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The Apache Hadoop open source software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
It contains four module :
HDFS is a hadoop distributed file system that manages large data files with streaming data access patterns, running on clusters of commodity hardware.
The fundamental idea of YARN is to split up the functionalities of resource management and job scheduling/monitoring into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job or a DAG of jobs.
Mapreduce is a programming model for data processing. The model is simple, yet not too simple to express useful programs in. Hadoop can run MapReduce programs written in various languages. Mapreduce works by breaking the processing into two phases: map phase and reduce. Each phase has key and value paris as input and output, the types of which may be chosen by the programmer. The programmer also specifies two functions: the map function and reduce function.
It provides common Java libraries that can be used across all modules
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Hadoop assignment is a task of practical learning or Hands-on where teacher, professor, instructor assign to students or learner for assessment as proof of concept. Submission time of such assignments and tasks varies on learning formats like MCQs, short answers, subjective questions and complete project solutions that solve real life problems. Students face challenges to complete the task as per given instructions and submit on the deadline for evaluations.
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