End-to-End Engineering Performance Stack

### Optimizing Engineering Performance with GitHub, Datadog, and Sentry.

Integrate and analyze data from GitHub, Datadog, and Sentry using Airbyte and dbt to assess and optimize overall engineering performance, from code quality and collaboration to system health and error resolution.

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 first, then compare the structure of an existing quickstart such as ecommerce_analytics_bigquery. The issue does not name target files or tests; completion would require a documented quickstart integrating the listed data sources and showing how engineering performance is analyzed.

Written by the indexing model from the issue text.