Load Byteplant-address-validator data to DuckDB

Byteplant Address Validator is an address validation, correction, autocomplete, and geocoding REST API for global addresses. Everything needed to build a working Byteplant-address-validator → 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 Byteplant-address-validator 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 Byteplant-address-validator 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 Byteplant-address-validator 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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Byteplant-address-validator API at a glance

These values come from the Byteplant-address-validator API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Byteplant-address-validator API?

Authentication is performed by passing an API key as a query parameter named 'APIKey' in all requests.

1. Get your credentials

To obtain an API key for Byteplant services, navigate to the official Byteplant account management page at https://www.byteplant.com/account/. You can register for an account or sign up for a free trial at https://www.byteplant.com/en/address-validator/signup.html to receive your credentials. Once logged into your account dashboard, your API key will be available for use with the address or email validation REST APIs.

2. Add them to .dlt/secrets.toml

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[sources.byteplant_address_validator_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.

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What Byteplant-address-validator data can I load into DuckDB?

These are the Byteplant-address-validator endpoints dlt can load into DuckDB:

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How do I load only new Byteplant-address-validator records?

The Byteplant-address-validator 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": "verify", "endpoint": {
    "path": "api/verify",
    # 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 Byteplant-address-validator pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/verify and /api/fetch from the Byteplant-address-validator API into DuckDB:

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

@dlt.source
def byteplant_address_validator_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.address-validator.net",
            "auth": {"type": "api_key", "api_key": api_key},
        },
        "resources": [
            {"name": "verify", "endpoint": {"path": "api/verify"}},
            {"name": "search", "endpoint": {"path": "api/search", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_byteplant_address_validator_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="byteplant_address_validator_pipeline",
        destination="duckdb",
        dataset_name="byteplant_address_validator_data",
    )
    load_info = pipeline.run(byteplant_address_validator_source())
    print(load_info)


if __name__ == "__main__":
    load_byteplant_address_validator_to_duckdb()

Run it with python byteplant_address_validator_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 Byteplant-address-validator 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("byteplant_address_validator_pipeline").dataset()
df = data.verify.df()
print(df.head())

SQL:

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SELECT * FROM byteplant_address_validator_data.verify LIMIT 10;

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

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How do I deploy the Byteplant-address-validator 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 Byteplant-address-validator 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 Byteplant-address-validator 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