Certopus Python API Docs | dltHub

Certopus is a platform to create and manage verifiable digital credentials (certificates and badges) for courses, events and training programs. The REST API base URL is https://api.certopus.com/v1 and All requests require an API key (from your Certopus account) sent in request headers..

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 Certopus data in under 10 minutes.

---

What data can I load from Certopus?

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

---

How do I authenticate with the Certopus API?

Certopus uses an API key (found in your Certopus account profile) to authenticate requests. Include the API key in request headers; the docs show standard JSON headers (Accept: application/json, Content-Type: application/json). Confirm whether the key must be sent as an Authorization: Bearer header or a provider‑specific header (e.g., x‑api‑key).

1. Get your credentials

1. Log in to your Certopus account at https://app.certopus.com.
2. Open your profile from the top‑right corner.
3. Locate the API key value in the profile section.
4. Copy the key and keep it secure; you may regenerate it from the profile if needed.

2. Add them to .dlt/secrets.toml

Copy
[sources.certopus_source]
api_key = "your_certopus_api_key_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.

---

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 Certopus 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 certopus_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline certopus_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 certificates and templates from the Certopus 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 certopus_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.certopus.com/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "certificates", "endpoint": {"path": "v1/certificates"}},
            {"name": "templates", "endpoint": {"path": "v1/templates"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="certopus_pipeline",
        destination="duckdb",
        dataset_name="certopus_data",
    )
    load_info = pipeline.run(certopus_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("certopus_pipeline").dataset()
sessions_df = data.certificates.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM certopus_data.certificates LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("certopus_pipeline").dataset()
data.certificates.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 Certopus 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 you receive 401/403 responses, verify the API key in your profile and ensure it is sent in request headers. Check whether the API expects 'Authorization: Bearer ' or a provider‑specific header (e.g., 'x-api-key').

Missing or empty data in GET responses

If list endpoints return unexpected structures, inspect the OpenAPI UI at https://api.certopus.com/ or call the endpoint with a valid key and examine the JSON to find the exact key that contains the records array.

Rate limiting and errors

The public docs do not document rate limits; handle 429 responses by backing off and retrying with exponential backoff. For 5xx errors, retry with backoff and contact Certopus support if persistent.

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