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

Building open source Hadoop

In 2006, Doug Cutting joined Yahoo in a team led by Eric Baldeschweiler (also known as eric14 or e14). This team had grid computing experts and users. Eric was in charge of figuring out how to build a next generation search grid computing framework for web searches. Here is a quote from a Yahoo employee at that time that described the situation prevailing at that time:

"Fortunately, and I remember the day well, Eric14 assembled the merry bunch of Grid (then called 'Utility Computing') engineers, and started down the path of rethinking the strategy - focussing on figuring out how to make Hadoop functional, featureful, and robust, instead." (Kumar, 2011)

The new team split out of Hadoop from Nutch with the leadership of Doug Cutting and created an open source Hadoop Framework based upon Hadoop Distributed File System as its storage system, and the MapReduce paradigm as the parallel computing model. Yahoo put more than 300 person-years of effort into Hadoop projects between 2006 - 2011. A team of nearly 100 people worked upon Apache Hadoop, and related projects such as Pig, ZooKeeper, Hive, HBase and Oozie.

In 2011, Yahoo was running Hadoop on over 40,000 machines (>300 cores). Hadoop has over a thousand regular users who use Hadoop for search-related research, advertising, detection of spam and personalization apart from many other topics. Hadoop has proven itself at Yahoo in many revenue driving improvement projects.

Figure 1 Timeline of Hadoop evolution

Nowadays, Hadoop is a top-level project at Apache Foundation. Hadoop is a software library that contains programs that allow processing of very large datasets, also known as big data, on a large cluster of commodity servers using a simple programming model known as MapReduce. At the time of writing this book, Hadoop 2.7.1 is the latest stable version.

It should be evident from the history of Hadoop that it was invented to solve the problem of searching and indexing massive data sets in large Internet companies. The purpose of Hadoop was to store and process the information inside Yahoo. Yahoo decided to make Hadoop open source so that the Hadoop project could benefit from the innovative ideas and involvement of the open source community.