Load Runpod S3 API data to DuckDB

Runpod S3 API is an S3-compatible interface for managing files and datasets on Runpod network volumes. Everything needed to build a working Runpod S3 API → 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 Runpod S3 API 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 Runpod S3 API 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 Runpod S3 API 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.

---

Runpod S3 API API at a glance

These values come from the Runpod S3 API API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Runpod S3 API API?

Authentication requires an S3 Access Key (your Runpod user ID) and a Secret Access Key. These credentials are used via standard AWS S3-compatible signature headers, not a Bearer token.

1. Get your credentials

To obtain S3-compatible API credentials, navigate to the Runpod console Settings page (https://www.console.runpod.io/user/settings). Expand the S3 API Keys section, select Create an S3 API key, and provide a name. Copy the access key (starts with user_) and the secret access key (starts with rps_). The secret is only displayed once; ensure it is saved securely. For general REST API access, navigate to the same Settings page, expand the API Keys section, and select Create API Key to generate a token for Bearer authentication.

2. Add them to .dlt/secrets.toml

Copy
[sources.runpod_s3_api_source]
runpod_s3_access_key = "user_your_user_id_here"
runpod_api_key = "your_bearer_token_here"

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 Runpod S3 API data can I load into DuckDB?

These are the Runpod S3 API endpoints dlt can load into DuckDB:

---

How do I load only new Runpod S3 API records?

The Runpod S3 API 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": "pods", "endpoint": {
    "path": "/v2/pods",
    # 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 Runpod S3 API pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading pods and endpoints from the Runpod S3 API API into DuckDB:

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

@dlt.source
def runpod_s3_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://s3api-<datacenter>.runpod.io/",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "pods", "endpoint": {"path": "/v2/pods"}},
            {"name": "endpoints", "endpoint": {"path": "/v2/endpoints"}}
        ],
    }
    yield from rest_api_resources(config)


def load_runpod_s3_api_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="runpod_s3_api_pipeline",
        destination="duckdb",
        dataset_name="runpod_s3_api_data",
    )
    load_info = pipeline.run(runpod_s3_api_source())
    print(load_info)


if __name__ == "__main__":
    load_runpod_s3_api_to_duckdb()

Run it with python runpod_s3_api_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 Runpod S3 API 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("runpod_s3_api_pipeline").dataset()
df = data.endpoints.df()
print(df.head())

SQL:

Copy
SELECT * FROM runpod_s3_api_data.endpoints LIMIT 10;

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

---

How do I deploy the Runpod S3 API 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 Runpod S3 API 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 Runpod S3 API 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