Final answer:
MapReduce (MR) is a good paradigm for interactive analysis of large datasets as it allows for processing and analyzing huge amounts of data across a distributed computing cluster. MR breaks down tasks into two steps: map and reduce, allowing for efficient and scalable analysis of big data.
Step-by-step explanation:
MR, which stands for MapReduce, is a paradigm for interactive analysis of large datasets. It is a programming model that allows for processing and analyzing huge amounts of data across a distributed computing cluster. MapReduce breaks down tasks into two steps: map and reduce.
The map step processes individual data items, while the reduce step aggregates the results from the map step. MR is commonly used in big data processing, allowing for efficient and scalable analysis.
MapReduce (MR) is a good paradigm for interactive analysis of large datasets as it allows for processing and analyzing huge amounts of data across a distributed computing cluster.
MR breaks down tasks into two steps: map and reduce, allowing for efficient and scalable analysis of big data.