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Trino
A distributed SQL engine that queries data where it already lives, across object storage, warehouses and operational databases.
As of 30 August 2026, Trino is free to use. Trino runs one SQL query across S3 tables, Postgres, MySQL, Kafka and dozens of other sources without copying data first. Softwr lists it under Technology.
Overview
What Trino does
Trino is an open source distributed SQL query engine licensed under Apache 2.0 and governed by the Trino Software Foundation. It was created by the original authors of Presto, Martin Traverso, Dain Sundstrom and David Phillips, who left Meta in 2018 and renamed the project from PrestoSQL to Trino in December 2020; the separate PrestoDB project continues under the Linux Foundation. It is a Java service consisting of a coordinator and workers, holds no data of its own, and reaches sources through connectors: Hive, Iceberg, Delta Lake, PostgreSQL, MySQL, SQL Server, MongoDB, Elasticsearch, Kafka, BigQuery and several dozen more. Starburst is the principal commercial vendor. The distinguishing property is federation with pushdown. A single query can join a fact table in Iceberg on S3 against a dimension table in a production Postgres and a lookup in MongoDB, with predicates and aggregations pushed down to each source where the connector supports it. Commercially this changes what a data team has to build: the question that would otherwise require an ingestion pipeline, a schedule and a copy of somebody else's data can be answered as a query today. That makes it the usual choice for ad hoc analytics across a fragmented estate, and the usual first step for organisations that want a lakehouse without first migrating everything into one warehouse. It is adopted by data platform teams with data spread across several systems and analysts who want one SQL endpoint over all of it. The trade-offs are direct consequences of holding no data. Query performance depends on file layout, partitioning and statistics in the source, which Trino cannot fix; a careless federated query can put a full table scan onto a production database; and because it is memory-based with disk spilling deprecated in favour of fault-tolerant execution, a query that exceeds cluster memory fails rather than slowing down.
What people use it for
- Ad hoc analysis that spans a data lake and one or more operational databases, without building an ingestion pipeline first
- Serving a BI tool a single SQL endpoint over an estate that is actually several separate storage systems
- Querying Iceberg or Delta tables on object storage interactively, as the compute layer of a lakehouse
- Investigating whether a dataset is worth ingesting, by querying it in place before committing to a pipeline for it
The honest half
Where it falls short
Concrete and checkable, so you can decide whether any of them matter to you. This is the half of a review a vendor will not write about Trino.
- Trino stores nothing and computes no statistics of its own, so the plan it produces is only as good as the partitioning, file sizes and table statistics on the source; a Hive table of thousands of small files or a lake with no stats produces a slow query the engine cannot improve.
- Federated queries pull data out of the systems they touch, so a join between a lake table and a production Postgres can put a full table scan onto an OLTP database that other applications depend on, and the person who wrote the query will not see the incident it causes.
- Releases come roughly every one to two weeks with no community long-term support line, and deprecations arrive quickly, so you either dedicate someone to keeping current or you buy Starburst Enterprise for a supported long-term version.
- It is memory-based and disk spilling was deprecated in favour of fault-tolerant execution, so a query exceeding cluster memory fails outright rather than degrading; enabling fault-tolerant execution requires an external exchange store on object storage and makes queries measurably slower.
- It is a query engine and not a warehouse: there is no built-in job scheduling, no incremental materialised view maintenance and no transformation framework, so producing curated tables still needs dbt or an equivalent layer that somebody has to own.
- The 2020 fork split the ecosystem, so documentation, connectors, Stack Overflow answers and vendor material written before then describe PrestoDB, which is now a different project with different behaviour, and following the wrong one wastes real time.
Cross-shopped
What people choose instead of Trino
Each pairing was judged by two reviewers asking whether a buyer would genuinely weigh the two against each other. The ones that failed were deleted rather than published.
- TrinovsClickUp
ClickUp: One app to replace them all
- TrinovsLinear
Linear: The issue tracking tool you'll enjoy using
- TrinovsFigma
Figma: The collaborative interface design tool
Capabilities
Features
Federated querying
One ANSI SQL query joining across object storage tables, relational databases, document stores and streams
Connector architecture
Several dozen connectors including Iceberg, Delta Lake, Hive, PostgreSQL, MySQL, MongoDB, Elasticsearch, Kafka and BigQuery
Predicate and aggregation pushdown
Delegates filters, projections and some aggregations to the source system so less data crosses the network
Massively parallel execution
A coordinator plans and distributes stages across worker nodes that stream results between each other in memory
Fault-tolerant execution
Optional mode that checkpoints intermediate results to an external exchange store so long queries survive worker loss
Resource groups
Queues and limits queries by user, source or client tag so an analyst cannot starve a scheduled workload
Iceberg and Delta table support
Reads and writes open table formats with schema evolution, time travel and transactional commits
Standard client protocols
JDBC and ODBC drivers plus a REST API, so existing BI tools connect without adaptation
Answered, with sources
Questions people ask
Each answer names the page it came from, so you can check it rather than take our word for it.
What is the difference between Trino and Presto?
They share an origin. The original creators left Meta and renamed their fork from PrestoSQL to Trino in December 2020; PrestoDB continues separately under the Linux Foundation. They have diverged in features, connectors and SQL behaviour, so material written for one may not apply to the other.
Does Trino replace my data warehouse?
Not on its own. It is compute without storage, scheduling or transformation. Paired with Iceberg or Delta on object storage and something like dbt for modelling it can serve as a lakehouse; used alone it is a query layer over what you already have.
Why is my federated query slow?
Usually because a connector could not push a filter or aggregation down, so Trino is pulling whole tables across the network to join them itself. The fix is usually better source-side partitioning or statistics, or ingesting that source rather than federating it.
What happens when a query runs out of memory?
It fails. Disk spilling was deprecated in favour of fault-tolerant execution, which checkpoints to an external exchange store such as S3 and lets long queries survive memory pressure and worker loss, at the cost of noticeably slower execution.
Is there commercial support?
Yes, from Starburst, which offers Starburst Enterprise with long-term supported releases and Starburst Galaxy as a managed service. The open source project itself has no long-term support line.
Keep looking
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