Mailjet Python API Docs | dltHub

Mailjet is a transactional and marketing email platform that exposes a REST API to send emails, manage contacts, lists, templates, campaigns and retrieve statistics. The REST API base URL is https://api.mailjet.com/v3 and all requests require HTTP Basic auth using API Key (username) and API Secret Key (password).

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 Mailjet data in under 10 minutes.

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What data can I load from Mailjet?

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

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How do I authenticate with the Mailjet API?

Mailjet uses HTTP Basic Authentication. Use your API Key as the username and API Secret Key as the password on every request (HTTPS required). No additional headers besides standard JSON Accept/Content-Type are required.

1. Get your credentials

1. Sign in to the Mailjet dashboard. 2) Navigate to Account > API Key Management or open https://app.mailjet.com/account/api_keys. 3) If needed, click "Create a new API Key". 4) The page will display the API Key (username) and API Secret Key (password). 5) Copy both values and store them securely; use the API Key as the username and the API Secret as the password for HTTP Basic Auth.

2. Add them to .dlt/secrets.toml

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[sources.mailjet_source]
api_key = "your_mailjet_api_key_here"
api_secret = "your_mailjet_api_secret_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 Mailjet 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 mailjet_pipeline.py

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

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Pipeline mailjet_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mailjet_data
The duckdb destination used duckdb:/mailjet.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 mailjet_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 contacts and messages from the Mailjet 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 mailjet_source(api_key, api_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mailjet.com/v3",
            "auth": {
                "type": "http_basic",
                "api_secret": api_key, api_secret,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "contact", "data_selector": "Data"}},
            {"name": "messages", "endpoint": {"path": "message", "data_selector": "Data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mailjet_pipeline",
        destination="duckdb",
        dataset_name="mailjet_data",
    )
    load_info = pipeline.run(mailjet_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("mailjet_pipeline").dataset()
sessions_df = data.contacts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mailjet_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mailjet_pipeline").dataset()
data.contacts.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 Mailjet 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, verify you are using HTTP Basic Auth with API Key as username and API Secret Key as password. Ensure credentials are from https://app.mailjet.com/account/api_keys and not the dashboard user password.

Rate limits and status codes

The API uses standard HTTP status codes. Exceeding rate limits or malformed requests will return 4xx errors. Check Mailjet Status (https://mailjet.statuspage.io/) for platform outages.

Pagination

List endpoints return Count, Total and Data array. Use limit and offset query parameters where supported to paginate through Data results.

Common error responses

Errors are returned as JSON with HTTP status codes. On auth errors you'll get 401; on not found 404; on validation 400. Inspect the JSON error body for details.

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