Load Presto data to DuckDB

The Presto Client REST API allows users to submit SQL queries to a Presto coordinator, manage query execution, and stream back results. Everything needed to build a working Presto → 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.

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Build your Presto 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 Presto 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 Presto 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.

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Presto API at a glance

These values come from the Presto API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Presto API?

Authentication is handled via the Presto coordinator and depends on the specific security mechanism enabled (e.g., Kerberos, LDAP, OAuth 2.0, or custom authenticators). Requests typically require appropriate headers like X-Presto-User for identity, and security is often configured in the coordinator's config.properties file.

1. Get your credentials

Presto does not have a single unified "API key setup dashboard." Authentication is highly dependent on how your organization has configured the Presto coordinator. Common methods include: 1. Basic Authentication (LDAP/Password file): Obtain your username and password from your organization's Presto administrator. 2. OAuth 2.0: Obtain a client ID, client secret, and token URL from your identity provider or Presto administrator. 3. Kerberos: Ensure your environment is configured with a valid keytab and krb5.conf. Consult your administrator to determine which method is enabled for your specific Presto instance.

2. Add them to .dlt/secrets.toml

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[sources.presto_source]
username = "your_username"
password = "your_password"
host = "your_presto_coordinator_host"
port = 8080
database = "your_catalog"

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.

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What Presto data can I load into DuckDB?

These are the Presto endpoints dlt can load into DuckDB:

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How do I load only new Presto records?

The Presto 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.

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{"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.

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What does the generated Presto 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 Presto API into DuckDB:

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

@dlt.source
def presto_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://{coordinator}:{port}",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "statement", "endpoint": {"path": "v1/statement"}},
            {"name": "task", "endpoint": {"path": "v1/task"}}
        ],
    }
    yield from rest_api_resources(config)


def load_presto_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="presto_pipeline",
        destination="duckdb",
        dataset_name="presto_data",
    )
    load_info = pipeline.run(presto_source())
    print(load_info)


if __name__ == "__main__":
    load_presto_to_duckdb()

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

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How do I query Presto 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):

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import dlt

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

SQL:

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SELECT * FROM presto_data.statement LIMIT 10;

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

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How do I deploy the Presto 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 Presto loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Presto 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.

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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