Your inventory drops below 50 units, but nobody notices until a customer order fails.
A payment retries for the fifth time, but your team only finds out when the customer complains.
An invoice hits 30 days overdue, but the follow-up email never goes out.
Your systems know when something is wrong. They just can't act on it.
DATA INFRASTRUCTURE - The detection layer that transforms passive data into active triggers.
A condition-based trigger watches your data and fires when specific criteria are met. Inventory below 50 units? Trigger. Payment failed 3 times? Trigger. Lead score above 80? Trigger. You define the threshold, the system does the watching.
Unlike event-based triggers that respond to actions (order placed, email received), condition-based triggers respond to states. The inventory isn't low because something happened - it's low because enough things happened over time. The trigger catches the moment you care about.
The insight: Every business has invisible boundaries - thresholds where normal becomes urgent. Condition-based triggers make those boundaries visible and actionable.
Condition-based triggers solve a universal problem: how do you take action at exactly the right moment when that moment depends on accumulated state rather than a single event?
Define a condition. Continuously evaluate it against current data. Fire exactly once when the condition becomes true. Optionally reset when it becomes false again. This pattern turns passive monitoring into active automation.
Click "Sell 5" to decrement inventory. Watch the condition evaluate on each change. and fire exactly once when quantity drops below the reorder point.
Reorder at: 50 units
Reorder at: 75 units
Reorder at: 20 units
No triggers fired yet. Sell products until inventory drops below the reorder point.
Check periodically, act when true
A scheduled job runs every 5 minutes, queries the database for records matching your condition (inventory < 50), and fires workflows for any matches. Simple, predictable, works with any data source.
React on every row change
A database trigger evaluates your condition on every INSERT or UPDATE. When inventory drops below 50, the trigger fires immediately. No polling delay, no missed transitions.
Evaluate conditions on flowing data
Events flow through a stream processor that maintains running state (count of failed payments). When the count hits 3, it emits a trigger event. Handles high-volume data with complex conditions.
Your best-selling product inventory drops to 45 units. Instead of waiting for a team member to notice (or worse, a failed customer order), the system detects the threshold breach and initiates a purchase order within seconds.
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Inventory is below 50. Your trigger fires. It's still below 50 next check. It fires again. And again. Your team gets 500 emails about the same low inventory alert.
Instead: Track whether you've already fired for this condition. Only fire on the transition from false to true.
Inventory drops to 45, trigger fires, team restocks to 100. Inventory drops to 48 - but the trigger never fires because it already fired once and never reset.
Instead: Define both the trigger condition AND the reset condition. Trigger when < 50, reset when > 75.
Your trigger checks "average order value across all customers this month" on every new order. With 10,000 orders per day, you're running a full table scan 10,000 times.
Instead: Use aggregated metrics updated periodically, not recalculated from scratch on every change.
You've learned how to detect threshold crossings and state changes. The natural next step is understanding how to validate and transform the data that flows from these triggers.