Systems Wake on Change
The viewer learns that event-driven systems respond to meaningful state changes, turning a change into an immediate action.
Layer 7: Event & Flow Layer turns meaningful state changes into immediate action. By the end, you'll know: event triggers, flow routing, and state-driven responses. You open an app and a notification appears. No button press. No refresh. That is the first clue in this layer: systems do not always wait for you to ask. They can react when something changes. So the question is not just what the system did, but what happened before it. In event-driven behavior, a change is the trigger, and the reaction is built to follow it immediately. Now let’s pin down what counts as an event. In this layer, an event is a meaningful change in state. A file is saved. A message arrives. A payment succeeds. The system notices that the state is different from before. You can think of it as the moment the system has new information. The important part is not the noise around it. It is the change itself. If nothing changed, there is no event to react to. Notice how broad this is. A button click in the UI is an event. A database record updating is an event. A server error is an event. Different systems, same pattern: something moved from one state to another. That leads to a useful prediction. If you know what state changed, you can often predict what should happen next. A new order should enter processing. A failed login should raise an alert. The event carries the signal. And that is why events are so practical. They are not abstract labels. They are the system’s way of noticing that reality has changed and deciding that the old behavior no longer fits. Once you see the change, the chain becomes simple. State changes. The system recognizes an event. Then a reaction starts. The reaction is not delayed until someone checks manually. It is tied to the event itself. That matters because the reaction can be immediate and specific. If inventory drops to zero, the system can stop selling the item. If a user uploads a file, the system can begin validation right away. The event points to the action. So when you ask what caused the response, the answer is usually not a person watching the screen. It is the event pipeline: a change occurred, the system registered it, and the next step began automatically.
From Event to Flow
The viewer learns how one event can launch a multi-step flow and why webhooks and polling represent different ways of detecting and sharing change.
Now we move from a single reaction to a flow. One event rarely stops at one action. A password reset request can trigger email delivery, token creation, logging, and audit tracking. One change starts several downstream steps. This is where the system becomes organized. Each step can feed the next one. The first action creates data, the next action uses that data, and the sequence continues without a human stitching it together in real time. If you were predicting the outcome, you would not ask only, “What happens now?” You would ask, “What else depends on this event?” That is the idea of flow: one trigger, multiple connected reactions. So far, the reactions have stayed inside one system. Webhooks extend the same idea outward. When one system has an event, it can send an HTTP request to another system and say, in effect, something happened here. That second system does not need to keep asking for updates. It receives the notification when the event occurs. A payment platform can notify a shipping service. A form tool can notify a CRM. The connection is event-based, not manual. Identify the components in that exchange: the source system detects the event, the webhook delivers the message, and the receiving system runs its own response. That is system-to-system awareness built on change. Now compare that with polling. Polling means checking again and again: did anything change yet? did anything change yet? The system spends work on repeated questions even when the answer is still no. Event-driven behavior works differently. The system stays quiet until the change actually happens, then it reacts. That is usually more efficient, because effort is spent on real updates instead of constant checking. Apply that to a new situation. If you are monitoring a queue for incoming jobs, polling makes sense only if you cannot receive notifications. But if the system can emit events, you would rather listen than keep asking. The event arrives when it matters. The deeper point is timing. Polling is a schedule. Events are a signal. One asks on a rhythm. The other responds to reality. For reactive systems, that difference is not cosmetic. It changes cost, latency, and design. So if you need a quick test, ask yourself: am I checking for change, or am I being told when change happens? That question separates a polling design from an event-driven one.
