Northbeam Python API Docs | dltHub

Northbeam is a marketing analytics and attribution platform that provides APIs to ingest orders and export performance/exported metrics and reports. The REST API base URL is https://api.northbeam.io/v1 and All requests require an API key plus a Data-Client-ID header..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Northbeam data in under 10 minutes.

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What data can I load from Northbeam?

Here are some of the endpoints you can load from Northbeam:

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How do I authenticate with the Northbeam API?

Requests must include Authorization header with the API key and Data-Client-ID header with your client id. Content-Type: application/json for JSON requests; accept: application/json for GETs.

1. Get your credentials

1. Log into Northbeam dashboard. 2) Go to Settings → API Keys. 3) Click Create new API key. 4) Copy the generated Authorization (API key) and Data-Client-ID values and store them securely.

2. Add them to .dlt/secrets.toml

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[sources.northbeam_spend_api_source]
api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Northbeam API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python northbeam_spend_api_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline northbeam_spend_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset northbeam_spend_api_data
The duckdb destination used duckdb:/northbeam_spend_api.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline northbeam_spend_api_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads metrics and data_export_result from the Northbeam API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def northbeam_spend_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.northbeam.io/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "metrics", "endpoint": {"path": "v1/exports/metrics", "data_selector": "metrics"}},
            {"name": "attribution_models", "endpoint": {"path": "v1/exports/attribution-models", "data_selector": "attribution_models"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="northbeam_spend_api_pipeline",
        destination="duckdb",
        dataset_name="northbeam_spend_api_data",
    )
    load_info = pipeline.run(northbeam_spend_api_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("northbeam_spend_api_pipeline").dataset()
sessions_df = data.exports.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM northbeam_spend_api_data.exports LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("northbeam_spend_api_pipeline").dataset()
data.exports.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Northbeam data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401/403 responses, verify the Authorization header contains your API key exactly and Data-Client-ID header is present. Ensure you are using the correct environment (api-uat for sandbox vs api.northbeam.io for production).

Rate limits and retries

Exports and endpoints have rate limits; polling export status is allowed at ~1 request/sec. Respect rate limit headers and back off on 429 responses. Retry idempotent requests using the same order_id for Orders API.

Orders API validation errors

Orders API will return payload_validation_error and other validation messages in diagnostics. Common causes: missing required fields (order_id, products, customer_id), time_of_purchase earlier than pixel event, invalid ISO-8601 timestamps. Use the sandbox endpoint to validate payloads before production.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack