Load Pastebin API data to DuckDB

Pastebin is a plain text storage service that provides an API for creating, listing, and managing text snippets called pastes. Everything needed to build a working Pastebin 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.

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Build your Pastebin 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 Pastebin 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 Pastebin 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.

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Pastebin API API at a glance

These values come from the Pastebin API API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Pastebin API API?

Pastebin does not use standard Authorization headers; it requires an 'api_dev_key' (developer key) and an optional 'api_user_key' (user session token) to be sent as part of the POST body payload with 'application/x-www-form-urlencoded' content type. The user key is obtained via a separate login POST request using the username and password.

1. Get your credentials

To obtain your Pastebin API credentials, first sign up for a member account at https://pastebin.com/signup. Once logged in, navigate to the official API documentation page at https://pastebin.com/doc_api. Your 'Developer API Key' (api_dev_key) will be listed in the 'Your Unique Developer API Key' section on that page. To generate an 'API User Key' (api_user_key) for managing user-specific pastes, perform a POST request to the API login endpoint with your username and password, or use the online generator provided at https://pastebin.com/api/api_user_key.html.

2. Add them to .dlt/secrets.toml

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[sources.pastebin_api_source]
api_dev_key = "REPLACE_ME"

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 Pastebin API data can I load into DuckDB?

These are the Pastebin API endpoints dlt can load into DuckDB:

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How do I load only new Pastebin API records?

The Pastebin 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.

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{"name": "list_user_pastes", "endpoint": {
    "path": "api/api_post.php",
    # 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 Pastebin API pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading api_post.php and api_raw.php from the Pastebin API API into DuckDB:

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

@dlt.source
def pastebin_api_source(api_dev_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://pastebin.com/api/",
            "auth": {"type": "api_key", "api_key": api_dev_key, "name": "api_dev_key"},
        },
        "resources": [
            {"name": "list_user_pastes", "endpoint": {"path": "api/api_post.php"}},
            {"name": "list_recent_pastes", "endpoint": {"path": "api_scraping.php"}}
        ],
    }
    yield from rest_api_resources(config)


def load_pastebin_api_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pastebin_api_pipeline",
        destination="duckdb",
        dataset_name="pastebin_api_data",
    )
    load_info = pipeline.run(pastebin_api_source())
    print(load_info)


if __name__ == "__main__":
    load_pastebin_api_to_duckdb()

Run it with python pastebin_api_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 Pastebin 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):

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import dlt

data = dlt.pipeline("pastebin_api_pipeline").dataset()
df = data.list_user_pastes.df()
print(df.head())

SQL:

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SELECT * FROM pastebin_api_data.list_user_pastes LIMIT 10;

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

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How do I deploy the Pastebin 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 Pastebin API 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 Pastebin 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.

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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