Question

Dealing with late, out-of-order data in DataFlow: TRIGGER.
a) True
b) False

Answer 1

The statement that dealing with late, out-of-order data in DataFlow is achieved using the TRIGGER is False. In DataFlow, other methods and techniques such as windowing and event time characteristics are used to handle late or out-of-order data.

Step-by-step explanation:

The statement that dealing with late, out-of-order data in DataFlow is achieved using the TRIGGER is False.

In DataFlow, the TRIGGER is not used to handle late or out-of-order data. A TRIGGER is a database object that is used to automatically execute a set of actions when a specified event occurs, such as the insertion, deletion, or modification of data in a table. To handle late or out-of-order data in DataFlow, other methods and techniques such as windowing and event time characteristics are used to ensure the proper processing of the data.

Example:

Instead of using a TRIGGER, DataFlow pipelines can incorporate windowing techniques to handle late or out-of-order data. Windowing allows the grouping of data based on specific time intervals or event counts, ensuring that the data is processed in the desired order. For example, a sliding window of 1 minute can be used to capture data within a specific time range and process it accordingly.