Tumbling Window
Question
Steve Warner's Car Dealership is an establishment in Wellington, New Zealand, which deals in purchasing and selling of cars and autos.
Steve has hired you as a skilled consultant for Azure work and projects.You are chairing a team session and, in that session, you need to describe the Tumbling Window.
Which of the following statements can you use to explain the Tumbling window?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D. E.Correct Answer: B
A Tumbling window function segments a data stream into a contiguous series of fixed size, non-overlapping time segments and works against them.
Events can't belong to more than 1 tumbling window.
Option A is incorrect.
It is the Hopping window function that models scheduled overlapping windows, jumping forward in time by a fixed/defined period.
Option B is correct.
A Tumbling window function segments a data stream into a contiguous series of fixed size, non-overlapping time segments and works against them.
Events can't belong to more than 1 tumbling window.
Option C is incorrect.
It is the sliding window function that generates events for specific times when the content of the window is really changed.
Option D is incorrect.
A session window function clusters together events arriving at similar times, filtering out periods of time with no data.
Option E is incorrect.
A Snapshot window function groups events by the same timestamp values.
To know more about Windowing functions, please visit the below-given link:
Sure, I can explain the Tumbling Window in detail.
In data processing, a window refers to a subset of data that is processed at a time. Windows are used to group data and perform operations such as aggregation, filtering, and transformation on that grouped data. There are several types of windows in data processing, and one of them is the Tumbling Window.
A Tumbling Window is a type of window that segments a data stream into a contiguous series of fixed-size, non-overlapping time segments, also known as buckets. In a tumbling window, events can't belong to more than one window, meaning that once an event is assigned to a window, it will remain in that window until the window's end time is reached.
The Tumbling Window works by defining a fixed period of time, such as 5 minutes, and creating a new window every 5 minutes. Each window will contain all the events that occurred during that period. The end time of each window is fixed, and the start time of the next window is determined by jumping forward in time by the fixed period. This means that each window is defined by a specific time period, and all events that occur during that period are processed together.
Tumbling windows are useful in scenarios where data needs to be processed at a fixed interval, such as hourly, daily, or weekly. They are commonly used for time-based aggregation and analysis, such as calculating the average sales per day or the total revenue per hour.
In summary, a Tumbling Window is a window function that segments a data stream into a contiguous series of fixed-size, non-overlapping time segments and works against them. It's useful for time-based aggregation and analysis, and events can't belong to more than one tumbling window.
