Summary
In this episode Kostas Pardalis talks about Fenic - an open-source, PySpark-inspired dataframe engine designed to bring LLM-powered semantics into reliable data engineering workflows. Kostas shares why today’s data infrastructure assumptions (BI-first, expert-operated, CPU-bound) fall short for AI-era tasks that are increasingly inference- and IO-bound. He explores how Fenic introduces semantic operators (e.g., semantic filter, extract, join) as first-class citizens in the logical plan so the optimizer can reason about inference, costs, and constraints. This enables developers to turn unstructured data into explicit schemas, compose transformations lazily, and offload LLM work safely and efficiently. He digs into Fenic’s architecture (lazy dataframe API, logical/physical plans, Polars execution, DuckDB/Arrow SQL path), how it exposes tools via MCP for agent integration, and where it fits in context engineering as a companion for memory/state management in agentic systems.

Announcements

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Your host is Tobias Macey and today I'm interviewing Kostas Pardalis about Fenic, an opinionated, PySpark-inspired DataFrame framework for building AI and agentic applications

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Fenic is and the story behind it?
What are the core problems that you are trying to address with Fenic?
Dataframes have become a popular interface for doing chained transformations on structured data. What are the benefits of using that paradigm for LLM use-cases?
Can you describe the architecture and implementation of Fenic?
How have the design and scope of the project changed since you first started working on it?
You position Fenic as a means of bringing reliability to LLM-powered transformations. What are some of the anti-patterns that teams should be aware of when getting started with Fenic?
What are some of the most common first steps that teams take when integrating Fenic into their pipelines or applications?
What are some of the ways that teams should be thinking about using Fenic and semantic operations for data pipelines and transformations?
How does Fenic help with context engineering for agentic use cases?
What are some examples of toolchains/workflows that could be replaced with Fenic?
How does Fenic integrate with the broader ecosystem of data and AI frameworks? (e.g. Polars, Arrow, Qdrant, LangChan/Pydantic AI)
What are the most interesting, innovative, or unexpected ways that you have seen Fenic used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Fenic?
When is Fenic the wrong choice?
What do you have planned for the future of Fenic?

Contact Info

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.
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Links

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

Semantic Operators Meet Dataframes: Building Context for Agents with FENIC

Data Engineering Podcast

Reliability patterns and anti-patterns with LLMs

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