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Answer: Optimize the pipeline for analytical processing by focusing on data aggregation, windowing, and stateful processing.
For an analytical use case like analyzing customer purchase patterns in real-time, the pipeline should be optimized for analytical processing. This involves focusing on data aggregation, windowing, and stateful processing to extract meaningful insights from the data. Techniques such as time windows, session windows, and watermarking can be used to handle out-of-order events and late data. Additionally, the pipeline should be designed to handle high throughput and low latency to ensure real-time processing.
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You are developing a data pipeline for a retail company that needs to analyze customer purchase patterns in real-time. The pipeline processes data from multiple sources, including online transactions, in-store purchases, and customer feedback. How would you optimize the pipeline for analytical purposes?
A
Optimize the pipeline for transactional processing by ensuring data consistency and atomicity.
B
Optimize the pipeline for analytical processing by focusing on data aggregation, windowing, and stateful processing.
C
Use a single processing engine for both transactional and analytical processing to simplify the architecture.
D
Prioritize processing speed over data accuracy and completeness to meet real-time requirements.