Ad Performance Stack

### Optimizing Ad Performance with Facebook Ads and Google Ads Data.

Integrate and analyze Facebook Ads and Google Ads data using Airbyte and dbt to optimize ad spend and improve campaign ROI.

How to get started:

- Check out the [README](https://github.com/airbytehq/quickstarts/tree/main) and the [Contribution instructions](https://github.com/airbytehq/quickstarts/blob/main/CONTRIBUTING.md) in this repo.
- Check one of the [existing quickstarts](https://github.com/airbytehq/quickstarts#list-of-available-quickstarts) and observe their structure.
- (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with [E-commerce analytics in BigQuery](https://github.com/airbytehq/quickstarts/tree/main/ecommerce_analytics_bigquery)

Read the repository README and CONTRIBUTING.md, then compare the existing quickstarts, especially E-commerce analytics in BigQuery. Define the new quickstart's structure for Facebook Ads and Google Ads data with Airbyte and dbt, and consider it done when its README and implementation demonstrate the intended ad-spend and campaign-ROI analysis.

Written by the indexing model from the issue text.