Load Abstract data to DuckDB

Abstract API provides a collection of REST APIs for data validation, enrichment, and intelligence services across multiple domains. Everything needed to build a working Abstract → 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 Abstract 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 Abstract 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 Abstract 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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Abstract API at a glance

These values come from the Abstract API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Abstract API?

Authentication is performed by appending the unique API key as a query parameter named 'api_key' to the base URL of the specific API. Some documentation also mentions the possibility of using an Authorization header, though query parameter is the standard method described.

1. Get your credentials

1. Visit the Abstract API signup page to create an account. 2. Log in to the Abstract API dashboard. 3. Navigate the dashboard's side menu to select the specific API you wish to use. 4. Once the specific API page loads, locate your unique API key, typically displayed in a prominent area such as 'Your API Key'. 5. Copy the key for use in your integration. Note that each Abstract service uses a unique API key, meaning you must repeat this process for every distinct API you intend to use.

2. Add them to .dlt/secrets.toml

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[sources.abstract_source]
abstract_api_key = "your_unique_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.

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What Abstract data can I load into DuckDB?

These are the Abstract endpoints dlt can load into DuckDB:

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How do I load only new Abstract records?

The Abstract 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": "ip_geolocation", "endpoint": {
    "path": "v1",
    # 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 Abstract pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading email-validation and ip-geolocation from the Abstract API into DuckDB:

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

@dlt.source
def abstract_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{service}.abstractapi.com/v1/",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "ip_geolocation", "endpoint": {"path": "v1"}},
            {"name": "company_enrichment", "endpoint": {"path": "v2"}}
        ],
    }
    yield from rest_api_resources(config)


def load_abstract_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="abstract_pipeline",
        destination="duckdb",
        dataset_name="abstract_data",
    )
    load_info = pipeline.run(abstract_source())
    print(load_info)


if __name__ == "__main__":
    load_abstract_to_duckdb()

Run it with python abstract_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 Abstract 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("abstract_pipeline").dataset()
df = data.ip_geolocation.df()
print(df.head())

SQL:

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SELECT * FROM abstract_data.ip_geolocation LIMIT 10;

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

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How do I deploy the Abstract 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 Abstract 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 Abstract 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