Imagine a regulator contacts a fintech about a customer complaint. A credit denial, some months back. They want to understand the decision. The engineering team starts pulling on threads. Sometimes reconstruction is possible. Sometimes it is not. Either way, it takes days of engineering time that should have been a query.
Read post →Every production ML system has three well-understood layers. The fourth, the decision plane, is conspicuously absent from most architectures.
A user_id is a key. An entity is a first-class object with history, drift, and temporal context. Infrastructure that cannot distinguish the two is not entity-aware.
Sub-millisecond persistence in a synchronous serving path requires specific architectural choices. Every abstraction has a cost. Here is a precise accounting.
Article 13 uses the word "transparency" twelve times without defining it at the infrastructure level. Here is a technical reading for engineers who need to implement it.
Integrating a decision store into an existing Kafka topology is not a schema problem, it is a semantic problem. The events you persist are not the events you produce.
A precise description of what is in the Community release, what is gated behind Enterprise, and the reasoning behind every boundary decision.