Hadoop & MapReduce Getting Started

Apache Hadoop    |    Beginner
  • 8 videos | 1h 3m 29s
  • Includes Assessment
  • Earns a Badge
Rating 4.7 of 54 users Rating 4.7 of 54 users (54)
In this course, learners will explore the theory behind big data analysis using Hadoop, and how MapReduce enables parallel processing of large data sets distributed on a cluster of machines. Begin with an introduction to big data and the various sources and characteristics of data available today. Look at challenges involved in processing big data and options available to address them. Next, a brief overview of Hadoop, its role in processing big data, and the functions of its components such as the Hadoop Distributed File System (HDFS), MapReduce, and YARN (Yet Another Resource Negotiator). Explore the working of Hadoop's MapReduce framework to process data in parallel on a cluster of machines. Recall steps involved in building a MapReduce application and specifics of the Map phase in processing each row of the input file's data. Recognize the functions of the Shuffle and Reduce phases in sorting and interpreting the output of the Map phase to produce a meaningful output. To conclude, complete an exercise on the fundamentals of Hadoop and MapReduce.

WHAT YOU WILL LEARN

  • Describe what big data is and list the various sources and characteristics of data available today
    Recognize the challenges involved in processing big data and the options available to address them such as vertical and horizontal scaling
    Specify the role of hadoop in processing big data and describe the function of its components such as hdfs, mapreduce, and yarn
    Identify the purpose and describe the workings of hadoop's mapreduce framework to process data in parallel on a cluster of machines
  • Recall the steps involved in building a mapreduce application and the specific workings of the map phase in processing each row of data in the input file
    Recognize the functions of the shuffle and reduce phases in sorting and interpreting the output of the map phase to produce a meaningful output
    Recognize the techniques related to scaling data processing tasks, working with clusters, and mapreduce and identify the hadoop components and their functions

IN THIS COURSE

  • 2m 53s
  • 8m 27s
    After completing this video, you will be able to describe what big data is and list the various sources and characteristics of data available today. FREE ACCESS
  • Locked
    3.  Building Systems to Scale with Data
    9m 16s
    Upon completion of this video, you will be able to recognize the challenges involved in processing big data and the options available to address them, such as vertical and horizontal scaling. FREE ACCESS
  • Locked
    4.  A Quick Overview of Hadoop
    9m 28s
    Upon completion of this video, you will be able to specify the role of Hadoop in processing big data and describe the function of its components, such as HDFS, MapReduce, and YARN. FREE ACCESS
  • Locked
    5.  MapReduce Overview
    9m 17s
    During this video, you will learn how to identify the purpose and describe the workings of Hadoop's MapReduce framework. This framework processes data in parallel on a cluster of machines. FREE ACCESS
  • Locked
    6.  The Map Phase of a MapReduce
    8m 24s
    Upon completion of this video, you will be able to recall the steps involved in building a MapReduce application and the specific workings of the Map phase in processing each row of data in the input file. FREE ACCESS
  • Locked
    7.  The Shuffle and Reduce Phases
    7m 16s
    After completing this video, you will be able to recognize the functions of the Shuffle and Reduce phases in sorting, and interpreting the output of the Map phase to produce a meaningful output. FREE ACCESS
  • Locked
    8.  Exercise: Fundamentals of Hadoop and MapReduce
    8m 28s
    After completing this video, you will be able to recognize the techniques related to scaling data processing tasks, working with clusters, and MapReduce, and identify the Hadoop components and their functions. FREE ACCESS

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