Podcast
Questions and Answers
Match the following terms with their descriptions:
Match the following terms with their descriptions:
MapReduce = Parallel programming model for distributed computing Divide-and-conquer technique = Method used by MapReduce to process data in parallel Traditional Enterprise Systems = Systems with a centralized server for storing and processing data Bottleneck issue = Challenge faced when processing multiple files simultaneously in a centralized system
Match the following concepts with their roles in MapReduce:
Match the following concepts with their roles in MapReduce:
Task division = Dividing a task into small parts for parallel execution Intermediate results integration = Collecting and combining intermediate results to form the final output Parallel programming = Model used for building big data applications on multiple nodes Analytical capabilities = Providing the ability to analyze huge volumes of complex data
Match the following components with their roles in distributed computing:
Match the following components with their roles in distributed computing:
Workers-threads = Execute small parts of a task in parallel on a processor core Multi-core processor = Allows parallel execution of tasks across multiple cores Cluster = Group of many machines used for parallel processing MapReduce = Program model used for distributed computing
Match the following challenges with their solutions provided by MapReduce:
Match the following challenges with their solutions provided by MapReduce:
Match the following statements with their descriptions of MapReduce:
Match the following statements with their descriptions of MapReduce:
What is the primary purpose of MapReduce in distributed computing?
What is the primary purpose of MapReduce in distributed computing?
How does MapReduce address the bottleneck issue in centralized systems?
How does MapReduce address the bottleneck issue in centralized systems?
What analytical capabilities does MapReduce provide for processing complex data?
What analytical capabilities does MapReduce provide for processing complex data?
In what way does MapReduce work based on the divide-and-conquer technique?
In what way does MapReduce work based on the divide-and-conquer technique?
How does MapReduce differ from traditional enterprise systems in terms of data processing?
How does MapReduce differ from traditional enterprise systems in terms of data processing?
