Constant contact Python API Docs | dltHub

Constant Contact is an email marketing platform providing RESTful APIs to manage contacts, lists, email campaigns, account settings, bulk activities, and webhooks. The REST API base URL is https://api.cc.email/v3 and all requests require OAuth2 access tokens (Bearer).

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 Constant contact data in under 10 minutes.

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What data can I load from Constant contact?

Here are some of the endpoints you can load from Constant contact:

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How do I authenticate with the Constant contact API?

The V3 API uses OAuth 2.0. Obtain an access token via the Authorization Code (server), PKCE, or Client Credentials flows; include the token in the Authorization header: Authorization: Bearer {access_token}. TLS v1.2 or higher is required.

1. Get your credentials

1. Create or log into a developer account at https://developer.constantcontact.com and register a new application. 2) In the app settings, note the client_id and client_secret (if applicable) and configure redirect URIs. 3) Implement OAuth2 flow (Authorization Code, PKCE, or Client Credentials) to request scopes and receive an authorization code. 4) Exchange the authorization code for an access_token (and refresh_token for server flows). 5) Use the access_token in Authorization: Bearer {access_token}; refresh using the refresh_token as needed.

2. Add them to .dlt/secrets.toml

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[sources.constant_contact_source]
access_token = "your_oauth2_access_token_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 Constant contact 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 constant_contact_pipeline.py

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

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Pipeline constant_contact_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset constant_contact_data
The duckdb destination used duckdb:/constant_contact.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 constant_contact_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 contact_lists from the Constant contact 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 constant_contact_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cc.email/v3",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "contacts"}},
            {"name": "contact_lists", "endpoint": {"path": "contact_lists", "data_selector": "contact_lists"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM constant_contact_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("constant_contact_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 Constant contact 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 the Authorization header is present and the access_token is valid and unexpired. Ensure your token was obtained with correct scopes and the app has required privileges.

Rate limits

Status 429 indicates rate limiting. Implement exponential backoff and retry; batch operations using bulk endpoints where available.

Pagination and collections

Many collection endpoints return a JSON object with a top-level array field (e.g., "contacts", "contact_lists", "emails", "activities") plus pagination links/next. Respect the "next" link or paging query parameters to retrieve additional pages.

Common HTTP errors

400 Bad Request — malformed JSON or validation error. 403 Forbidden — missing scopes or insufficient user privileges. 404 Not Found — resource does not exist. 500/503 — server errors; retry with backoff.

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