Which technology is commonly used to process large-scale data by distributing computation across clusters?

Which technology is commonly used to process large-scale data by distributing computation across clusters?

• A. NoSQL database
• B. Master Data Management
• C. MapReduce
• D. Distributed Computing Concept

Answer: (C)

Processing very large data sets by dividing work across many machines is the idea of distributed data processing, where a framework coordinates multiple computers to work in parallel. MapReduce does exactly that: it splits the input into chunks, runs map tasks on those chunks to produce intermediate key-value pairs, and then runs reduce tasks to aggregate results. This parallel execution across cluster nodes, combined with automatic fault tolerance (retrying failed tasks) and data-local processing via a distributed file system, makes it the go-to model for scalable big-data processing. NoSQL databases focus mainly on storing and retrieving data at scale, not the compute model for distributing and coordinating complex processing tasks. Master Data Management centers on governance and ensuring consistency of core data, not on processing large-scale computations. A broad “distributed computing concept” lacks a concrete, widely adopted mechanism for large-scale data processing, whereas MapReduce is a defined technology designed specifically for this purpose.

Processing very large data sets by dividing work across many machines is the idea of distributed data processing, where a framework coordinates multiple computers to work in parallel. MapReduce does exactly that: it splits the input into chunks, runs map tasks on those chunks to produce intermediate key-value pairs, and then runs reduce tasks to aggregate results. This parallel execution across cluster nodes, combined with automatic fault tolerance (retrying failed tasks) and data-local processing via a distributed file system, makes it the go-to model for scalable big-data processing.

NoSQL databases focus mainly on storing and retrieving data at scale, not the compute model for distributing and coordinating complex processing tasks. Master Data Management centers on governance and ensuring consistency of core data, not on processing large-scale computations. A broad “distributed computing concept” lacks a concrete, widely adopted mechanism for large-scale data processing, whereas MapReduce is a defined technology designed specifically for this purpose.