Feature Usage Analysis Stack

### Tracking Product Feature Usage with Event Data.

Employ Airbyte to extract event data from product analytics tools, transform the data using dbt, and analyze feature usage to enhance product development.

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 structure of an existing quickstart, especially E-commerce analytics in BigQuery. Define the new quickstart around extracting event data with Airbyte, transforming it with dbt, and analyzing feature usage; done means the documented workflow follows the repository's established quickstart structure.

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