Load Trino data to DuckDB

Trino is a distributed SQL query engine that provides a REST API for clients to submit queries and retrieve results. Everything needed to build a working Trino → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

---

Build your Trino to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Trino to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Trino API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

---

Trino API at a glance

These values come from the Trino API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Trino API?

Authentication is typically handled via an Authorization header with a Bearer token. For Basic authentication, requests use standard HTTP Basic Auth headers, while JWT-based sessions also utilize the Bearer token scheme in the Authorization header.

1. Get your credentials

Trino does not utilize a traditional API key-based authentication system. Instead, authentication is determined by the Trino cluster's configuration, which typically delegates identity to LDAP, Kerberos, OAuth 2.0, or TLS certificates. To authenticate against the Trino REST API: 1. Consult your cluster administrator to identify the enabled authentication method (e.g., LDAP/Basic Auth, JWT, or OAuth 2.0). 2. If Basic Auth is enabled, use your standard username and password credentials. 3. If OAuth 2.0 or JWT is required, obtain the appropriate bearer token from your identity provider. 4. Include credentials in your HTTP request headers (e.g., 'Authorization: Basic <base64_encoded_creds>' or 'Authorization: Bearer ') or via the 'X-Trino-User' header for identity propagation depending on the server setup. There is no central dashboard to generate an "API key" because Trino treats API access as a standard client connection.

2. Add them to .dlt/secrets.toml

Copy
[sources.trino_source]
trino_user = "your_username"
trino_password = "your_password"
# If using JWT/OAuth2
trino_access_token = "your_bearer_token"
# If using advanced client protocol spooling
protocol_spooling_shared_secret = "your_256bit_base64_secret"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

---

What Trino data can I load into DuckDB?

These are the Trino endpoints dlt can load into DuckDB:

---

How do I load only new Trino records?

The Trino API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

Copy
{"name": "statement", "endpoint": {
    "path": "v1/statement",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

---

What does the generated Trino pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/statement and /v1/info from the Trino API into DuckDB:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def trino_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "/v1/statement",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "statement", "endpoint": {"path": "v1/statement", "data_selector": "data"}},
            {"name": "query_results", "endpoint": {"path": "{nextUri}", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_trino_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="trino_pipeline",
        destination="duckdb",
        dataset_name="trino_data",
    )
    load_info = pipeline.run(trino_source())
    print(load_info)


if __name__ == "__main__":
    load_trino_to_duckdb()

Run it with python trino_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

---

How do I query Trino data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("trino_pipeline").dataset()
df = data.statement.df()
print(df.head())

SQL:

Copy
SELECT * FROM trino_data.statement LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

---

How do I deploy the Trino to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Trino loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load Trino data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

---

Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack