Tive Python API Docs | dltHub

Tive is a logistics IoT platform that provides APIs to access device, shipment, location and sensor telemetry and manage trackers. The REST API base URL is https://api.tive.com/public/v3 and all requests require a Bearer access token obtained from POST /public/v3/authenticate.

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 Tive data in under 10 minutes.

---

What data can I load from Tive?

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

---

How do I authenticate with the Tive API?

Tive uses client credentials to obtain an access token via POST /public/v3/authenticate. Include the returned Bearer token in the Authorization header for subsequent requests (Authorization: Bearer ).

1. Get your credentials

1. Log in to the Tive dashboard (contact Tive support if you lack access). 2) In the developer/API or account settings, create an API client to obtain a client id and client secret. 3) Call POST https://api.tive.com/public/v3/authenticate with the client id/secret to receive an access token. 4) Use the token in Authorization: Bearer for API calls.

2. Add them to .dlt/secrets.toml

Copy
[sources.tive_source]
client_id = "your_client_id"
client_secret = "your_client_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Tive 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:

Copy
python tive_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline tive_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.

---

Python pipeline example

This example loads devices and shipments from the Tive API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def tive_source(client_credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.tive.com/public/v3",
            "auth": {
                "type": "bearer",
                "token": client_credentials,
            },
        },
        "resources": [
            {"name": "devices", "endpoint": {"path": "Devices", "data_selector": "data"}},
            {"name": "shipments", "endpoint": {"path": "Shipments", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tive_pipeline",
        destination="duckdb",
        dataset_name="tive_data",
    )
    load_info = pipeline.run(tive_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.

---

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):

Copy
import dlt

data = dlt.pipeline("tive_pipeline").dataset()
sessions_df = data.devices.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM tive_data.devices LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("tive_pipeline").dataset()
data.devices.df().head()

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

---

What destinations can I load Tive 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.

---

Troubleshooting

Authentication failures

If POST /public/v3/authenticate fails, verify client_id and client_secret and that the client is enabled. The API returns standard HTTP error codes; re-run authentication to obtain a fresh Bearer token and ensure Authorization: Bearer is present on all requests.

Pagination and data selector

List endpoints return pagination metadata (totalRecords, pageNumber, pageSize) and the records array under the data key. Use pageNumber and pageSize query parameters (max 50 per page for Devices) to iterate pages.

Rate limiting and errors

The public docs do not publish explicit rate limits; handle 429 responses by backing off and retrying. Standard error responses follow HTTP status codes with JSON error bodies. For 4xx/5xx responses, inspect the response body for error details and message fields.

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

---

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.

Copy
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