The change is easy to miss and large in practice: rather than configuring an extraction pipeline with triggers and scheduling, you declare a materialised view and the system maintains it. That collapses work most data teams treat as unavoidable, since a pipeline is a transformation plus the orchestration around it, and most of that orchestration exists to keep a derived dataset current. The performance argument comes with both a number and a mechanism, which is rarer than it should be — around twenty per cent reduced cost, driven by local disk, where exhaustion fails jobs and constraint slows them with stragglers holding up everything else. That contradicts where teams look first, since disk exhaustion presents as symptoms shared with many other problems. The most honest passage concerns engine upgrades, which got harder precisely because the data lake features teams want depend on the newest releases.