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Home > Data > Data Processing > Hadoop Blueprints
Hadoop Blueprints
Hadoop Blueprints

Hadoop Blueprints: Use Hadoop to solve business problems by learning from a rich set of real-life case studies

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Profile Icon Anurag Shrivastava Profile Icon Sudheesh Narayan Profile Icon Deshpande
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Hadoop Blueprints

Chapter 2. A 360-Degree View of the Customer

In this chapter, we will take the example of a fictitious company called Cosmetica Inc. This company was founded in 1983, when the web commerce was invented. From its humble start as a small shop in Uden, it has now more than 300 shopping outlets. The company also runs a web shop where customers can buy products 24x7. The company is planning to launch a personalized shopping service where the customer will get assistance in choosing the right product.

This company is interested in building a 360-degree view of customers who often visit their web shop, and who are also active on social media. To build a 360-degree view, we will follow the following steps in this chapter:

  • Understanding the data required in the 360-degree view
  • Setting up the technology stack
  • Engineering the solution
  • Presenting the solution using a web interface

Capturing business information

Like any other mid-sized retailer, the information technology needs of Cosmetica have grown with times. Previously, most customers visited their shopping outlets and did most of their purchasing during the weekends. During the festival seasons, the sales used to be brisk. In late 90s, Cosmetica introduced a loyalty card to boost customer loyalty. This loyalty card allowed customers to collect loyalty points at the time of making a purchase in the shop. The customers could redeem those loyalty points to buy products that were on special offer.

Since the year 2005, Cosmetica has a good presence on the World Wide Web through their webshop; customers can browse their products online and buy them. Cosmetica is planning to offer a personalized cosmetic shopping service. A customer can call the Cosmetica call center in order to approach a human shopping assistant and get personalized advice.

In order to do this, Cosmetica wants to have a 360-degree view of customers...

Setting up the technology stack

In Chapter 1, Hadoop and Big Data, we covered various tools in the Hadoop ecosystem. In this chapter, we will use some of those tools to set up the technology stack for building a 360-degree view of a customer. Setting up all the tools in the Hadoop ecosystem can be cumbersome and a fault-prone process, owing to multiple dependencies on the libraries. The tools in the Hadoop ecosystem have evolved over a period of time by contributions from the open source community. Therefore, these tools lack an integrated installation and configuration approach. The Pure Play Hadoop vendors have made good progress in easing the installation of Hadoop by offering Hadoop sandboxes and RPM packages. One such vendor is Hortonworks who offer the Hortonwork Data Platform or HDP. HDP is a pure open source platform built upon open source Hadoop, and several tools from the Hadoop ecosystem.

HDP is available on a CentOS-based virtual machine such as a VirtualBox image. We will...

Test driving Hive and Sqoop

In the previous section, we verified that MySQL, Hive, and Sqoop were available on our Hadoop Sandbox. We will now test drive Hive and Sqoop.

Querying data using Hive

We run Hive queries to select data from tables. Hive has two types of tables:

  • Managed tables
  • External tables

Hive creates managed tables by default. To create external tables, we specify the keyword external during table creation.

In the case of managed tables, the table lifecycle is completely managed by Hive. If you drop a managed table, then the associated data and metadata are also deleted by Hive. The external table reads data from an HDFS file. This file is not deleted when the table is dropped by Hive. Other tools can also access the HDFS file while at the same time we can run Hive queries on the HDFS by defining an external table for the file.

In Chapter 1, Hadoop and Big Data, of this book, we used a dataset containing the historical stock price of IBM to run a MapReduce job that calculated...

Engineering the solution

We will engineer the solution by breaking down the problem into several parts. In each part, we will perform a step to import or transform the data. Finally, we will bring everything together to create the view. To engineer the solution, we will use Sqoop to load customer master data from MySql RDBMS into Hive. We will use HDFS copy commands to load the Apache Access logs and tweets in Hadoop.

In the 360-degree view of the customer, we will combine the information from the following sources:

  • Full name, gender, userID, and e-mail from customer master data as the data from the system of records
  • Brand names frequently visited on Cosmetica's web shop as the data from web logs
  • Tweets on certain topics as the social media data

Engineering the solution
Figure 5 360-degree view combines data from various sources

You should bear in mind that we have taken a small set of data sources to create the 360-degree view. In practice, you should think of several data sources that can be used to build...

