YoVDO

The Building Blocks of Hadoop - HDFS, MapReduce, and YARN

Offered By: Pluralsight

Tags

Hadoop Courses Big Data Courses Data Storage Courses Data Processing Courses MapReduce Courses Cluster Management Courses HDFS Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
This course gives you a fundamental understanding of the building blocks of Hadoop: HDFS for storage, MapReduce for processing, and YARN for cluster management.

You know how to write Java code and you know what processing you want to perform on your huge dataset. But, can you use the Hadoop distributed framework effectively to get your work done? This course, The Building Blocks of Hadoop ­ HDFS, MapReduce, and YARN, gives you a fundamental understanding of the building blocks of Hadoop: HDFS for storage, MapReduce for processing, and YARN for cluster management, to help you bridge the gap between programming and big data analysis. First, you'll get a complete architecture overview for Hadoop. Next, you'll learn how to set up a pseudo-distributed Hadoop environment and submit and monitor tasks on that environment. And finally, you'll understand the configuration choices you can make for stability, reliability optimized task scheduling on your distributed system. By the end of this course you'll have gained a strong understanding of the building blocks needed in order for you to use Hadoop effectively.

Syllabus

  • Course Overview 1min
  • Introducing Hadoop 20mins
  • Installing Hadoop 33mins
  • Storing Data with HDFS 34mins
  • Processing Data with MapReduce 26mins
  • Scheduling and Managing Tasks with YARN 22mins

Taught by

Janani Ravi

Related Courses

Software Engineering for SaaS
University of California, Berkeley via Coursera
Android. Programación de Aplicaciones
Miríadax
Informatik für Ökonomen
University of Zurich via Coursera
Aprendizaje en la Nube, Herramientas web en el Aula
Galileo University via Independent
Desarrollo de Aplicaciones para Android
Galileo University via Independent