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Apache Hadoop 3 Quick Start Guide

Apache Hadoop 3 Quick Start Guide

By : Vijay Karambelkar
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Apache Hadoop 3 Quick Start Guide

Apache Hadoop 3 Quick Start Guide

By: Vijay Karambelkar

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)
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Advanced Topics in Apache Hadoop

Previously, we have seen some of Apache Hadoop's ecosystem components. In this chapter, we will be looking at advanced topics on Apache Hadoop, which also involves use of some of the Apache Hadoop components that are not covered in previous chapters. Apache Hadoop has started solving the complex problems of large data, but it is important for developers to understand that not all data problems are really big data problems or Apache Hadoop problems. At times, Apache Hadoop may not be the suitable technology for your data problems.

The decision whether to assess a given problem is usually driven by the famous 3Vs (Volume, Variety, and Veracity) of data. In fact, many organizations that use Apache Hadoop often face challenges in terms of efficiency and performance of solutions due to lack of good Hadoop architecture. A good example of it is a...

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