Parallel Computing and Distributed Computing

What is Parallel Computing? Fill in the blank: In parallel computing, multiple ________ perform multiple tasks assigned to them simultaneously. Memory in parallel systems can either be shared or distributed. Parallel computing provides concurrency and saves time and money.

• processors

• memories

• networks

• programs

In distributed computing, there is no shared memory and computers communicate with each other through message passing.

• True

• False

Fill in the blank: The distributed systems have the ability to scale and expand horizontally through the addition of more _______ in the networks.

• machines

• routers

• cables

• protocols

Fill in the blank: MapReduce is a programming model for writing applications that can process Big Data in parallel on multiple _______.

• nodes

• files

• tables

• columns

What is Big Data?

• A small dataset that can be processed using traditional computing techniques

• A collection of large datasets that cannot be processed using traditional computing techniques

• A type of database

• A programming language

Which of the following aspects are involved in Big Data?

• Velocity

• Variety

• Volume

• Complexity

• All of the above

Why are traditional enterprise systems not suitable for processing huge volumes of scalable data?

• Traditional enterprise systems create too much of a bottleneck while processing multiple files simultaneously and cannot accommodate huge volumes of scalable data.

• Traditional enterprise systems are designed to process only unstructured data efficiently.

• Traditional enterprise systems are optimized for cloud-native architectures.

• Traditional enterprise systems are primarily used for mobile applications.

What are the two important tasks in the MapReduce algorithm?

• Map and Reduce

• Sort and Filter

• Join and Aggregate

• Split and Merge

Which phase in the MapReduce algorithm translates each record in an input file and sends the parsed data to the mapper in the form of key-value pairs?

• Map

• Input Phase

• Combiner

• Reducer

Which phase in MapReduce is responsible for aggregating, filtering, and combining data in a number of ways, and may output zero or more key-value pairs to the final step?

• Map

• Input Phase

• Reducer

• Combiner