Engagevia Python API Docs | dltHub

Engagevia (Mapp Engage) is a REST API platform for managing and sending customer engagement messages (email, push, SMS), tracking events such as abandoned carts and wishlist actions, and managing audience groups. The REST API base URL is https://{subdomain}.yourdomain.com/api/rest/v19 and all requests require HTTP Basic authentication (Authorization: Basic ...).

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 Engagevia data in under 10 minutes.

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What data can I load from Engagevia?

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

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How do I authenticate with the Engagevia API?

The REST API uses HTTP Basic authentication. Each request must include an Authorization header containing Basic <base64(username:password)> where the username/password are credentials for an API or Hybrid system user. Required headers also include Host, Accept (application/json or application/xml), and Content-Type (application/json or application/xml).

1. Get your credentials

1. In the Mapp (Engagevia) platform, open Administration > Users (or API Users).
2. Create or locate a system user with role API or Hybrid and set a secure password.
3. Use that user's email (username) and password to construct the Basic auth header: base64("username:password").

2. Add them to .dlt/secrets.toml

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[sources.engagevia_source]
api_user_credentials = "username:password"

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 Engagevia 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 engagevia_pipeline.py

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

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Pipeline engagevia_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset engagevia_data
The duckdb destination used duckdb:/engagevia.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 engagevia_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 messages and groups from the Engagevia 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 engagevia_source(api_user_credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.yourdomain.com/api/rest/v19",
            "auth": {
                "type": "http_basic",
                "password": api_user_credentials,
            },
        },
        "resources": [
            {"name": "messages", "endpoint": {"path": "{integrationId}/message"}},
            {"name": "groups", "endpoint": {"path": "{integrationId}/group"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="engagevia_pipeline",
        destination="duckdb",
        dataset_name="engagevia_data",
    )
    load_info = pipeline.run(engagevia_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("engagevia_pipeline").dataset()
sessions_df = data.messages.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM engagevia_data.messages LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("engagevia_pipeline").dataset()
data.messages.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 Engagevia 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

Ensure you use an API or Hybrid system user. Construct Authorization: Basic base64(username:password). The API requires the Accept and Content-Type headers (application/json). 401/403 responses indicate invalid credentials or insufficient permissions.

Pagination and empty responses

Some responses may return 204 when the call succeeds but no data is returned. Check HTTP 200 for responses with body. The docs do not define a uniform list-key—inspect the specific endpoint response for the key that contains the records; if the body is empty, the API may use top‑level objects rather than an array.

Rate limits and error codes

The API surface uses standard HTTP response codes: 200 (OK with body), 204 (Success, no body), 400 (Bad Request), 401 (Unauthorized), 403 (Forbidden), 404 (Not Found), 429 (Too Many Requests / rate limiting), 500 (Server Error). Retry on 429 with exponential backoff and on intermittent 5xx errors.

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