About Lesson
- Introduction to MapReduce
- Concepts of MapReduce
- Map Reduce architecture
- Advance Concept of Map Reduce
- Understanding how the distributed processing solves the big data challenge and how MapReduce helps to solve that problem
- Understanding the concept of Mappers and Reducers
- Phases of a MapReduce program
- Anatomy of a Map Reduce Job Run
- Data-types in Hadoop MapReduce
- Role of InputSplit and RecordReader
- Input format and Output format in Hadoop
- Concepts of Combiner and Partitioner
- Running and Monitoring MapReduce jobs
- Writing your own MapReduce job using MapReduce API
- Difference between Hadoop 1 & Hadoop 2
- The Hadoop Java API for MapReduce
- Mapper Class
- Reducer Class
- Driver Class
- Basic Configuration of MapReduce
- Writing and Executing the Basic MapReduce Program using Java
- Submission & Initialization of MapReduce Job.
- Explain the Driver, Mapper and Reducer code
- Word count problem and solution
- Configuring development environment – Eclipse
- Testing, debugging project through eclipse and then finally packaging, deploying the code on Hadoop Cluster