MapReduce was a breakthrough in big data processing that has become mainstream and been improved upon significantly. Learn about how MapReduce works.
In this module, you will:
- Identify the underlying distributed programming model of MapReduce
- Explain how MapReduce can exploit data parallelism
- Identify the input and output of map and reduce tasks
- Define task elasticity, and indicate its importance for effective job scheduling
- Explain the map and reduce task-scheduling strategies in Hadoop MapReduce
- List the elements of the YARN architecture, and identify the role of each element
- Summarize the lifecycle of a MapReduce job in YARN
- Compare and contrast the architectures and the resource allocators of YARN and the previous Hadoop MapReduce
- Indicate how job and task scheduling differ in YARN as opposed to the previous Hadoop MapReduce
In partnership with Dr. Majd Sakr and Carnegie Mellon University.