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
About the Authors
About the Reviewers


In this chapter, we covered some topics that are useful in building Hadoop-based solutions but not directly related to the main theme of this book. We explained that building a Hadoop-based solution involves a team work, where data engineering and data science skills are represented adequately. We covered how cloud-based Hadoop reduces the upfront investment and time-to-market for Hadoop-based solutions. Hadoop as a cloud-based service is available from Amazon and Microsoft. NoSQL databases play an important role in building web and mobile applications. Often, a Hadoop-based data pipeline will have to be integrated with NoSQL databases. Finally, we covered how In-Memory databases are changing the nature of analytics, transaction processing, and stream processing to provide actionable business insights faster.

This book has given you several practical examples of how we can use Hadoop to solve business problems. We covered several commonly used tools from the Hadoop ecosystem to build...