Event-driven Dataset
An event-driven dataset is designed for questions like “What changed?” and “When did it change?” Instead of storing only regular time intervals, it stores updates when an event progresses. That makes the dataset feel more like a history of states than a simple price chart.
Most event-driven datasets start with a core event record. That record includes a title, category, key timestamps, and the rules that define what will count as the final outcome. As the event evolves, the dataset adds updates, such as trading status changes, clarifications, or additional milestones.
A strong event-driven dataset also preserves identifiers so you can connect all updates back to the same event. This is what makes it possible to join event metadata with related series like prices, probabilities, or volume. Without consistent IDs, you end up with disconnected fragments.
Event-driven datasets are often used for research because they support clean comparisons across many events. You can align data by lifecycle stage, measure how expectations moved, and study what happens before and after resolution. They’re also useful for building products because they map well to how applications work: an event page is a single object that updates over time.
From a practical standpoint, the format matters too. Some teams prefer APIs for flexible querying, while others prefer bulk files for large-scale historical analysis. An event-driven dataset can support both approaches as long as it keeps timestamps, identifiers, and update history consistent.
Why It Matters
Event-driven datasets make it easier to analyze and build around real-world milestones. They reduce noise and create a clearer picture of how markets or expectations evolve from creation to resolution.
What fields are commonly found in an event-driven dataset?
You usually see identifiers, titles, categories, and multiple timestamps (creation, scheduled time, resolution). Many datasets include status fields that describe the lifecycle stage. If the dataset supports outcomes, it may include outcome definitions and final result fields. Clear versioning or update timestamps help track changes reliably.
How is an event-driven dataset different from a traditional time-series dataset?
Time-series datasets are organized around regular intervals, like 1-minute bars or daily closes. Event-driven datasets are organized around state changes and milestones, so updates can be irregular. That structure is better for capturing clarifications, status transitions, and final outcomes. It also makes it easier to link numeric series to meaningful event context.
When should you use bulk files instead of an API for event-driven datasets?
Bulk files are often better when you need to analyze a lot of history, run large backtests, or keep a local archive. They can be faster and cheaper to ingest at scale than repeated API calls. APIs are better for targeted queries and real-time applications. Many teams use bulk for history and API calls for incremental updates.
Real-World Example
A data scientist downloads a year of prediction market histories and uses the event-driven dataset to align every market by “days to resolution.” They then study how often probabilities drift versus jump after new information.
Event-driven dataset and Flat Files S3 API
FinFeedAPI’s Flat Files S3 API is relevant when you want an event-driven dataset in bulk form for research and backtesting. Bulk delivery makes it easier to ingest large historical periods, rerun analyses, and build reproducible pipelines. It’s a practical choice when your goal is analysis at scale rather than one-off queries.