Answer: Implementing mapWithState for efficient state updates

Implementing `mapWithState` is the most suitable technique for ensuring stateful computation across batches in a Spark streaming application processing events from Azure Event Hubs, especially for windowed aggregations. Here's why: 1. **Stateful Computation**: `mapWithState` allows for maintaining arbitrary state information across batches, crucial for windowed aggregations where state tracking over time is necessary. 2. **Efficient State Updates**: It offers a more efficient way to update and manage state compared to alternatives like `updateStateByKey`, by allowing the definition of functions to update state based on incoming data. 3. **Windowed Aggregations**: It is particularly well-suited for windowed aggregations, enabling stateful computations within specific time windows, which is essential for calculating aggregations over fixed intervals in streaming data. 4. **Scalability**: Designed for large-scale stateful computations in distributed environments like Spark streaming, `mapWithState` ensures scalability and fault tolerance. 5. **Real-time Processing**: It facilitates real-time processing of streaming data from Azure Event Hubs while maintaining stateful computations across batches, allowing for continuous updates to aggregations as new data arrives. In summary, `mapWithState` provides an efficient, scalable, and reliable method for stateful computation in Spark streaming applications, making it the optimal choice for processing events from Azure Event Hubs with windowed aggregations.

Author: LeetQuiz Editorial Team