Practice Stream Processing With Apache Kafka And Spark Streaming (5.2) - Chapter 5: IoT Data Engineering and Analytics — Detailed Explanation

Practice: Stream Processing with Apache Kafka and Spark Streaming

Lesson 5.2IoT Data Engineering and Analytics — Detailed Explanation

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9 questions

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6 sample questions

Question 1.
What is the primary purpose of Apache Kafka?
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Think about how data flows in IoT scenarios.
Question 2.
What does Spark Streaming process?
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Consider the processing method in real-time analytics.
3Question 3.
What aspect of Apache Kafka ensures that data is never lost?
- High Scalability
- Durability
- Micro-batching
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Think about the importance of data integrity.
4Question 4.
True or False: Spark Streaming processes data in large batches instead of micro-batches.
- True
- False
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Recall the definition of micro-batching.
Question 5.
Design a hypothetical IoT system that utilizes both Kafka and Spark Streaming to monitor and respond to environmental conditions. What would be the architecture, and how would data flow through the system?
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Sketch a flow diagram to visualize the data movement.
Question 6.
Discuss the potential challenges when implementing Kafka and Spark Streaming in a critical real-time system. How would you mitigate these challenges?
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Consider real-world examples where these technologies faced issues.

5 more questions in this section. Enroll free to practise them all.