Capturing business information

Like any other mid-sized retailer, the information technology needs of Cosmetica have grown with times. Previously, most customers visited their shopping outlets and did most of their purchasing during the weekends. During the festival seasons, the sales used to be brisk. In late 90s, Cosmetica introduced a loyalty card to boost customer loyalty. This loyalty card allowed customers to collect loyalty points at the time of making a purchase in the shop. The customers could redeem those loyalty points to buy products that were on special offer.

Since the year 2005, Cosmetica has a good presence on the World Wide Web through their webshop; customers can browse their products online and buy them. Cosmetica is planning to offer a personalized cosmetic shopping service. A customer can call the Cosmetica call center in order to approach a human shopping assistant and get personalized advice.

In order to do this, Cosmetica wants to have a 360-degree view of customers...

Setting up the technology stack

In Chapter 1, Hadoop and Big Data, we covered various tools in the Hadoop ecosystem. In this chapter, we will use some of those tools to set up the technology stack for building a 360-degree view of a customer. Setting up all the tools in the Hadoop ecosystem can be cumbersome and a fault-prone process, owing to multiple dependencies on the libraries. The tools in the Hadoop ecosystem have evolved over a period of time by contributions from the open source community. Therefore, these tools lack an integrated installation and configuration approach. The Pure Play Hadoop vendors have made good progress in easing the installation of Hadoop by offering Hadoop sandboxes and RPM packages. One such vendor is Hortonworks who offer the Hortonwork Data Platform or HDP. HDP is a pure open source platform built upon open source Hadoop, and several tools from the Hadoop ecosystem.

HDP is available on a CentOS-based virtual machine such as a VirtualBox image. We will deploy...

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Key benefits

  • Solve real-world business problems using Hadoop and other Big Data technologies
  • Build efficient data lakes in Hadoop, and develop systems for various business cases like improving marketing campaigns, fraud detection, and more
  • Power packed with six case studies to get you going with Hadoop for Business Intelligence

Description

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.

Who is this book for?

If you are interested in building efficient business solutions using Hadoop, this is the book for you This book assumes that you have basic knowledge of Hadoop, Java, and any scripting language.

What you will learn

  • Learn about the evolution of Hadoop as the big data platform
  • Understand the basics of Hadoop architecture
  • Build a 360 degree view of your customer using Sqoop and Hive
  • Build and run classification models on Hadoop using BigML
  • Use Spark and Hadoop to build a fraud detection system
  • Develop a churn detection system using Java and MapReduce
  • Build an IoT-based data collection and visualization system
  • Get to grips with building a Hadoop-based Data Lake for large enterprises
  • Learn about the coexistence of NoSQL and In-Memory databases in the Hadoop ecosystem

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 30, 2016
Length: 316 pages
Edition : 1st
Language : English
ISBN-13 : 9781783980314
Vendor :
Apache
Category :
Data
Languages :
Java
Concepts :
Data Processing
Tools :
Hadoop

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Product Details

Publication date : Sep 30, 2016
Length: 316 pages
Edition : 1st
Language : English
ISBN-13 : 9781783980314
Vendor :
Apache
Category :
Data
Languages :
Java
Concepts :
Data Processing
Tools :
Hadoop

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Hadoop Blueprints
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Table of Contents

8 Chapters
1. Hadoop and Big Data Chevron down icon Chevron up icon
1. Hadoop and Big Data
The beginning of the big data problem
Building open source Hadoop
Enterprise Hadoop
The design of the Hadoop system
MapReduce
Building a MapReduce Version 2 program
Hadoop platform tools
Big data use cases
The architecture of Hadoop-based systems
Summary
2. A 360-Degree View of the Customer Chevron down icon Chevron up icon
2. A 360-Degree View of the Customer
Capturing business information
Setting up the technology stack
Test driving Hive and Sqoop
Engineering the solution
Presenting the view
Summary
3. Building a Fraud Detection System Chevron down icon Chevron up icon
3. Building a Fraud Detection System
Understanding the business problem
Selecting and cleansing the dataset
Machine learning for fraud detection
Designing the high-level architecture
Creating our fraud detection model
Putting the fraud detection model to use
4. Marketing Campaign Planning Chevron down icon Chevron up icon
4. Marketing Campaign Planning
Creating the solution outline
Supervised learning
Tree-structure models for classification
Finding the right dataset
Setting the up the solution architecture
Building the machine learning model
Running the Model on Hadoop
Creating the target List
Post campaign activities
Summary
5. Churn Detection Chevron down icon Chevron up icon
5. Churn Detection
A business case for churn detection
Creating the solution outline
Building a churn predictor using Hadoop
Summary
6. Analyze Sensor Data Using Hadoop Chevron down icon Chevron up icon
6. Analyze Sensor Data Using Hadoop
A business case for sensor data analytics
Creating the solution outline
Technology stack
Batch data analytics
Stream data analytics
Summary
7. Building a Data Lake Chevron down icon Chevron up icon
7. Building a Data Lake
Data lake building blocks
Hadoop security
Apache Ranger
Apache Flume
Apache Zeppelin
Technology stack for Data Lake
Data Lake business requirements
Summary
8. Future Directions Chevron down icon Chevron up icon
8. Future Directions
Hadoop solutions team
Hadoop on Cloud
NoSQL databases
Summary

