MapReduce and YARN

Take our free course


MapReduce and YARN

with Glen Mules, Joe Byers

Data scientists, engineers, or anyone who is interested in learning about MapReduce and YARN.

Time to complete:

Available in:

Apache Hadoop is one of the most popular tools for big data processing. It has been successfully deployed in production by many companies for several years. Though Hadoop is considered a reliable, scalable, and cost-effective solution, it is constantly being improved by a large community of developers. As a result, the 2.0 version offers several revolutionary features, including Yet Another Resource Negotiator (YARN), HDFS Federation, and high availability, which make the Hadoop cluster much more efficient, powerful, and reliable.
The most serious limitations of classical MapReduce are primarily related to scalability, resource utilization, and the support of workloads different from MapReduce. In the MapReduce framework, the job execution is controlled by two types of processes: a single master process called JobTracker and a number of subordinate processes called TaskTrackers.
Apache Hadoop 2.0 includes YARN, which separates the resource management and processing components. The YARN-based architecture is not constrained to MapReduce. In YARN, MapReduce is simply degraded to a role of a distributed application (but still a very popular and useful one) and is now called MRv2. MRv2 is simply the re-implementation of the classic MapReduce engine, now called MRv1, which runs on top of YARN.

The course reviews MapReduce1 and provides insight into the design and implementation of YARN: ResourceManager instead of a cluster manager, ApplicationMaster instead of a dedicated and short-lived JobTracker, NodeManager instead of TaskTracker, a distributed application instead of a MapReduce job.

Big Data University has been chosen by IBM as one of the issuers of badges as part of the IBM Open Badge program. Share your achievements through LinkedIn, Facebook, Twitter, and more!

Big Data University leverages the services of Pearson VUE Acclaim to assist in the administration of the IBM Open Badge program.  By enrolling into this course, you agree to Big Data University sharing your details with Pearson VUE Acclaim for the strict use of issuing your badge upon completion of the badge criteria.

Course Syllabus

After completing this course, you should be able to:

  • Describe the MapReduce model v1
  • List the limitations of Hadoop 1 and MapReduce 1
  • Review the Java code required to handle the Mapper class, the Reducer class, and the program driver needed to access MapReduce
  • Describe the YARN model
  • Compare YARN / Hadoop 2 / MR2 vs Hadoop 1 / MR1

General Information

  • This course is free.
  • It is self-paced.
  • It can be taken at any time.
  • It can be taken as many times as you wish.
  • Students passing the course (by passing the final exam) will have immediate access to printing their online certificate of achievement.
  • Your name in the certificate will appear exactly as entered in your profile in
  • If you did not pass the course, you can take it again at any time.


Before taking this course, you should have the following background:


  • Taken the Hadoop Fundamental v3 on BDU or equivalent
  • Basic understanding of Big Data, Apache Hadoop, and HDFS
  • Basic Linux Operating System knowledge
  • Some knowledge of Java and XML 

Recommended skills prior to taking this course

  • Basic understanding of Apache Hadoop and Big Data.
  • Basic Linux Operating System knowledge
  • Basic understanding of the Scala, Python, or Java programming languages.

Grading Scheme

The minimum passing mark for the course is 60%, where the final test is worth 100% of the course mark. You have 3 attempts to take the test