Trino vs Spark vs DuckDB for the Open Lakehouse

The worst way to choose a lakehouse engine is to ask which one is best.

The engine is not the platform

Trino, Spark, and DuckDB can all be useful in an open lakehouse. That does not mean they should do the same job. Engines have different design centers, operational models, and user experiences.

ODI gives teams the right frame. Keep the table and catalog contracts open. Then choose engines based on workload fit instead of turning one engine into a religion.

Trino is built for interactive federated SQL

Trino is usually strongest when teams need fast interactive SQL across data sources and a shared query layer for analysts, applications, and platform services. Its Iceberg connector supports reading and writing Iceberg tables and exposes many lakehouse operations through SQL.

The tradeoff is operational discipline. Trino is a distributed service with coordinators, workers, catalogs, access control, and workload management. It is powerful because it centralizes query access. It needs production ownership for the same reason.

Spark is built for distributed processing and pipelines

Spark remains a natural fit for heavy batch processing, large transformations, machine learning pipelines, and structured streaming workloads. Iceberg support through Spark gives teams a familiar processing layer over open table contracts.

Spark is not automatically the best interactive SQL engine for every analyst workload. It is often the workhorse for jobs that need distributed processing, custom logic, and ecosystem depth.

DuckDB is built for local analytical power

DuckDB is compelling because it brings serious analytical execution into local, embedded, and lightweight environments. Its Iceberg extension can scan Iceberg tables and metadata, which makes it useful for exploration, development, and edge analytics patterns.

That does not make DuckDB a replacement for every shared service. It is a different shape: local-first, embeddable, and excellent when the working set and concurrency model fit.

A practical decision model

• Use Trino when many users need interactive SQL across governed data.
• Use Spark when pipelines, distributed processing, or ML workloads dominate.
• Use DuckDB when local analytics, embedded use cases, or developer workflows need fast SQL close to the user.

The open lakehouse pattern is not choosing one forever. It is making sure all three can participate without corrupting table meaning.

Sources to start with

Use official engine and connector docs to verify workload fit and Iceberg support boundaries.

• Trino Iceberg connector documentation
• Apache Spark documentation
• Apache Iceberg Spark structured streaming documentation
• DuckDB Iceberg extension documentation
• Apache Iceberg table specification