BigData / Hadoop MapReduce
1. What is Hadoop MapReduce?
MapReduce is a parallel processing framework that processes big amounts of data in-parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. MapReduce works on master-slave architecture and can process a large amount of data by dividing the...
2. Explain The MapReduce process.
MapReduce has two main processes. Job Tracker (Master)....
3. Explain the steps involved in Hadoop MapReduce Process.
MapReduce Job is complex in nature and it involves multiple steps for complete parallel execution of the job. Following steps are executed by MapReduce Job in a sequential order.
4. Explain Mapper in Hadoop MapReduce.
In MapReduce, task tracker presents the Mapper that provides parallelism to MapReduce job. The output of the mapper is a MAP <Key, Value>....
5. What is Shuffle and Sorting in Hadoop MapReduce?
Shuffle and Sorting are the intermediate steps between Mapper and Reducer steps of MapReduce Job. Shuffle process aggregates all the mapper data by grouping them into key value....
6. What is Reducer in Hadoop MapReduce?
After the shuffling and sorting process, the processed map output is sent to Reducer for generation of final output. Reducer aggregates the data based on the logic provided in Reducer class.
7. Explain Hadoop streaming.
Hadoop distribution has a generic application programming interface (API) for writing Map and Reduce jobs in any preferred programming language like Java Python, Perl, Ruby, etc. This is referred as Hadoop Streaming....
