Engagespot Python API Docs | dltHub

Engagespot is a notification delivery platform and API for sending and managing multi-channel notifications (email, push, in-app, SMS) and user notification data. The REST API base URL is US: https://api.engagespot.co/v3 (EU: https://api-eu.engagespot.co/v3) and requests use API key + API secret headers for backend operations; frontend read operations can use API key + user id or a Bearer JWT..

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 Engagespot data in under 10 minutes.

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What data can I load from Engagespot?

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

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How do I authenticate with the Engagespot API?

Backend authentication requires two custom headers: X-ENGAGESPOT-API-KEY and X-ENGAGESPOT-API-SECRET. Frontend (user-scoped) requests may use X-ENGAGESPOT-API-KEY with X-ENGAGESPOT-USER-ID or an Authorization: Bearer header.

1. Get your credentials

1. Sign in to your Engagespot dashboard/console (https://console.engagespot.co). 2) Open your workspace settings or API/keys section. 3) Create or copy an API key and API secret for backend usage. 4) For frontend/user-scoped access either configure a user id header (X-ENGAGESPOT-USER-ID) or create signed JWTs for Authorization: Bearer usage.

2. Add them to .dlt/secrets.toml

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[sources.engagespot_source]
api_key = "YOUR_ENGAGESPOT_API_KEY"
api_secret = "YOUR_ENGAGESPOT_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 Engagespot 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 engagespot_pipeline.py

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

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Pipeline engagespot_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset engagespot_data
The duckdb destination used duckdb:/engagespot.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 engagespot_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 notifications and users from the Engagespot 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 engagespot_source(api_key, api_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "US: https://api.engagespot.co/v3 (EU: https://api-eu.engagespot.co/v3)",
            "auth": {
                "type": "api_key",
                "api_key, api_secret": api_key, api_secret,
            },
        },
        "resources": [
            {"name": "notifications", "endpoint": {"path": "notifications", "data_selector": "data or notifications (depending on endpoint variant)"}},
            {"name": "users", "endpoint": {"path": "users", "data_selector": "users"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="engagespot_pipeline",
        destination="duckdb",
        dataset_name="engagespot_data",
    )
    load_info = pipeline.run(engagespot_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("engagespot_pipeline").dataset()
sessions_df = data.notifications.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM engagespot_data.notifications LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("engagespot_pipeline").dataset()
data.notifications.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 Engagespot 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 both X-ENGAGESPOT-API-KEY and X-ENGAGESPOT-API-SECRET are present and correct for backend requests. For frontend read-only calls provide X-ENGAGESPOT-USER-ID or a valid Bearer JWT. 401 responses indicate missing/invalid credentials.

Rate limits and throttling

Docs do not publish public rate limit numbers; handle 429 responses by backing off and retrying with exponential backoff. Inspect Retry-After headers when present.

Pagination and data selectors

List endpoints frequently return results inside a plural-named top-level key (e.g., "users", "workflows", "templates"). Confirm by calling the GET endpoint and inspect the top-level JSON to use the correct dlt data_selector (empty string if response is a top-level array).

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