Tenfold NetSuite Integration Python API Docs | dltHub

Tenfold provides REST API documentation for integrating with Oracle NetSuite, focusing on setup and configuration. The key guide is available at https://docs.tenfold.com/en/oracle-netsuite-integration-guide.html. This guide covers essential integration steps and settings. The REST API base URL is No fixed base URL; use the External URL of each deployed RESTlet script as the endpoint. and Authentication uses NetSuite Token‑Based Authentication (Consumer Key/Secret and Access Token ID/Secret)..

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 Tenfold NetSuite Integration data in under 10 minutes.

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What data can I load from Tenfold NetSuite Integration?

Here are some of the endpoints you can load from Tenfold NetSuite Integration:

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How do I authenticate with the Tenfold NetSuite Integration API?

Requests must be signed with OAuth 1.0 using the Consumer Key/Secret and Access Token ID/Secret. The required Authorization header is generated from these four values.

1. Get your credentials

1. In NetSuite, go to Setup → Integrations → Manage Integrations → New. Name it (e.g., "Tenfold Integration") and select Token‑Based Authentication.
2. Save the record and copy the generated Consumer Key and Consumer Secret.
3. Create a Role with appropriate permissions and assign it to a Service Employee.
4. Navigate to Setup → Users/Roles → Access Tokens → New. Select the Integration, Role, and Employee, then generate the Token ID and Token Secret.
5. In the Tenfold dashboard, open CRM Settings and paste the Consumer Key, Consumer Secret, Token ID, and Token Secret.

2. Add them to .dlt/secrets.toml

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[sources.tenfold_netsuite_integration_source]
consumer_key = "<your_consumer_key>"
consumer_secret = "<your_consumer_secret>"
token_id = "<your_token_id>"
token_secret = "<your_token_secret>"

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 Tenfold NetSuite Integration 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 tenfold_netsuite_integration_pipeline.py

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

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Pipeline tenfold_netsuite_integration_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset tenfold_netsuite_integration_data
The duckdb destination used duckdb:/tenfold_netsuite_integration.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 tenfold_netsuite_integration_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 load and search from the Tenfold NetSuite Integration 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 tenfold_netsuite_integration_source(consumer_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "No fixed base URL; use the External URL of each deployed RESTlet script as the endpoint.",
            "auth": {
                "type": "api_key",
                "token_id": consumer_key,
            },
        },
        "resources": [
            {"name": "load", "endpoint": {"path": "load"}},
            {"name": "search", "endpoint": {"path": "search"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tenfold_netsuite_integration_pipeline",
        destination="duckdb",
        dataset_name="tenfold_netsuite_integration_data",
    )
    load_info = pipeline.run(tenfold_netsuite_integration_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("tenfold_netsuite_integration_pipeline").dataset()
sessions_df = data.load.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM tenfold_netsuite_integration_data.load LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("tenfold_netsuite_integration_pipeline").dataset()
data.load.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 Tenfold NetSuite Integration 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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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