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Hadoop Blueprints

Hadoop Blueprints

By : Sudheesh Narayan, Anurag Shrivastava, Deshpande
5 (1)
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Hadoop Blueprints

Hadoop Blueprints

5 (1)
By: Sudheesh Narayan, Anurag Shrivastava, 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 (9 chapters)
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Creating the solution outline

Let us assume that at this moment we know nothing about who will respond to our marketing folders. In traditional marketing, we can provide discount coupons in the campaign folders with a barcode as a means to uniquely identify a customer. When a customer presents a discount coupon during the purchase then we know that the customer has responded to our marketing campaign. We can join the barcode on the discount coupon with the customer master data available in the company to find out who has responded to our campaign.

We will solve the problem of Furnitica using classification. Classification is a supervised learning method which uses historical data and past outcomes to predict future outcomes.

As an example, for a credit card company, the historical credit card approval data is shown in Table 1 :

Gender

Age

Owns a House

Owns a Car

Annual Salary in EUR

Result

M

23

N

N

24000

Not Approved

F

35

Y

N

55000

Approved

M

40

Y

Y

52000...

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