Storage & MapReduce

Apache Hadoop    |    Beginner
  • 11 videos | 46m 33s
  • Includes Assessment
  • Earns a Badge
Rating 4.2 of 46 users Rating 4.2 of 46 users (46)
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 data
    Understand 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 graph
    Understand 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

  • 3m 46s
    In this video, find out how to illustrate and describe the components of Hadoop. Know the different parts of Hadoop, and what purpose they serve. FREE ACCESS
  • 7m 9s
    During this video, you will learn how to understand and differentiate between the different types of data. You will also learn about the difference between structured, unstructured, and semi-structured data. FREE ACCESS
  • Locked
    3.  Types of NoSQL Databases
    3m 43s
    In this video, you will understand the cloud column family of databases, and how they are used by Hadoop to store data. You will also discuss four different types of cloud databases; column, key value, document and graph. FREE ACCESS
  • Locked
    4.  Introduction to the Hadoop Distributed File System
    3m 21s
    In this video, you will understand the basics of the Hadoop Distributed file system. You will know how the HDFS is architected and structured. You will also know how the HDFS compares with other popular file system models. FREE ACCESS
  • Locked
    5.  Interacting with the HDFS
    3m 11s
    In this video, you will understand the DFS and learn basic HDFS navigation operations. Learn how to navigate the command line and look inside the HDFS. FREE ACCESS
  • Locked
    6.  File Operations within the HDFS
    3m 19s
    In this video, you will 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. FREE ACCESS
  • Locked
    7.  The MapReduce Principles, Mappers, and Reducers
    7m 47s
    In this video, you will understand the basic principles of MapReduce and general mapping issues. You will be able to describe the two steps in MapReduce, using Mappers and Reducers. You will also be able to explain how MapReduce is part of the Hadoop framework. FREE ACCESS
  • Locked
    8.  Using MapReduce with Pig and Hive
    3m 24s
    In this video, you will 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. FREE ACCESS
  • Locked
    9.  Introduction to the MapReduce Life Cycle
    4m 31s
    In this video, you will understand how Hadoop uses MapReduce. You will be able to describe the MapReduce lifecycle. You will know the role of a Job Client, Job Tracker, and Task Tracker. You will know the workings of Map Tasks, and Reduce Tasks. FREE ACCESS
  • Locked
    10.  Understanding the MapReduce Data Flow
    3m
    In this video, you will 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. FREE ACCESS
  • Locked
    11.  Subdividing Data
    3m 22s
    In this video, you will understand the process of mapping and reducing from a conceptual point of view. Also, you will know how to programmatically start MapReduce processing with mappers and reducers. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course Data Nuts & Bolts: Fundamentals of Data
Rating 4.4 of 524 users Rating 4.4 of 524 users (524)
Course Processing Data: Introducing Apache Spark
Rating 4.7 of 57 users Rating 4.7 of 57 users (57)
Course Cloud Data Platforms: Cloud-based Applications & Storage
Rating 4.4 of 153 users Rating 4.4 of 153 users (153)

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course Fundamentals & Installation
Rating 4.3 of 97 users Rating 4.3 of 97 users (97)
Course Getting Started with Hive
Rating 4.4 of 79 users Rating 4.4 of 79 users (79)
Course Big Data Concepts: Big Data Essentials
Rating 4.3 of 577 users Rating 4.3 of 577 users (577)