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

NoSQL databases

Programmers and DBAs have used relational database management systems (RDBMS) for the last three decades. RDBMS systems have been used to build online transaction processing systems and data warehousing systems. The relational model of database design is well understood in the industry, where data is stored in tables in a structured format and relationships between the tables are maintained using the concept of keys. As a result of the exponential jump in the number of transactions taking place through web and mobile applications, having a scalable RDBMS that delivers high availability and performance at a reasonable cost has become gradually more difficult. Internet companies such as Google, Amazon, and Yahoo! were the first to face this problem.

Hadoop is one such system that took center stage in handling the massive volumes of data for analytic purposes. However, the requirement to process big data and offer superior performance and availability is not limited to analytical...