Practice Aggregation/summarization (1.1.3.1) - Cloud Applications: MapReduce, Spark, and Apache Kafka
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Aggregation/Summarization

Practice - Aggregation/Summarization

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What are the two main phases of MapReduce?

💡 Hint: Think about the steps involved in processing data.

Question 2 Easy

What does Kafka primarily do?

💡 Hint: Consider the role of Kafka in data processing.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main function of MapReduce?

Data storage
Data processing
Data visualization

💡 Hint: Think about what MapReduce is primarily used for.

Question 2

True or False: Spark processes data in a disk-based manner like MapReduce.

True
False

💡 Hint: Remember how Spark improves upon traditional methods.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a simple MapReduce job for analyzing tweet sentiment over a dataset of tweets. Include considerations for the mapping and reducing functions.

💡 Hint: Think about how sentiment analysis can be structured in key-value pairs.

Challenge 2 Hard

Explain how Kafka handles message reprocessing and eventual consistency in a distributed environment.

💡 Hint: Consider what happens in the event of consumer failures.

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Reference links

Supplementary resources to enhance your learning experience.