Event-driven data
Event-driven data is organized around the idea that the update itself is the signal: something changed, and that change matters.
In finance, event-driven updates often appear around milestones such as earnings releases, rate decisions, corporate actions, or prediction market questions. Updates can include context beyond numbers—updated text, status, new timestamps, clarified rules, trading state, and final outcomes.
Because updates are irregular, stable identifiers (for the event/market) and reliable timestamps are essential. They let you link each update to the right object and reconstruct history accurately.
In practice, developers commonly:
- Maintain a latest-state snapshot for fast reads (dashboards, APIs)
- Append an immutable update log for auditability and replay
- Trigger downstream actions only when relevant fields change
This avoids repeated ingestion of identical data and focuses compute on moments that move the event forward.
Why It Matters
Event-driven data enables fast reaction to meaningful changes while reducing noise. In many workflows, timing and status can be as important as the numeric values.
How it’s used in financial apps
Used for alerts, workflow automation, and monitoring. Examples:
- Notify when an event is created
- Alert when trading opens/closes or rules are clarified
- Record and display when an outcome is finalized
Because updates are discrete, they’re also easier to audit.
How it differs from time-series data
Time-series data is sampled or delivered at regular intervals. Event-driven updates occur on state changes, so spacing is irregular and metadata/status fields are often central.
Real-World Example
A developer tracks prediction market events and updates only when a market is created, rules are clarified, or the market resolves. The app refreshes a single event page per update instead of reloading everything on a timer.
Event-driven data and Prediction Market API
FinFeedAPI’s Prediction Market API fits event-driven data workflows by letting you pull event metadata, monitor state changes, and capture meaningful updates for dashboards, alerts, and backtests.