Emarsys Python API Docs | dltHub

Emarsys is a cloud‑based marketing automation platform that offers a REST API for managing contacts, campaigns, and other marketing resources. The REST API base URL is https://api.emarsys.net/api/v3 and All requests require a Bearer JWT token obtained via OAuth 2.0 client‑credentials flow..

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 Emarsys data in under 10 minutes.

---

What data can I load from Emarsys?

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

---

How do I authenticate with the Emarsys API?

Obtain a JWT by POSTing to https://auth.emarsys.net/oauth2/token with HTTP Basic authentication (client_id:client_secret). Include the JWT in every API request with the header Authorization: Bearer <JWT>.

1. Get your credentials

1. Log in to the Emarsys UI.
2. Navigate to Account → API → OAuth Clients.
3. Click Create New Client.
4. Provide a name and optional redirect URI.
5. Save the client; the generated Client ID and Client Secret will be displayed. Copy these values for use in dlt configuration.

2. Add them to .dlt/secrets.toml

Copy
[sources.emarsys_source]
client_id = "your_client_id"
client_secret = "your_client_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Emarsys 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:

Copy
python emarsys_pipeline.py

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

Copy
Pipeline emarsys_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset emarsys_data
The duckdb destination used duckdb:/emarsys.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline emarsys_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.

---

Python pipeline example

This example loads contact and campaign from the Emarsys API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def emarsys_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.emarsys.net/api/v3",
            "auth": {
                "type": "bearer",
                "token": client_id,
            },
        },
        "resources": [
            {"name": "contact", "endpoint": {"path": "contact", "data_selector": "data"}},
            {"name": "campaign", "endpoint": {"path": "campaign", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="emarsys_pipeline",
        destination="duckdb",
        dataset_name="emarsys_data",
    )
    load_info = pipeline.run(emarsys_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.

---

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):

Copy
import dlt

data = dlt.pipeline("emarsys_pipeline").dataset()
sessions_df = data.contact.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM emarsys_data.contact LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("emarsys_pipeline").dataset()
data.contact.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Emarsys 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.

---

Troubleshooting

Authentication failures

• If the JWT is missing or malformed, the API returns HTTP 401 with a replyCode indicating authentication error.
• Ensure the client_id and client_secret are correct and that the token has not expired.

Rate limits

• Each API key is limited to 1000 requests per minute.
• Exceeding the limit returns HTTP 429 with headers X-RateLimit-Limit, X-Ratelimit-Remaining, and X-RateLimit-Reset.
• Token fetches are limited to 50 requests per minute per client_id and 200 per hour.

Error response format

• Errors are returned as JSON objects with fields replyCode (integer), replyText (string), and data (may be empty). Example:

Copy
{ "replyCode": 6026, "replyText": "No such response type", "data": "" }

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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