Using a data warehouse as a middleman for all point-to-point integration workflows can offer significant benefits in terms of quality and reliability. This approach, known as the hub-and-spoke model, involves using the data warehouse to transform and load data before sending it to downstream systems, rather than performing transformations on the fly as data moves from one place to another. By doing so, data teams can avoid the complexity and challenges associated with traditional point-to-point integrations, such as limited customization options and brittle architecture. The use of a data warehouse also enables more efficient and reliable data pipelines, with benefits including improved debuggability, metric consistency, and governance. Additionally, this approach allows for greater control over data and the ability to use all customer data, regardless of its origin. While there may be some latency involved, most operational use cases do not require sub-minute latency, and the benefits in quality and reliability far outweigh any potential speed difference.