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Wissem Dec 22, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As a technical reviewer of this book, I highly recommend reading it. It has very complete and useful real world use cases of using Hadoop and its ecosystem. Chapters explain Big Data technology trends like IOT , Data Lakes and how Hadoop fits with is ecosystem to solve those problems. Analyze Sensor Data Using Hadoop, Building a data lake, Building a Fraud Detection System, Churn Detection are my favorites chapters where the authors bring with examples the steps of using Hadoop ecosystem. In Summary, if you want to learn Hadoop with examples, this is the right book for you.Cheers
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About the 3 authors

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Profile icon Anurag Shrivastava
Anurag Shrivastava
Anurag Shrivastava is an entrepreneur, blogger, and manager living in Almere near Amsterdam in the Netherlands. He started his IT journey by writing a small poker program on a mainframe computer 30 years back, and he fell in love with software technology. In his 24-year career in IT, he has worked for companies of various sizes, ranging from Internet start-ups to large system integrators in Europe. Anurag kick-started the Agile software movement in North India when he set up the Indian business unit for the Dutch software consulting company Xebia. He led the growth of Xebia India as the managing director of the company for over 6 years and made the company a well-known name in the Agile consulting space in India. He also started the Agile NCR Conference, which has become a heavily visited annual event on Agile best practices, in the New Delhi Capital Region. Anurag became active in the big data space when he joined ING Bank in Amsterdam as the manager of the customer intelligence department, where he set up their first Hadoop cluster and implemented several transformative technologies, such as Netezza and R, in his department. He is now active in the payment technology and APIs, using technologies such as Node.js and MongoDB. Anurag loves to cycle on the reclaimed island of Flevoland in the Netherlands. He also likes listening to Hindi film music.
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Profile icon Sudheesh Narayan
Sudheesh Narayan
Sudheesh Narayanan is a Technology Strategist and Big Data Practitioner with expertise in technology consulting and implementing Big Data solutions. With over 15 years of IT experience in Information Management, Business Intelligence, Big Data & Analytics, and Cloud & J2EE application development, he provided his expertise in architecting, designing, and developing Big Data products, Cloud management platforms, and highly scalable platform services. His expertise in Big Data includes Hadoop and its ecosystem components, NoSQL databases (MongoDB, Cassandra, and HBase), Text Analytics (GATE and OpenNLP), Machine Learning (Mahout, Weka, and R), and Complex Event Processing. Sudheesh is currently working with Genpact as the Assistant Vice President and Chief Architect – Big Data, with focus on driving innovation and building Intellectual Property assets, frameworks, and solutions. Prior to Genpact, he was the co-inventor and Chief Architect of the Infosys BigDataEdge product.
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Profile icon Deshpande
Deshpande
Tanmay Deshpande is a Hadoop and big data evangelist. He currently works with Schlumberger as a Big Data Architect in Pune, India. He has interest in a wide range of technologies, such as Hadoop, Hive, Pig, NoSQL databases, Mahout, Sqoop, Java, cloud computing, and so on. He has vast experience in application development in various domains, such as oil and gas, finance, telecom, manufacturing, security, and retail. He enjoys solving machine-learning problems and spends his time reading anything that he can get his hands on. He has great interest in open source technologies and has been promoting them through his talks. Before Schlumberger, he worked with Symantec, Lumiata, and Infosys. Through his innovative thinking and dynamic leadership, he has successfully completed various projects. He regularly blogs on his website http://hadooptutorials.co.in. You can connect with him on LinkedIn at https://www.linkedin.com/in/deshpandetanmay/. He has also authored Mastering DynamoDB, published in August 2014, DynamoDB Cookbook, published in September 2015, Hadoop Real World Solutions Cookbook-Second Edition, published in March 2016, Hadoop: Data Processing and Modelling, published in August, 2016, and Hadoop Blueprints, published in September 2016, all by Packt Publishing.
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Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.