# Trino

> Trino - How to connect to the native Trino connector.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-08-30T06:11:46.109616+00:00` (UTC).

## Primary page

- [Trino](https://docs.datarobot.com/en/docs/reference/data-ref/connectivity/data-sources/dc-trino.html.md): Full documentation for this topic (Markdown sidecar).

## Sections on this page

- [Supported authentication](https://docs.datarobot.com/en/docs/reference/data-ref/connectivity/data-sources/dc-trino.html.md#supported-authentication): In-page section heading.
- [Prerequisites](https://docs.datarobot.com/en/docs/reference/data-ref/connectivity/data-sources/dc-trino.html.md#prerequisites): In-page section heading.
- [Batch predictions requirements](https://docs.datarobot.com/en/docs/reference/data-ref/connectivity/data-sources/dc-trino.html.md#batch-predictions-requirements): In-page section heading.
- [Required parameters](https://docs.datarobot.com/en/docs/reference/data-ref/connectivity/data-sources/dc-trino.html.md#required-parameters): In-page section heading.
- [Troubleshooting](https://docs.datarobot.com/en/docs/reference/data-ref/connectivity/data-sources/dc-trino.html.md#troubleshooting): In-page section heading.
- [Code examples](https://docs.datarobot.com/en/docs/reference/data-ref/connectivity/data-sources/dc-trino.html.md#code-examples): In-page section heading.

## Related documentation

- [Reference documentation](https://docs.datarobot.com/en/docs/reference/index.html.md): Linked from this page.
- [Data](https://docs.datarobot.com/en/docs/reference/data-ref/index.html.md): Linked from this page.
- [Connectivity](https://docs.datarobot.com/en/docs/reference/data-ref/connectivity/index.html.md): Linked from this page.
- [Structured data stores](https://docs.datarobot.com/en/docs/reference/data-ref/connectivity/data-sources/index.html.md): Linked from this page.
- [intake source](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html.md#trino-scoring): Linked from this page.
- [output destination](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-options.html.md#trino-write): Linked from this page.
- [Allowed source IP addresses](https://docs.datarobot.com/en/docs/reference/data-ref/connectivity/allowed-ips.html.md): Linked from this page.

## Documentation content

## Supported authentication

- Basic (username/password)

## Prerequisites

The following is required before connecting to Trino in DataRobot:

- Data stored in a Trino database

## Batch predictions requirements

You can use Trino as both an [intake source](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html.md#trino-scoring) and an [output destination](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-options.html.md#trino-write) for batch prediction jobs.

- Uselowercase onlyfor column names in the dataset used to train a project. Trino sanitizes column names automatically (unquoted identifiers are lowercased), so mixed-case or uppercase column names can cause column inconsistency errors when reading from Trino for batch scoring. This applies even when creating tables with quoted column names—Trino still stores them as lowercase.
- When using Trino as a batch predictionoutputdestination, set an explicit numericchunkSizein bytes. Named chunk strategies (auto,dynamic,fixed) are not supported. The valuemust not exceed 1,000,000 bytes(1MB), which is the default Trinoquery.max-lengthlimit. Exceeding this limit or using a named strategy will cause the job to fail.

## Required parameters

The table below lists the minimum required fields to establish a connection with Trino:

| Required field | Description | Documentation |
| --- | --- | --- |
| Host | The hostname or IP address of your Trino coordinator. | Trino documentation |

## Troubleshooting

| Problem | Solution | Instructions |
| --- | --- | --- |
| When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. |

## Code examples

The Python example below shows how to connect to and move data from Trino into DataRobot.

Initialize the DataRobot client and define database details for later use:

```
api_token = '<token>'
endpoint = 'https://app.datarobot.com/api/v2'

import datarobot as dr
from datarobot.enums import DataStoreTypes

dr.Client(token=api_token, endpoint=endpoint)

TRINO_HOST = "datarobot.trino.galaxy.starburst.io"
TRINO_PORT = 443
USE_SSL = "true"
CATALOG = "<catalog>"
SCHEMA = "<schema>"
TABLE = "<table>"
QUERY = None
TRINO_USERNAME = "<username>"
TRINO_PASSWORD = "<password>"
```

Do one of the following to locate your Trino driver ID:

- Create the Trino driver ID: trino_driver=dr.DataDriver.create(class_name=DataStoreTypes.DR_DATABASE_V1,canonical_name='Trino Driver',database_driver='trino-v1',)
- Reference an existing Trino driver ID: trino_driver=dr.DataDriver.get('<trino_driver_id>')

Create (or reuse) Trino credentials and securely save them in DataRobot:

```
trino_credentials = dr.Credential.create_basic(
    name='Trino Credentials',
    user=TRINO_USERNAME,
    password=TRINO_PASSWORD,
)
```

Define a connection to the external data store:

```
datastore_fields = [
    {"id": "host", "name": "Host Name", "value": TRINO_HOST},
    {"id": "port", "name": "port", "value": str(TRINO_PORT)},
    {"id": "ssl", "name": "ssl", "value": USE_SSL},
]

trino_datastore = dr.DataStore.create(
    data_store_type=DataStoreTypes.DR_DATABASE_V1,
    canonical_name='Trino Datastore',
    driver_id=trino_driver.id,
    fields=datastore_fields,
)
```

Point to a specific data source (table or query):

```
data_source_params = dr.DataSourceParameters(
    data_store_id=trino_datastore.id,
    catalog=CATALOG,
    schema=SCHEMA,
    table=TABLE,
    query=QUERY,
)

trino_datasource = dr.DataSource.create(
    data_source_type=DataStoreTypes.DR_DATABASE_V1,
    canonical_name='Trino DataSource',
    params=data_source_params,
)
```

Pull the data from Trino and import a snapshotted version into DataRobot:

```
trino_dataset = trino_datasource.create_dataset(
    do_snapshot=True,
    credential_id=trino_credentials.id,
)
```