Course HighlightsCOURSE
MapReduce and YARN

MapReduce and YARN

Learn about MapReduce V1. Discover the benefits and features of YARN. Plus, get hands-on practice using the IBM Analytics Engine on IBM Cloud and explore the YARN architecture in detail, including ResourceManager, ApplicationMaster, and NodeManager.

Build critical skills for processing big data and boost your data science career.

MapReduce and YARN Highlights

  Course duration

Duration

  • 2 weeks, online
    3-4 hours/week
  Course Fee

Fee

US$ 99 - US$ 199

Course duration

Duration

  • 2 weeks, online
    3-4 hours/week
Course Fee

Fee

US$ 99 - US$ 199

Apache Hadoop is a popular tool in the world of big data processing. It is an open-source software framework designed for storing data and running applications on clusters of commodity hardware. It uses the MapReduce framework for data processing and provides the ability to handle large volumes of data with significant processing power.

In this course, you will learn about MapReduce V1, its phases and how a MapReduce V1 job is run. You will work on various hands-on-labs using the IBM Analytics Engine on IBM Cloud using PuTTY SSH and WinSCP clients. You will also learn about YARN, and investigate its core features and advantages over MapReduce. Plus, you will explore the YARN architecture in detail, including ResourceManager, ApplicationMaster, and NodeManager, and look at how to run an application using YARN.

For engineers working with big data, MapReduce and YARN are crucial tools for carrying out tasks more efficiently. This MapReduce and YARN course provides the necessary knowledge and hands-on-experience you need to boost your career as a data scientist.

This IBM certified course comprises three purposely designed modules that take you on a carefully defined learning journey.

It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 3-4 hours per week, you will complete the course in 2 weeks. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And don’t worry, you’re not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.

The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include videos, reading material, and online exam questions.

As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services will vary across packages.

Once you have successfully completed the course, you will earn your IBM Certificate.

You will be able to:

  • Understand the MapReduce model
  • Create a YARN model
  • Understand MR2/YARN processing
  • Understand the high level architecture of YARN
  • Configure, monitor, and run applications in the YARN environment
  • Big data engineers and data scientists looking to use Apache Hadoop 2.0 to improve their work efficiency.
  • Individuals keen to build upon their big data and Hadoop skills.
  • Individuals seeking to earn an IBM Hadoop Programming – Level 1 badge.

We recommend that you:

  • Have taken the Big Data Foundations course.
  • Have taken the Hadoop Foundations course.
  • Know some basic Linux administration and commands.

 

Course Outline

General Information
Learning Objectives
Syllabus
Grading Scheme
Certificate and Badge Information
Change Log
Copyrights and Trademarks
Learning Objectives
Overview (7:43)
MapReduce Phases (6:36)
Wordcount Program Example (8:20)
Miscellaneous Details - (9:48)
Lab Setup
Lab 1 : MapReduce and Yarn
Lab Setup [Optional]
Hands-on Lab Exercise 1 [Optional]
Graded Review Questions
Learning Objectives
High Level Architecture (6:31)
Running an Application (5:33)
Lab 2 - More Map Reduce Programming
Lab 2 - More MapReduce Programming [Optional]
Graded Review Questions
Course Certificate

Earn your certificate

Once you have completed this course, you will earn your certificate.

Preview digital certificate
MapReduce and YARN

FAQs

MapReduce is a processing framework that allows you to process large amounts of data in a distributed manner on a Hadoop cluster. YARN is ‘in charge’ of allocating resources among the applications running in a cluster.

Yes, this course will help you to build critical skills for processing big data. You will learn about MapReduce V1. You will discover the benefits and features of YARN. Plus, you will get hands-on practice using the IBM Analytics Engine on IBM Cloud and you will explore the YARN architecture in detail, including Resource Manager, Application Master, and Node Manager.

This course is suitable for big data engineers and data scientists looking to use Apache Hadoop 2.0 to improve their work efficiency. Additionally, individuals keen to build upon their big data and Hadoop skills would benefit from taking this course, as well as individuals seeking to earn an IBM Hadoop Programming – Level 1 badge.

It is highly recommended that you have taken the Big Data Foundations and Hadoop Foundations courses on SkillUp Online. Additionally, knowledge of some basic Linux administration and commands will be an added advantage.

Yes, you will be issued an IBM Certificate once you have successfully completed this course. You can upload this to your LinkedIn profile and include it on your resumé. 

Yes. This course is 100% online. Moreover, it is a self-paced course, so it is not run to a fixed schedule for completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 3-4 hours per week, you will complete the course in 2 weeks. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And don’t worry, you’re not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.

You will be able to access the course materials through your dashboard as soon as you enroll in this course.