Notify Python API Docs | dltHub

Notify is a messaging API that enables sending notifications to customers via various channels. The REST API base URL is https://api.notify.eu and All requests require X-ClientId and X-SecretKey headers for authentication..

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 Notify data in under 10 minutes.

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What data can I load from Notify?

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

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How do I authenticate with the Notify API?

Authentication is performed via two custom HTTP headers: X-ClientId containing your client identifier and X-SecretKey containing your secret key.

1. Get your credentials

1. Sign in to the Notify dashboard.
2. Navigate to the API credentials section.
3. After the first sign‑in you will be shown a client id and a secret key.
4. Store these values securely; the secret key cannot be viewed again.
5. To create additional keys, use the "Generate new key" button in the same section.

2. Add them to .dlt/secrets.toml

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[sources.notify_source]
client_id = "your_client_id_here"
secret_key = "your_secret_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.

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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 Notify 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 notify_pipeline.py

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

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Pipeline notify_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset notify_data
The duckdb destination used duckdb:/notify.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 notify_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 notification_send and UNKNOWN_GET_1 from the Notify 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 notify_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.notify.eu",
            "auth": {
                "type": "api_key",
                "api_key": client_id,
            },
        },
        "resources": [
            {"name": "notification_send", "endpoint": {"path": "notification/send"}},
            {"name": "unknown_get", "endpoint": {"path": "unknown/get"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="notify_pipeline",
        destination="duckdb",
        dataset_name="notify_data",
    )
    load_info = pipeline.run(notify_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("notify_pipeline").dataset()
sessions_df = data.notification_send.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM notify_data.notification_send LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("notify_pipeline").dataset()
data.notification_send.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 Notify 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 the X-ClientId or X-SecretKey headers are missing or invalid, the API returns a JSON error such as:

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{ "success": false, "message": "customer not found" }

Verify that the client id and secret key are correct and that they are sent exactly as shown in the header table.

General request errors

Any malformed request or missing required fields will also return a JSON object with success: false and a descriptive message. Check the request payload against the API specification and ensure all required parameters are included.

Rate limiting

The documentation does not explicitly mention rate limits; if you encounter HTTP 429 responses, implement exponential back‑off and retry logic.

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