Exam Professional Data Engineer topic 1 question 266 discussion

to detect average noise levels from sensors, the best approach is to use session windows with a 15-minute gap duration (Option A). Session windows are ideal for cases like this where the events (sensor data) are sporadic. They group events that occur within a certain time interval (15 minutes in your case) and a new window is started if no data is received for the duration of the gap. This matches your requirement to end the window when no data is received for 15 minutes, ensuring that the average noise level is calculated over periods of continuous data

You need a window that start when data for a sensor arrives and end when there's a gap in data. That would rule out hopping and tumbling windows. - > Windows need to stay open as long as there's data arriving - 30+ mins -> Window Should close when no data has been received for 15 mins -> Gap 15 mins

D for me, clearly stated on example: https://cloud.google.com/dataflow/docs/concepts/streaming-pipelines#hopping-windows

The problem requires detecting average noise levels when data is received for more than 30 minutes, but the window should end when no data has been received for 15 minutes. Session windows are ideal for this scenario because: They are designed to capture bursts of activity followed by periods of inactivity. They dynamically size based on the data received, which fits well with the variable duration of noise events. The gap duration can be set to define when a session ends. The 15-minute gap duration aligns perfectly with the requirement to end the window when no data has been received for 15 minutes. Session windows will naturally extend beyond 30 minutes if data keeps coming in, satisfying the requirement to detect levels for durations of more than 30 minutes.

D. Using a 15-minute window with a 15-minute tumbling window withAllowedLateness is the most suitable option for the following reasons: Flexibility: By allowing a 15-minute delay, it can accommodate various situations such as network latency or sensor failures. Processing efficiency: Using a fixed window improves processing efficiency. Compliance with conditions: The window ends if no data is received for 15 minutes, meeting the specified condition. Implementation points: .withAllowedLateness: This operator allows delayed events to be included in the current window. Trigger: When 30 minutes of data is collected, a trigger event is generated, and the average value is calculated based on this event. Watermark: By setting a watermark, processing of old data can be terminated.

Without a doubt A: https://cloud.google.com/dataflow/docs/concepts/streaming-pipelines#session-windows

The requirements are - recieve data for a duration of MORE than 30 minutes - end the window based on inactivity

Correct answer: A. Use a session Window to cature data and create an aggregation when the Session is larger than 30 minutes. https://cloud.google.com/dataflow/docs/concepts/streaming-pipelines#session-windows https://beam.apache.org/releases/javadoc/2.6.0/org/apache/beam/sdk/transforms/windowing/Sessions.html

C- Running average: https://cloud.google.com/dataflow/docs/concepts/streaming-pipelines#hopping-windows

Guys, I think its B. I was considering C, but C is calculating every 30 min, the 15min window gap data.Thats not what the questions wants. The questions wants a solution to get the average data of a 30 min window. So Its B. Look at this relating to C: "Use hopping windows with a 15-minute window, and a thirty-minute period" --> Wrong (IS DIFFERENT THEN) "Use hopping windows with a 30-minute window, and a 15-minute period" --> Right. Thats why I think the B is the right answer.

To take running averages of data, use hopping windows. You can use one-minute hopping windows with a thirty-second period to compute a one-minute running average every thirty seconds.

https://cloud.google.com/dataflow/docs/concepts/streaming-pipelines#session-windows A session window contains elements within a gap duration of another element. The gap duration is an interval between new data in a data stream. If data arrives after the gap duration, the data is assigned to a new window. So, we need

https://cloud.google.com/dataflow/docs/concepts/streaming-pipelines#hopping-windows To take running averages of data, use hopping windows. You can use one-minute hopping windows with a thirty-second period to compute a one-minute running average every thirty seconds.

Option D to me, It aligns with the specified criteria for detecting the average noise level within a 30-minute duration and handling the end of the window when no data is received for 15 minutes.

OPTION D is correct for the specific scenario where we want to detect the average noise level for a duration of more than 30 minutes but end the window when no data has been received for 15 minutes. Explanation: - Tumbling windows are non-overlapping windows, and in this case, you want to capture data continuously for 30-minute intervals. - Using a tumbling window with a 15-minute window size aligns with your requirement to detect the average noise level for a duration of more than 30 minutes. - Adding a .withAllowedLateness operator with a duration of fifteen minutes ensures that the window will still consider late-arriving data within that time frame. After fifteen minutes of no data, the window will be closed, and any late-arriving data will not be considered. Option A and B invalid as they capture fixed logic with 15 or 30 mins. Option C captures only 15 min average with 30 min trigger hence not suitable.

Hopping windows (called sliding windows in Apache Beam). To take running averages of data, use hopping windows.

C. Use hopping windows with a 15-minute window, and a thirty-minute period.