Emails Python API Docs | dltHub

Mailjet is a transactional and marketing email delivery platform providing REST APIs to send, manage and track emails. The REST API base URL is https://api.mailjet.com/v3 and https://api.mailjet.com/v3.1 and all requests use HTTP Basic Auth with API key as username and API secret as 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 Emails data in under 10 minutes.

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What data can I load from Emails?

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

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How do I authenticate with the Emails API?

Mailjet authenticates every Email API request using HTTPS Basic Auth. Provide your API Key as the HTTP Basic username and your API Secret Key as the password (e.g., curl --user "$MJ_APIKEY_PUBLIC:$MJ_APIKEY_PRIVATE").

1. Get your credentials

1. Sign in to Mailjet dashboard. 2) Open Account > API Keys (or visit https://app.mailjet.com/account/api_keys). 3) Create or copy the two keys shown: API Key (public) and API Secret Key (private). Use them for HTTP Basic auth.

2. Add them to .dlt/secrets.toml

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[sources.emails_source]
api_key = "your_public_api_key"
api_secret = "your_private_api_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.

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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 Emails 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 emails_pipeline.py

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

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Pipeline emails_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset emails_data
The duckdb destination used duckdb:/emails.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 emails_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 message and contact from the Emails 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 emails_source(api_key, api_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mailjet.com/v3 and https://api.mailjet.com/v3.1",
            "auth": {
                "type": "http_basic",
                "api_key (username) and api_secret (password)": api_key, api_secret,
            },
        },
        "resources": [
            {"name": "message", "endpoint": {"path": "v3/REST/message", "data_selector": "Data"}},
            {"name": "contact", "endpoint": {"path": "v3/REST/contact", "data_selector": "Data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="emails_pipeline",
        destination="duckdb",
        dataset_name="emails_data",
    )
    load_info = pipeline.run(emails_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("emails_pipeline").dataset()
sessions_df = data.message.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM emails_data.message LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("emails_pipeline").dataset()
data.message.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 Emails 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 get 401 Unauthorized, verify you are using HTTP Basic Auth with your API Key as username and API Secret as password. Check for accidental URL version mismatches (v3 vs v3.1).

Rate limiting and HTTP errors

Mailjet uses standard HTTP status codes. Respect 4xx for client errors and 5xx for server errors. If you encounter 429, implement exponential backoff and retries.

Pagination and data selector

Most REST GET endpoints return a JSON envelope with Count and Data fields; the list of records is in the Data array. Use the returned Count and typical offset/limit query parameters to paginate.

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