What Open Data Infrastructure looks like with dbt Mesh

At a dbt Summit 2026 breakout session, Jack Lowery, lead sales engineer specialist for data lakes and transformations at Fivetran, and Anna Lee, software engineer at dbt Labs, walked through what Open Data Infrastructure looks like in practice, built around Apache Iceberg and a live, cross-platform dbt Mesh demo.

Open Data Infrastructure, or ODI, is the idea that data should live in an open format that any compute engine can read, rather than locked inside one warehouse's proprietary storage. Lee explained the technical backbone: a new catalogs.yml v2 spec that decouples dbt's Iceberg configuration from the older, Snowflake-specific concept of a catalog integration and write integration. That change means a dbt project can define where its Iceberg tables live independently of which warehouse is doing the writing.

The session's centerpiece was a live demo of a cross-platform dbt Mesh spanning three teams. A platform team on Snowflake wrote to a catalog Lowery and Lee referred to as Horizon, a finance team on Databricks wrote to a catalog called Unity, and a marketing team on Snowflake read from both, using catalog federation and catalog-linked databases to make tables written by one engine visible to another without copying the underlying data. That is the practical promise of ODI: three teams, two warehouses, one shared set of Iceberg tables.

Lowery also introduced two services built around this model. Managed Data Lake writes Iceberg or Delta Lake metadata directly into a customer's own cloud storage, using the open source Polaris catalog rather than a proprietary one. And Lake Compute, currently in private beta and built on DuckDB, lets specific dbt models run on a different, often cost-optimized compute engine than the rest of a project, configured through a simple adapter setting rather than a platform migration.

Lowery demoed Lake Compute live, routing a single model to run on DuckDB while the rest of the project kept running on Snowflake.

The through-line of the talk was that open table formats and shared catalogs are what make dbt Mesh genuinely cross-platform, rather than cross-platform in name only. Teams no longer need to standardize on one warehouse to share governed data with each other. They need a shared catalog and a table format every engine involved agrees to speak.

Readers curious about the mechanics behind the demo can start with dbt's Apache Iceberg support and about Iceberg catalogs documentation, the catalogs.yml reference, and dbt Mesh for the governance model that makes cross-project, cross-platform references possible in the first place. Lake Compute's private beta is documented at About Lake Compute.

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