Over 100+ hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout
About This Book
- Implement outstanding Machine Learning use cases on your own analytics models and processes.
- Solutions to common problems when working with the Hadoop ecosystem.
- Step-by-step implementation of end-to-end big data use cases.
Who This Book Is For
Readers who have a basic knowledge of big data systems and want to advance their knowledge with hands-on recipes.
What You Will Learn
- Installing and maintaining Hadoop 2.X cluster and its ecosystem.
- Write advanced Map Reduce programs and understand design patterns.
- Advanced Data Analysis using the Hive, Pig, and Map Reduce programs.
- Import and export data from various sources using Sqoop and Flume.
- Data storage in various file formats such as Text, Sequential, Parquet, ORC, and RC Files.
- Machine learning principles with libraries such as Mahout
- Batch and Stream data processing using Apache Spark
Big data is the current requirement. Most organizations produce huge amount of data every day. With the arrival of Hadoop-like tools, it has become easier for everyone to solve big data problems with great efficiency and at minimal cost. Grasping Machine Learning techniques will help you greatly in building predictive models and using this data to make the right decisions for your organization.
Hadoop Real World Solutions Cookbook gives readers insights into learning and mastering big data via recipes. The book not only clarifies most big data tools in the market but also provides best practices for using them. The book provides recipes that are based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout and many more such ecosystem tools. This real-world-solution cookbook is packed with handy recipes you can apply to your own everyday issues. Each chapter provides in-depth recipes that can be referenced easily. This book provides detailed practices on the latest technologies such as YARN and Apache Spark. Readers will be able to consider themselves as big data experts on completion of this book.
This guide is an invaluable tutorial if you are planning to implement a big data warehouse for your business.
Table of Contents
Chapter 1. Getting Started with Hadoop 2.X
Chapter 2. Exploring HDFS
Chapter 3. Mastering Map Reduce Programs
Chapter 4. Data Analysis Using Hive, Pig, and Hbase
Chapter 5. Advanced Data Analysis Using Hive
Chapter 6. Data Import/Export Using Sqoop and Flume
Chapter 7. Automation of Hadoop Tasks Using Oozie
Chapter 8. Machine Learning and Predictive Analytics Using Mahout and R
Chapter 9. Integration with Apache Spark
Chapter 10. Hadoop Use Cases