NetSuite Python API Docs | dltHub

NetSuite is a cloud‑based ERP platform providing RESTful SuiteTalk APIs for programmatic access to NetSuite records, SuiteAnalytics, and metadata. The REST API base URL is https://{account_id}.suitetalk.api.netsuite.com/services/rest and Requests require Token‑Based Authentication (OAuth 1.0a) or OAuth 2.0 client credentials..

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 NetSuite data in under 10 minutes.

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What data can I load from NetSuite?

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

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How do I authenticate with the NetSuite API?

NetSuite supports Token‑Based Authentication (OAuth 1.0/HMAC‑SHA256) where the Authorization header contains consumer key, token id, token secret and realm, and OAuth 2.0 client‑credentials flow that returns a Bearer token used in the Authorization header.

1. Get your credentials

1. Navigate to Setup → Integration → Manage Integrations → New. Enable Token‑Based Authentication (or OAuth 2.0) and save to obtain Consumer Key and Consumer Secret.
2. Create a role with the REST Web Services permission and assign it to a user.
3. Go to Setup → Users/Roles → Access Tokens → New. Select the application, user, and role to generate a Token ID and Token Secret.
4. For OAuth 2.0, configure client ID/secret and RSA key on the integration record, then POST a JWT assertion to the token endpoint https://{accountID}.suitetalk.api.netsuite.com/services/rest/auth/oauth2/v1/token to obtain an access_token.

2. Add them to .dlt/secrets.toml

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[sources.netsuite_source]
consumer_key = "YOUR_CONSUMER_KEY"
consumer_secret = "YOUR_CONSUMER_SECRET"
token_id = "YOUR_TOKEN_ID"
token_secret = "YOUR_TOKEN_SECRET"
# or for OAuth2
client_id = "YOUR_CLIENT_ID"
client_secret = "YOUR_CLIENT_SECRET"
private_key = "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----"

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 NetSuite 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 netsuite_pipeline.py

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

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Pipeline netsuite_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset netsuite_data
The duckdb destination used duckdb:/netsuite.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 netsuite_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 customers and salesorders from the NetSuite 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 netsuite_source(auth=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{account_id}.suitetalk.api.netsuite.com/services/rest",
            "auth": {
                "type": "oauth1_tba_or_oauth2",
                "token": auth,
            },
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "record/v1/customer", "data_selector": "items"}},
            {"name": "sales_orders", "endpoint": {"path": "record/v1/salesOrder", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="netsuite_pipeline",
        destination="duckdb",
        dataset_name="netsuite_data",
    )
    load_info = pipeline.run(netsuite_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("netsuite_pipeline").dataset()
sessions_df = data.customers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM netsuite_data.customers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("netsuite_pipeline").dataset()
data.customers.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 NetSuite 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

401 Unauthorized — check consumer key/secret and token id/secret or OAuth2 token; ensure the role has REST Web Services permission and the correct account (realm) is used in the OAuth header.

Request signing and realm

Token‑Based Authentication requires a correctly formed OAuth 1.0 Authorization header with HMAC‑SHA256 and the realm set to the account ID; an incorrect signature or missing realm results in 401/403 errors.

Pagination and SuiteQL

Record collections are paginated; use limit and offset query parameters or follow links in the response. SuiteQL queries also support limit/offset for paging.

Rate limits and 429

NetSuite may return 429 Too Many Requests — implement exponential backoff and retry logic.

Common API errors

400 Bad Request, 401 Unauthorized, 403 Forbidden (insufficient permissions), 404 Not Found, 413 Request Entity Too Large, 429 Too Many Requests, 500 Internal Server Error.

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