Book Image

Mastering Hadoop 3

By : Chanchal Singh, Manish Kumar
Book Image

Mastering Hadoop 3

By: Chanchal Singh, Manish Kumar

Overview of this book

Apache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency. With this guide, you’ll understand advanced concepts of the Hadoop ecosystem tool. You’ll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You’ll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you’ll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals. By the end of this book, you’ll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you’ll be equipped to tackle a range of real-world problems in data pipelines.
Table of Contents (23 chapters)
Title Page
Dedication
About Packt
Foreword
Contributors
Preface
Index

Mahout


Mahout is the Apache library for open source learning. Mahout mainly uses, but is not limited to, the classification and dimensional algorithms of clustering recommend engines (collaborative filtering and classification). Mahout's objective is to provide the usual machine learning algorithms with a highly scalable implementation. If the historical data to be used is large, then Mahout is the machinery of choice. We generally find that it is not possible to process the data on a single device. With large data becoming an important area of focus, Mahout meets the need for a machine learning tool that can extend beyond a single computer. Mahout is different from other tools such as R, Weka, and so on, as its emphasis on scalability. The Mahout learning implementations are written in Java, and most but not all of them are compiled using the MapReduce paradigm on Apache's distributed Hadoop calculation project. Mahout will be built using Scala DSL on Apache Spark, and programs written...