Load Bun data to DuckDB

Bun is a JavaScript runtime environment providing native high-performance HTTP server and API routing capabilities for building custom REST APIs. Everything needed to build a working Bun → 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 Bun 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 Bun 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 Bun 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.

---

Bun API at a glance

These values come from the Bun API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Bun API?

Authentication is implemented at the application level by the developer, typically using standard HTTP Bearer tokens with JWTs or API keys passed in the Authorization header.

1. Get your credentials

Bun itself is a JavaScript runtime, not a SaaS platform with a single universal REST API dashboard. To obtain credentials for services running on Bun, you must follow the specific provider's instructions. Common patterns include: 1) Accessing a web-based dashboard for the service (e.g., Unkey, BunMail, or custom admin panels), 2) Running a seed script provided by the application (e.g., bun run src/db/seed.ts) to generate a new key, or 3) Checking your environment configuration files (e.g., .env) where API tokens are defined. For local development, credentials are often managed securely via Bun.secrets (which interfaces with the OS Keychain/Keyring).

2. Add them to .dlt/secrets.toml

Copy
[sources.bun_source]
api_key = "your_api_key_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 Bun data can I load into DuckDB?

These are the Bun endpoints dlt can load into DuckDB:

---

How do I load only new Bun records?

The Bun 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": "users", "endpoint": {
    "path": "api/users",
    # 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 Bun pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/v1/api-keys and /api/keys from the Bun API into DuckDB:

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

@dlt.source
def bun_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "not applicable; Bun is a runtime environment, not a hosted service with a fixed API base URL.",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "api/users", "data_selector": "data"}},
            {"name": "users_detail", "endpoint": {"path": "api/users/:id", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_bun_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bun_pipeline",
        destination="duckdb",
        dataset_name="bun_data",
    )
    load_info = pipeline.run(bun_source())
    print(load_info)


if __name__ == "__main__":
    load_bun_to_duckdb()

Run it with python bun_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 Bun 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("bun_pipeline").dataset()
df = data.users.df()
print(df.head())

SQL:

Copy
SELECT * FROM bun_data.users LIMIT 10;

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

---

How do I deploy the Bun 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 Bun 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 Bun 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