic Layers

Summary
In this episode of the Data Engineering Podcast, host Tobias Macy welcomes back Shinji Kim to discuss the evolving role of semantic layers in the era of AI. As they explore the challenges of managing vast data ecosystems and providing context to data users, they delve into the significance of semantic layers for AI applications. They dive into the nuances of semantic modeling, the impact of AI on data accessibility, and the importance of business logic in semantic models. Shinji shares her insights on how SelectStar is helping teams navigate these complexities, and together they cover the future of semantic modeling as a native construct in data systems. Join them for an in-depth conversation on the evolving landscape of data engineering and its intersection with AI.

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 Shinji Kim about the role of semantic layers in the era of AI

Interview

• Introduction
• How did you get involved in the area of data management?
• Semantic modeling gained a lot of attention ~4-5 years ago in the context of the "modern data stack". What is your motivation for revisiting that topic today?
• There are several overlapping concepts – "semantic layer," "metrics layer," "headless BI." How do you define these terms, and what are the key distinctions and overlaps?
  • Do you see these concepts converging, or do they serve distinct long-term purposes?
• Data warehousing and business intelligence have been around for decades now. What new value does semantic modeling beyond practices like star schemas, OLAP cubes, etc.?
• What benefits does a semantic model provide when integrating your data platform into AI use cases?
  • How is it different between using AI as an interface to your analytical use cases vs. powering customer facing AI applications with your data?
• Putting in the effort to create and maintain a set of semantic models is non-zero. What role can LLMs play in helping to propose and construct those models?
  • For teams who have already invested in building this capability, what additional context and metadata is necessary to provide guidance to LLMs when working with their models?
• What's the most effective way to create a semantic layer without turning it into a massive project? 
• There are several technologies available for building and serving these models. What are the selection criteria that you recommend for teams who are starting down this path?
• What are the most interesting, innovative, or unexpected ways that you have seen semantic models used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working with semantic modeling?
• When is semantic modeling the wrong choice?
• What do you predict for the future of semantic modeling?

Contact Info

• LinkedIn

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 AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links

• SelectStar
• Sun Microsystems
• Markov Chain Monte Carlo
• Semantic Modeling
• Semantic Layer
• Metrics Layer
• Headless BI
• Cube
  • Podcast Episode
• AtScale
• Star Schema
• Data Vault
• OLAP Cube
• RAG == Retrieval Augmented Generation
  • AI Engineering Podcast Episode
• KNN == K-Nearest Neighbers
• HNSW == Hierarchical Navigable Small World
• dbt Metrics Layer
• Soda Data
• LookML
• Hex
• PowerBI
• Tableau
• Semantic View (Snowflake)
• Databricks Genie
• Snowflake Cortex Analyst
• Malloy

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