The Building Blocks of Hadoop - HDFS, MapReduce, and YARN
Processing billions of records requires a deep understanding of distributed computing. In this course, you'll get introduced to Hadoop, an open-source distributed computing framework that can help you do just that.
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.
Course SyllabusCourse Overview- 1m 32s
—Course Overview 1m 32sIntroducing Hadoop- 20m 34s
—The Need for Distributed Computing 4m 58s
—Two Ways to Build a System 5m 0s
—Introducing Hadoop 5m 34s
—Other Technologies in the Hadoop Eco-system 5m 0sInstalling Hadoop- 33m 24sStoring Data with HDFS- 34m 8sProcessing Data with MapReduce- 26m 19sScheduling and Managing Tasks with YARN- 22m 39s