MapReduce is a programming methodology used for writing programs on Apache Hadoop. It allows the programs to run on a large scalable cluster of servers. MapReduce was inspired by functional programming (https://en.wikipedia.org/wiki/Functional_programming). Functional Programming (FP) offers amazing unique features when compared to today's popular programming paradigms such as object-oriented (Java and JavaScript), declarative (SQL and CSS), or procedural (C, PHP, and Python). You can look at a comparison between multiple programming paradigms here. While we see a lot of interest in functional programming in academics, we rarely see equivalent enthusiasm from the developer community. Many developers and mentors claim that MapReduce is not actually a functional programming paradigm. Higher order functions in FP are functions that can take a function as...
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Table Of Contents
Apache Hadoop 3 Quick Start Guide
By :
Apache Hadoop 3 Quick Start Guide
By:
Overview of this book
Apache Hadoop is a widely used distributed data platform. It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics, including MapReduce, YARN, and HDFS.
The book begins with an overview of big data and Apache Hadoop. Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems.
The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring.
You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark.
By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster.
Table of Contents (10 chapters)
Preface
Free Chapter
Hadoop 3.0 - Background and Introduction
Planning and Setting Up Hadoop Clusters
Deep Dive into the Hadoop Distributed File System
Developing MapReduce Applications
Building Rich YARN Applications
Monitoring and Administration of a Hadoop Cluster
Demystifying Hadoop Ecosystem Components
Advanced Topics in Apache Hadoop
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