Practice Datasets (2.1.3) - Cloud Applications: MapReduce, Spark, and Apache Kafka
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Datasets

Practice - Datasets

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is MapReduce used for?

💡 Hint: Think about the big data context.

Question 2 Easy

What does RDD stand for?

💡 Hint: What does the term 'RDD' represent in Spark?

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is a key characteristic of MapReduce?

Real-time processing
Distributed batch processing
In-memory computation

💡 Hint: Think about the type of tasks it is meant to handle.

Question 2

True or False: Apache Spark can only handle batch processing.

True
False

💡 Hint: Consider Spark's flexibility in handling data.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Consider a scenario where you have a massive log file. Design a MapReduce job that would count the unique IP addresses visiting a website. Mention each phase and how you would implement the Mapper and Reducer.

💡 Hint: Think about how the data is structured in the log file.

Challenge 2 Hard

In a data processing application, you are tasked with analyzing customer purchase patterns from large datasets. Discuss how you would use Spark's RDDs to manage this, and why it would be advantageous over using MapReduce.

💡 Hint: Consider the advantages of processing speed and data retrieval.

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