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Answer: Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore.
The correct answer is **B**: Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore. **Why Option B is correct:** 1. **Hopping window**: Ideal for aggregating data at regular intervals, such as every 2 seconds, which is crucial for real-time systems like a ride-hailing app. 2. **Dataflow pipeline**: Efficiently processes continuous data streams from multiple sources. 3. **Memorystore**: A low-latency, in-memory data store service by Google Cloud, perfect for storing real-time aggregated data for quick access and visualization. **Why other options are incorrect:** - **Option A**: Session windows group data based on user-defined sessions, not suitable for time-based aggregation. - **Option C**: Tumbling windows do not support continuous processing required for real-time data aggregation every 2 seconds. - **Option D**: While a hopping window is correct, BigQuery may introduce latency issues compared to Memorystore for real-time visualization.
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You are tasked with developing a real-time system for a ride-hailing app to identify high-demand areas and efficiently redirect drivers to meet demand. The system will collect data from various sources, process it, and store the outcomes for real-time dashboards. Which of the following steps should be taken to implement the system effectively?
A
Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
B
Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore.
C
Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore.
D
Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.