Dsv Python API Docs | dltHub

DSV is a global transport and logistics company offering REST APIs for booking, tracking, labels, invoices, visibility, warehousing, XPress and related services. The REST API base URL is https://api.dsv.com and APIs require a DSV‑Subscription‑Key header and an OAuth2 Bearer token (legacy service keys are also supported)..

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 Dsv data in under 10 minutes.

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What data can I load from Dsv?

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

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How do I authenticate with the Dsv API?

DSV APIs use the DSV‑Subscription‑Key header for the API key and OAuth2 Bearer tokens via Authorization: Bearer {access_token}; legacy endpoints may also need DSV‑Service‑Auth, Actor or x‑pat headers.

1. Get your credentials

1. Sign up on the DSV Developer Portal and subscribe to the desired API product.
2. After approval, locate your DSV‑Subscription‑Key on your profile page.
3. For legacy XPress or warehousing services, retrieve service keys (DSV‑Service‑Auth, x‑pat) from the portal or email.
4. To obtain an OAuth2 token, subscribe to the "DSV Access Token" API and POST to the token endpoint (demo: https://api.dsv.com/my-demo/oauth/v1/token, prod: https://api.dsv.com/my/oauth/v1/token) with grant_type=client_credentials, client_id (username) and client_secret (password), including the Subscription‑Key header.

2. Add them to .dlt/secrets.toml

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[sources.dsv_source]
dsv_subscription_key = "your_subscription_key_here"
oauth_client_id = "your_mydsv_username"
oauth_client_secret = "your_mydsv_password"
# optional legacy tokens
service_auth = "your_service_auth_here"
x_pat = "your_xpat_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 Dsv 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 dsv_pipeline.py

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

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Pipeline dsv_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dsv_data
The duckdb destination used duckdb:/dsv.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 dsv_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 bookings and tracking_shipments from the Dsv 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 dsv_source(dsv_subscription_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.dsv.com",
            "auth": {
                "type": "bearer",
                "token": dsv_subscription_key,
            },
        },
        "resources": [
            {"name": "bookings", "endpoint": {"path": "my/booking/v2/bookings", "data_selector": "(depends on operation; booking list responses return bookings or items)"}},
            {"name": "tracking_shipments", "endpoint": {"path": "my/tracking/v1/shipments", "data_selector": "shipments"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dsv_pipeline",
        destination="duckdb",
        dataset_name="dsv_data",
    )
    load_info = pipeline.run(dsv_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("dsv_pipeline").dataset()
sessions_df = data.tracking_shipments.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM dsv_data.tracking_shipments LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("dsv_pipeline").dataset()
data.tracking_shipments.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 Dsv 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

Ensure DSV-Subscription-Key is present and valid. For OAuth2 flows include Authorization: Bearer {access_token}. For endpoints still supporting legacy auth include required headers (DSV-Service-Auth, Actor or x-pat) as documented. If token expired use refresh_token flow at the token endpoint.

Rate limits and pagination

Many endpoints are paginated; responses include page elements and links to next/previous pages. Honor pageSize and use provided next links. Monitor HTTP 429 responses and implement exponential backoff.

Environment and go-live

Test/demo and production use different base URLs and credentials. Subscribe and pass certification (required for XPress Booking) before go-live; request production access via SUPPORT and obtain production keys and tokens.

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