Storage & MapReduce
Apache Hadoop 2.0 | Beginner
- 11 videos | 46m 33s
- Includes Assessment
- Earns a Badge
MapReduce is a framework for writing applications to process huge amounts of data. Let's look at Hadoop storage, MapReduce, and how to use MapReduce with associated development tools.
WHAT YOU WILL LEARN
Illustrate and describe the components of Hadoop. Know the different parts of Hadoop, and what purpose they serve.Understand and differentiate between the different types of data. Explain the difference between structured, unstructured, and semi-structured dataUnderstand the cloud column family of databases, and how they are used by Hadoop to store data. Discuss four different types of cloud databases; column, key value, document and graphUnderstand the basics of the Hadoop Distributed file system. Know how the HDFS is architected and structured. Know how the HDFS compares with other popular file system models.Understand the DFS and learn basic HDFS navigation operations. Learn how to navigate the command line and look inside the HDFS.Learn how to perform file operations within the HDFS. Understand how to add and delete files, and how to list files and see their properties.
Understand the basic principles of MapReduce and general mapping issues. Be able to describe the two steps in Map Reduce, using Mappers and Reducers. Explain how MapReduce is part of the Hadoop framework.Understand how Pig and Hive are used for Hadoop Map Reduce Jobs. Know the differences between Pig and Hive, and how each can be used for unstructured and structured data.Understand how Hadoop uses MapReduce. Be able to describe the MapReduce lifecycle. Know the role of a Job Client, Job Tracker, and Task Tracker. Know the workings of Map Tasks, and Reduce Tasks.Understand how Hadoop MapReduce handles and processes data. Explore the mapping and reducing steps in more detail. Know the vocabulary of the MapReduce dataflow process.Understand the process of Mapping and Reducing from a conceptual point of view. Also know how to programmatically start MapReduce processing with Mappers and Reducers.
IN THIS COURSE
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