As you get into deeper configuration and analysis, you will start facing new issues as you progress. This might include exceptions coming from programs, failing nodes, or even random errors. In this section, we will try to cover how they can be identified and addressed. Note that we will look at debugging MapReduce programs in Chapter 4, Developing MapReduce Applications; this section is more focused on debugging issues pertaining to the Hadoop cluster.

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
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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