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

Hadoop and R


R is a data science programming tool for analyzing statistical data on models and translating analytical results into colorful graphics. R without the doubt is the most preferred programming tool for statisticians, data scientists, data analysts, and data architects, but when working with large datasets, it is short. One major disadvantage of the R programming language is that all objects are loaded into a single machine's main memory. Large petabyte size datasets cannot be loaded into the RAM. Hadoop is an ideal solution when it is integrated with R language. Data scientists must limit their data analysis to a sample of data from the large dataset to adapt to the single machine limitation of the R programming language in memory. When dealing with big data, this limitation of the R programming language is a major obstacle. Since R is not very scalable, only limited data can be processed by the core R engine. Its data processing capacity is limited to one node memory. This limits...