Jeeng Python API Docs | dltHub

Jeeng is a marketing automation platform that provides APIs for advertising campaign management and reporting. The REST API base URL is https://powerinbox.azure-api.net/v and All requests require a Bearer token obtained through Microsoft OAuth 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 Jeeng data in under 10 minutes.

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What data can I load from Jeeng?

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

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How do I authenticate with the Jeeng API?

Obtain an access token from the Microsoft OAuth token endpoint using your client ID and client secret, then include it in each request's Authorization header as Bearer <access_token>.

1. Get your credentials

1. Contact your Jeeng account manager to have an API account provisioned. 2. Receive a client ID (application ID) and client secret. 3. Send a POST request to https://login.microsoftonline.com/revenuestripe.onmicrosoft.com/oauth2/v2.0/token with grant_type=client_credentials, client_id, client_secret, and scope as required. 4. Extract the access_token from the JSON response. 5. Use this token in the Authorization: Bearer <access_token> header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.jeeng_source]
access_token = "YOUR_ACCESS_TOKEN"

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 Jeeng 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 jeeng_pipeline.py

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

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Pipeline jeeng_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset jeeng_data
The duckdb destination used duckdb:/jeeng.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 jeeng_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 reporting_campaigns and reporting_placements from the Jeeng 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 jeeng_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://powerinbox.azure-api.net/v",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "reporting_campaigns", "endpoint": {"path": "reporting/campaigns", "data_selector": "campaigns"}},
            {"name": "reporting_placements", "endpoint": {"path": "reporting/placements", "data_selector": "placements"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="jeeng_pipeline",
        destination="duckdb",
        dataset_name="jeeng_data",
    )
    load_info = pipeline.run(jeeng_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("jeeng_pipeline").dataset()
sessions_df = data.reporting_campaigns.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM jeeng_data.reporting_campaigns LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("jeeng_pipeline").dataset()
data.reporting_campaigns.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 Jeeng 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 errors

If you receive a 401 Unauthorized response, verify that your client ID and secret are correct and that the access token has not expired. Request a new token using the OAuth client credentials flow.

Rate limiting

The API may return a 429 Too Many Requests status when you exceed the allowed call frequency. Respect the Retry-After header and implement exponential backoff.

Pagination quirks

Some list endpoints paginate results using page and pageSize query parameters. Check the response for a nextPageToken or similar field and continue fetching until no token is returned.

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