Full Answer

ETL tools are designed for batch extraction. They connect to a data source, query for new or changed records, and load them into a warehouse on a schedule. This architecture works well for databases that store persistent state — order records, customer profiles, product catalogues. WooCommerce's database stores these reliably, and ETL tools extract them accurately.

The gap is everything that happens before and between orders. A visitor arrives from a Google Ad carrying a gclid parameter. They browse three product pages, add two items to cart, remove one, proceed to checkout, abandon, return two days later via organic search, and complete the purchase. The WooCommerce database records the final order. It does not record the browsing sequence, the cart modifications, the abandonment, the original gclid, or the fact that two sessions were the same person.

Event streaming captures this entire sequence in real time. Each action fires as it happens — a server-side event with timestamp, session identifier, attribution parameters, and product data — and flows immediately to BigQuery, GA4, Facebook CAPI, and other destinations. There is no batch delay, no scheduled extraction, and no dependency on the database storing data it was never designed to hold.

The practical consequence is that ETL-only stores have order data but no journey data. They know what customers bought but not how they got there, what they considered, or what attribution path led to the conversion. That journey data is what ad platforms need for optimisation and what AI tools need for prediction.