Dcl logistics Python API Docs | dltHub

DCL Logistics API is a RESTful web service providing programmatic access to DCL Logistics resources (orders, shipments, documents, inventory, customers, batches, etc.) for integration and automation. The REST API base URL is https://api.dclcorp.com/api/v1/ and all requests require HTTP Basic authentication (Authorization: Basic ...) over TLS.

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 Dcl logistics data in under 10 minutes.

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What data can I load from Dcl logistics?

Here are some of the endpoints you can load from Dcl logistics:

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How do I authenticate with the Dcl logistics API?

DCL uses HTTP Basic Authentication over HTTPS. Provide your username and password (API credentials) in the Authorization header as 'Authorization: Basic <base64(username:password)>'.

1. Get your credentials

1. Contact DCL account team or onboarding to request API access.
2. DCL will provision API credentials (username and password) for your account/sandbox.
3. Use the provided username as the 'username' and the provided secret as the 'password' when constructing the Basic Authorization header.
4. For sandbox use the sandbox base URL; for production use the v1 base URL.

2. Add them to .dlt/secrets.toml

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[sources.dcl_logistics_source]
username = "your_api_username"
password = "your_api_password"

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 Dcl logistics 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 dcl_logistics_pipeline.py

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

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Pipeline dcl_logistics_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dcl_logistics_data
The duckdb destination used duckdb:/dcl_logistics.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 dcl_logistics_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 documents and orders from the Dcl logistics 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 dcl_logistics_source(username_password=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.dclcorp.com/api/v1/",
            "auth": {
                "type": "http_basic",
                "password": username_password,
            },
        },
        "resources": [
            {"name": "documents", "endpoint": {"path": "documents?document_type={document_type}&reference1={reference1}&reference2={reference2}&fields={fields}"}},
            {"name": "orders", "endpoint": {"path": "orders", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dcl_logistics_pipeline",
        destination="duckdb",
        dataset_name="dcl_logistics_data",
    )
    load_info = pipeline.run(dcl_logistics_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("dcl_logistics_pipeline").dataset()
sessions_df = data.documents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM dcl_logistics_data.documents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("dcl_logistics_pipeline").dataset()
data.documents.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 Dcl logistics 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 Unauthorized ensure your Authorization header uses HTTP Basic with correct username:password base64‑encoding and you are calling the correct environment (sandbox vs production). DCL requires HTTPS.

Rate limits and 429 responses

If you receive 429 Too Many Requests, back off and retry after a delay. Implement exponential backoff. The API Help pages indicate standard HTTP 4xx responses for client errors.

Pagination quirks

Endpoints that return large result sets are paginated. Check the response for pagination metadata and follow links or page parameters (page/limit) as documented in the API Help UI.

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