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

Machine learning case study in Spark


In this section, we will look into how to implement text classification using the Spark ML and Naive Bayes algorithms. The classification of text is one of NLP's most common cases of use. Text classification can be used to detect email spam, identify retail product hierarchy, and analyze feelings. This process is typically a problem of classification in which we try to identify a specific subject from a natural language source with a large volume of data. We can discuss several topics within each of the data groups and it is therefore important to classify the article or textual information in logical groups. The techniques of text classification help us to do this. These techniques require a lot of computing power if the data volume is large and a distributed computing framework for text classification is recommended. For example, if we want to classify legal documents in a knowledge repository on the internet, text classification techniques can be used...