Book Image

Hadoop Blueprints

By : Anurag Shrivastava, Tanmay Deshpande
Book Image

Hadoop Blueprints

By: Anurag Shrivastava, Tanmay Deshpande

Overview of this book

If you have a basic understanding of Hadoop and want to put your knowledge to use to build fantastic Big Data solutions for business, then this book is for you. Build six real-life, end-to-end solutions using the tools in the Hadoop ecosystem, and take your knowledge of Hadoop to the next level. Start off by understanding various business problems which can be solved using Hadoop. You will also get acquainted with the common architectural patterns which are used to build Hadoop-based solutions. Build a 360-degree view of the customer by working with different types of data, and build an efficient fraud detection system for a financial institution. You will also develop a system in Hadoop to improve the effectiveness of marketing campaigns. Build a churn detection system for a telecom company, develop an Internet of Things (IoT) system to monitor the environment in a factory, and build a data lake – all making use of the concepts and techniques mentioned in this book. The book covers other technologies and frameworks like Apache Spark, Hive, Sqoop, and more, and how they can be used in conjunction with Hadoop. You will be able to try out the solutions explained in the book and use the knowledge gained to extend them further in your own problem space.
Table of Contents (14 chapters)
Hadoop Blueprints
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface

Summary


In this chapter, we started with basic understanding of a customer 360-degree view and how it can be useful. We explored tools such as Hive and Sqoop. We worked with MySQL and HDFS to extract, load, and transform data. We used structured data, web logs, and tweets to build our customer 360-degree view. We performed batch processing on various datasets that contribute to building the 360-degree view.

In this chapter, we carried out extract, load, and transform (ELT) tasks manually. This will not be practical in a real-life situation. With the help of schedulers and workflow tools, we can automate ELT tasks and build a data pipeline.