Kick starter of Big Data and Hadoop

A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.

Transcript

1. MapReduce Kick starter of Big Data and Hadoop
2. Logical View
3. Ways to Map Reduce
4. Understanding Map Reduce
5. Example – Word Count
6. Map Reduce Objects
7. Ways to run MapReduce
8. Where does the data come from?
9. Common Data Jobs for Map Reduce
10. Mapper Task Optimization
11. Reducer Task Optimization
12. Map Reduce Optimization
13. Combine Phase
14. Limitations
15. Further Reading