Primis Google Ad Manager Python API Docs | dltHub

For REST API access in Primis Google Ad Manager, refer to the Primis Platform API documentation for integration details. The Google Ad Manager API documentation provides setup and client library guidance. Use OAuth 2.0 for authentication. The REST API base URL is https://console.primis.tech/UI/php/responders/apiResponder.php and all Primis requests require a daily‑renewed token obtained via an authentication GET call.

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 Primis Google Ad Manager data in under 10 minutes.

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What data can I load from Primis Google Ad Manager?

Here are some of the endpoints you can load from Primis Google Ad Manager:

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How do I authenticate with the Primis Google Ad Manager API?

Primis Platform API requires an initial authentication GET request with apiUserName and apiUserCode that returns a JSON token; all other requests must include this token as a token query or form parameter.

1. Get your credentials

1. Contact your Primis account manager to receive your apiUserName and apiUserCode.
2. Issue a GET request to the authentication endpoint: curl "https://console.primis.tech/UI/php/responders/apiResponder.php?method=authentication&apiUserName=[API_USER_NAME]&apiUserCode=[API_USER_CODE]&version=2.19"
3. The response will be JSON { "token": "XXXXXXXXXXXX" }. Store this token and use it as the token parameter for all subsequent API calls (the token is valid for ~24 hours).

2. Add them to .dlt/secrets.toml

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[sources.primis_google_ad_manager_source]
token = "YOUR_PRIMIS_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 Primis Google Ad Manager 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 primis_google_ad_manager_pipeline.py

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

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Pipeline primis_google_ad_manager_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset primis_google_ad_manager_data
The duckdb destination used duckdb:/primis_google_ad_manager.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 primis_google_ad_manager_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 authentication and publisher_report from the Primis Google Ad Manager 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 primis_google_ad_manager_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://console.primis.tech/UI/php/responders/apiResponder.php",
            "auth": {
                "type": "api_key",
                "token": token,
            },
        },
        "resources": [
            {"name": "authentication", "endpoint": {"path": "UI/php/responders/apiResponder.php?method=authentication&apiUserName=[API_USER_NAME]&apiUserCode=[API_USER_CODE]&version=2.19", "data_selector": "token"}},
            {"name": "publisher_report", "endpoint": {"path": "UI/php/responders/apiResponder.php (method=publisherReport)"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="primis_google_ad_manager_pipeline",
        destination="duckdb",
        dataset_name="primis_google_ad_manager_data",
    )
    load_info = pipeline.run(primis_google_ad_manager_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("primis_google_ad_manager_pipeline").dataset()
sessions_df = data.publisher_report.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM primis_google_ad_manager_data.publisher_report LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("primis_google_ad_manager_pipeline").dataset()
data.publisher_report.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 Primis Google Ad Manager 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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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