Quora Ads Python API Docs | dltHub

Quora Ads REST API (Reporting API v0 and Conversion API v0) allows users to track conversions and manage ad campaigns. The REST API base URL is https://api.quora.com/ads/v0 and The Reporting API uses OAuth2 with an authorization code flow, while the Conversion API uses a Conversion API token, both requiring a Bearer token for authentication..

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 Quora Ads data in under 10 minutes.

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What data can I load from Quora Ads?

Here are some of the endpoints you can load from Quora Ads:

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How do I authenticate with the Quora Ads API?

The Reporting API uses OAuth2 authorization code flow with the ads_read scope, requiring an Authorization: Bearer <access_token> header. The Conversion API uses a Conversion API token, also sent in the Authorization: Bearer <token> header.

1. Get your credentials

For the Reporting API, implement the OAuth2 authorization code flow to obtain an access token. This involves redirecting the user to Quora's authorization URL, receiving an authorization code, and then exchanging it for an access token at the token URL. The required scope is ads_read. For the Conversion API, generate a Conversion API token directly from the Conversion API page within the Quora Ads Manager. This token needs to be generated only once and does not expire.

2. Add them to .dlt/secrets.toml

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[sources.quora_ads_source]
reporting_api_access_token = "your_reporting_api_access_token_here"
conversion_api_token = "your_conversion_api_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 Quora Ads 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 quora_ads_pipeline.py

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

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Pipeline quora_ads_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset quora_ads_data
The duckdb destination used duckdb:/quora_ads.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 quora_ads_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 accounts and campaigns from the Quora Ads 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 quora_ads_source(reporting_api_access_token, conversion_api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.quora.com/ads/v0",
            "auth": {
                "type": "bearer",
                "token": reporting_api_access_token, conversion_api_token,
            },
        },
        "resources": [
            {"name": "accounts", "endpoint": {"path": "accounts/", "data_selector": "data"}},
            {"name": "campaigns", "endpoint": {"path": "campaigns/{campaign-id}", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="quora_ads_pipeline",
        destination="duckdb",
        dataset_name="quora_ads_data",
    )
    load_info = pipeline.run(quora_ads_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("quora_ads_pipeline").dataset()
sessions_df = data.accounts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM quora_ads_data.accounts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("quora_ads_pipeline").dataset()
data.accounts.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 Quora Ads 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

Rate Limits

For the Reporting API, there is a rate limit of 2000 requests per hour per OAuth client per ad account. Most responses will include an X-Rate-Limit-Remaining header to help monitor usage. The Conversion API has a rate limit of 100 requests per minute per ad account.

Common Errors

• 400 Bad Request: Indicates a validation error in the request.
• 401 Unauthorized: Occurs when the authentication token is missing or invalid.
• 403 Forbidden: Signifies insufficient account permissions for the requested action.
• 404 Not Found: The requested resource could not be found.
• 429 Too Many Requests: Indicates that the rate limit has been exceeded.
• 500 Internal Server Error: A generic server-side error.

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