- Solutions to common problems when working in the Hadoop environment.
- Recipes for (un)loading data, analytics, and troubleshooting.
- In depth code examples demonstrating various analytic models, analytic solutions, and common best practices.
Helping developers become more comfortable and proficient with solving problems in the Hadoop space. People will become more familiar with a wide variety of Hadoop related tools and best practices for implementation.
Hadoop Real World Solutions Cookbook provides in depth explanations and code examples. Each chapter contains a set of recipes that pose, then solve, technical challenges, and can be completed in any order. A recipe breaks a single problem down into discrete steps that are easy to follow. The book covers (un)loading to and from HDFS, graph analytics with Giraph, batch data analysis using Hive, Pig, and MapReduce, machine learning approaches with Mahout, debugging and troubleshooting MapReduce, and columnar storage and retrieval of structured data using Apache Accumulo.
Hadoop Real World Solutions Cookbook will give readers the examples they need to apply Hadoop technology to their own problems.
What you will learn from this book
- Data ETL, compression, serialization, and import/export.
- Simple and advanced aggregate analysis.
- Graph analysis.
- Machine learning.
- Troubleshooting and debugging.
- Scalable persistence.
- Cluster administration and configuration.
Cookbook recipes demonstrate Hadoop in action and then explain the concepts behind the code.
Who this book is written for
This book is ideal for developers who wish to have a better understanding of Hadoop application development and associated tools, and developers who understand Hadoop conceptually but want practical examples of real world applications.