TinyEmail Python API Docs | dltHub

TinyEmail is an email marketing platform and REST API for managing audiences, campaigns, senders, templates and reporting. The REST API base URL is https://api.tinyemail.com/v1 and all requests require an API key sent in the X-API-KEY header.

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 TinyEmail data in under 10 minutes.

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What data can I load from TinyEmail?

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

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How do I authenticate with the TinyEmail API?

API uses an API key passed in request headers. Requests require the API key in the X-API-KEY header (apiKey security scheme). Some docs reference an api_key field and instruct contacting support to obtain a key for Enterprise accounts.

1. Get your credentials

1. Log into your TinyEmail account (Enterprise access required for full tinyEmail API). 2) If your plan does not show API credentials, contact TinyEmail support (support@tinyemail.com or chat) to request an API key. 3) Copy the provided API key value to use in requests.

2. Add them to .dlt/secrets.toml

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[sources.tiny_email_source]
api_key = "your_tinyemail_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.

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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 TinyEmail 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 tiny_email_pipeline.py

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

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Pipeline tiny_email_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset tiny_email_data
The duckdb destination used duckdb:/tiny_email.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 tiny_email_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 campaigns and audiences from the TinyEmail 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 tiny_email_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.tinyemail.com/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "campaigns", "endpoint": {"path": "campaign", "data_selector": "campaigns.content"}},
            {"name": "audiences", "endpoint": {"path": "audience", "data_selector": "audience.content"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tiny_email_pipeline",
        destination="duckdb",
        dataset_name="tiny_email_data",
    )
    load_info = pipeline.run(tiny_email_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("tiny_email_pipeline").dataset()
sessions_df = data.campaigns.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM tiny_email_data.campaigns LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("tiny_email_pipeline").dataset()
data.campaigns.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 TinyEmail 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 or "API key is missing or invalid" errors, verify the X-API-KEY header is present and the key value is correct. Ensure your account plan includes access to the tinyEmail API (Enterprise access may be required) and request a key from support if you don't have one.

Pagination and data selectors

List endpoints are paginated and responses typically wrap results inside an object with a content array (e.g., campaigns.content). Check the response for fields like totalPages, totalElements, number, size and last. Use the content array as the records selector.

Rate limits and errors

The API returns 4xx errors for bad requests (400), unauthorized (401), method not allowed (405) and 5xx on server errors. Error responses commonly use a JSON object with message field: {"message":"Some error message"}.

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