Real-time dataset overview
A real-time dataset is a virtual table structure that serves as the foundation for creating real-time tags through metric mapping. You can define real-time datasets by using different methods based on your data source.
Feature overview
Real-time datasets support different definition methods based on the data source.
-
Data from Events: Define event properties or their statistical results as dataset metrics. For example, define an "Order event", create a real-time dataset based on the event, and create a real-time tag "Total consumption amount in the last 1 day".
-
Data from Tables: Parse and process fields from data source tables such as HBase and Hologres to define dataset metrics. For example, create a real-time dataset by querying transaction data in HBase and create a real-time tag "Number of orders in the last 7 days".
-
Data from API Requests: Parse and process request parameters to define dataset metrics. For example, acquire data from third-party open platforms to define real-time dataset metrics and create real-time tags.
The following table describes the available creation methods:
|
Creation Method |
Description |
|
Data from Events |
|
|
Create a dataset for real-time analysis by preprocessing events |
Preprocess events and use the results as dataset metrics. |
|
Data from Tables |
|
|
Parse fields from HBase data source tables by using calculation scripts to define dataset metrics. |
|
|
Process MySQL data sources by using SQL to define dataset metrics. |
|
|
Process Hologres data sources by using SQL to define dataset metrics. |
|
|
Process PostgreSQL data sources by using SQL to define dataset metrics. |
|