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.
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
Course Overview Hi, my name is Janani Ravi, and I'm very happy to meet you today. I have a masters degree in electrical engineering from Stanford University and have worked with companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own staff at Looneycorn, a studio for high-quality video content. This course focuses on the most widely used distributed computing environment today, the Hadoop framework. This course gives you a fundamental understanding of the building blocks of Hadoop, the Hadoop Distributed File System, HDFS for storage, the MapReduce programming model for processing, and finally the resource negotiator YARN for cluster management. This will help you bridge the gap between plain vanilla programming and big data analysis, learn how to set up a pseudo-distributed Hadoop environment, and submit and monitor tasks on that environment using the built-in dev interface. And finally, understand the configuration choices you can make for stability, reliability, and optimized task scheduling on your distributed system.