Signal
With Datablit, you can create Rule and Signal within minutes and get instant results without the need to write code and manage data pipelines.
Signals are the smallest data units and are expressed in key-value pairs. A key-value pair consists of two related data elements: A key is a constant that defines the data set (e.g., product, gender, price). A value is a variable that belongs to the set (e.g., camera, male, 100) Fully formed, a key value pair could look like this:
| Key | User 1 | User 2 |
|---|---|---|
| Product | Camera | Apparel |
| Gender | Male | Female |
| Price | 10000 | 200 |
| Type | Digital SLR | Sports |
Key: A unique identifier in the key-value pair.
A signal is identified by its unique key and is stored at the user level.
These basic terminologies will help you to create and understand signals in a few clicks.
Aggregation window
An aggregation window defines the time period over which signals are grouped. For example: if you want to count the number of orders in the last 24 hours, the aggregation window is 24 hours and you can use the signal key as order_count_24h.
Freshness
It represents the maximum time difference between an event’s occurrence and its signal being recorded — a measure of how up-to-date your signal is relative to when it was generated or last updated in the source system. Example: A user opens an app at 8:30, 8:40, 9:00, 9:10, 9:30, etc. If you want to compute the total number of sessions in the last hour and evaluate this every hour (at 8:00, 9:00, etc.), the signal’s freshness would be 1 hour. This means the computed session count will be 1 hour.
In short, the aggregation window is about grouping and freshness is about delay.
Signal Types
Based on aggregation window and freshness requirements, you can choose how you want to process signal -
Real time for high freshness(< 1 minute) and instant results - These are ideal for scenarios requiring immediate data updates, making it a solid option for business to personalise at scale and take immediate action. Example: Number of sessions in the last hour
Batch processing works best when you need in depth analysis at set intervals - As the name suggests, used to process data in batches. If you need to aggregate a large number of data points over longer periods and can tolerate a few hours of freshness. Example: Number of orders in the last 1 year with 12-hour freshness
Comparison of Signal Types
| Signal Type | Aggregation Window | Freshness | Computation | Example Use Case |
|---|---|---|---|---|
| Realtime Signal | Short (e.g., 1 hour) | High (< 1 minute) | For each event | Number of sessions in the last hour |
| Batch Signal | Long (e.g., 1 years) | Lower (few hours) | After specific duration (e.g., every 6 hours) | Number of orders in the last 1 years (12-hour freshness) |
Realtime Aggregation Types
It refers to how data points or events are grouped. Realtime signals support three types of aggregation:
- Static: It gives the raw value of the event’s field name (no aggregation)
- Count: The number of times an event occurs
- Sum: Sum a specific field name from events
| Aggregation Type | Signal |
|---|---|
| SUM | total_order_amount |
| STATIC | plan_type |
| COUNT | no._of_order |