Summary
In this episode Michael Toy, co-creator of Malloy, talks about rethinking how we work with data beyond SQL. Michael shares the origins of Malloy from his and Lloyd Tabb’s experience at Looker, why SQL’s mental model often fights human problem solving, and how Malloy aims to be a composable, maintainable language that treats SQL as the assembly layer rather than something humans should write. He explores Malloy’s core ideas — semantic modeling tightly coupled with a query language, hierarchical data as the default mental model, and preserving context so analysis stays interactive and open-ended. He also digs into the developer experience and ecosystem: Malloy’s TypeScript implementation, VS Code integration, CLI, emerging notebook support, and how Malloy can sit alongside or replace parts of existing transformation workflows. Michael discusses practical trade-offs in language design, the surprising fit for LLM-generated queries, and near-term roadmap areas like dimensional filtering, better aggregation strategies across levels, and closing gaps that still require escaping to SQL. He closes with an invitation to contribute to the open-source project and help shape its evolution.

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 Michael Toy about Malloy, a modern language for building composable and maintainable analytics and data models on relational engines

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Malloy is and the story behind it?
- What is the core problem that you are trying to solve with Malloy?
There are countless projects that aim to reimagine/reinvent/replace SQL. What are the factors that make Malloy stand out in your mind?
Who are the target personas for the Malloy language?
One of the key success factors for any language is the ecosystem around it and the integrations available to it. How does Malloy fit in the toolchains and workflows for data engineers and analysts?
Can you describe the key design and syntax elements of Malloy?
- How have the scope and focus of the language evolved since you first started working on it?
How do the structure and semantics of Malloy change the ways that teams think about their data models?
SQL-focused tools have gained prominence as the means of building the tranfromation stage of data pipelines. How would you characterize the capabilities of Malloy as a tool for building translation pipelines?
What are the most interesting, innovative, or unexpected ways that you have seen Malloy used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Malloy?
When is Malloy the wrong choice?
What do you have planned for the future of Malloy?

Contact Info

Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

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

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Outro

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