MapReduce and YARN
About this Course
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.
Lesson 1: Introduction to MapReduce and YARN
- Describe the MapReduce model v1 — this is the “classic” version that comes with Hadoop 1
- List the limitations of both 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, including the features of YARN and how a YARN program is run, and
- Compare “YARN / Hadoop 2 / MR2” versus “Hadoop 1 with MR1”
Lesson 2:Issues with/Limitations of Hadoop v1 & MapReduce v1
- List the limitations of MapReduce v1 and the need for MR v2 / YARN
- Describe MR2 / YARN processing
Lesson 3: The Architecture of YARN
- Understand the high level architecture of YARN
- Configuring, monitoring, and running applications in the YARN environment
Recommended skills prior to taking this course
- Know some basic Linux administration and commands
- The minimum passing mark for the course is 60%, where the review questions are worth 40% and the final exam is worth 60% of the course mark.
- You have 1 attempt to take the exam with multiple attempts per question.