Java实现大数据分析实战：案例研究与Hadoop技术

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"《大数据分析与Java》是一本深入讲解如何在大数据环境下应用Java进行数据分析的专业书籍。书中涵盖了四个实际案例研究，包括对推特数据的情感分析、电影推荐系统在MovieLens数据集上的实现、电子商务数据集上的客户细分以及对真实航班数据的图分析。作为一本完整的指南，它引导读者从头到尾掌握在大数据领域使用Java进行数据处理、存储、分析和机器学习的技术。章节一探讨了大数据分析的重要性，强调了大数据对Java开发人员的价值，尤其是在职业发展中的优势。接着，作者介绍了Hadoop项目的基础，Hadoop分布式计算平台是Java的重要子项目，用于处理海量数据。书中详细解释了Hadoop分布式文件系统（HDFS）的概念和架构，包括其主要组件，如NameNode、DataNode和BlockManager，并展示了基本的HDFS操作命令。 Apache Spark作为Hadoop生态系统中的一个重要组件，被重点介绍。Spark的概念、变换（Transformations）和行动（Actions）是核心概念，通过Spark Java API，读者可以学习如何使用Java 8编写Spark程序。书中还展示了如何加载数据、进行数据清洗和预处理、执行聚合操作，如计数、投影、分组和最大/最小值，以及RDD（Resilient Distributed Datasets）的操作，如配对RDD和变换。此外，本书还涵盖了如何将分析结果保存，以及如何在Hadoop集群上运行Spark程序。除了Spark本身，书还提到了Spark的一些子项目，如Spark Machine Learning，为读者展示了如何利用Spark进行机器学习任务。《大数据分析与Java》适合希望提升大数据处理能力的Java开发者，无论他们是初学者还是有经验的专业人士，都能从中获得实用的知识和实战经验。书中提供的案例和代码示例有助于读者快速理解和实践大数据分析技术。通过阅读这本书，读者将能够熟练运用Java在大数据分析领域施展才华，从而提升在当今竞争激烈的IT行业中的一项关键技能。"

Preface

Even as you read this content, there is a revolution happening behind the scenes in the field of

big data. From every coffee that you pick up from a coffee store to everything you click or

purchase online, almost every transaction, click, or choice of yours is getting analyzed. From

this analysis, a lot of deductions are now being made to offer you new stuff and better choices

according to your likes. These techniques and associated technologies are picking up so fast

that as developers we all should be a part of this new wave in the field of software. This

would allow us better prospects in our careers, as well as enhance our skill set to directly

impact the business we work for.

Earlier technologies such as machine learning and artificial intelligence used to sit in the labs

of many PhD students. But with the rise of big data, these technologies have gone mainstream

now. So, using these technologies, you can now predict which advertisement the user is going

to click on next, or which product they would like to buy, or it can also show whether the

image of a tumor is cancerous or not. The opportunities here are vast. Big data in itself

consists of a whole lot of technologies whether cluster computing frameworks such as

Apache Spark or Tez or distributed filesystems such as HDFS and Amazon S3 or real-time

SQL on underlying data using Impala or Spark SQL.

This book provides a lot of information on big data technologies, including machine learning,

graph analytics, real-time analytics and an introductory chapter on deep learning as well. I

have tried to cover both technical and conceptual aspects of these technologies. In doing so, I

have used many real-world case studies to depict how these technologies can be used in real

life. So this book will teach you how to run a fast algorithm on the transactional data

available on an e-commerce site to figure out which items sell together, or how to run a page

rank algorithm on a flight dataset to figure out the most important airports in a country based

on air traffic. There are many content gems like these in the book for readers.

What this book covers

Chapter 1, Big Data Analytics with Java, starts with providing an introduction to the core

concepts of Hadoop and provides information on its key components. In easy-to-understand

explanations, it shows how the components fit together and gives simple examples on the

usage of the core components HDFS and Apache Spark. This chapter also talks about the

different sources of data that can put their data inside Hadoop, their compression formats, and

the systems that are used to analyze that data.

Chapter 2, First Steps in Data Analysis, takes the first steps towards the field of analytics on

big data. We start with a simple example covering basic statistical analytic steps, followed by

two popular algorithms for building association rules using the Apriori Algorithm and the FP-

Growth Algorithm. For all case studies, we have used realistic examples of an online e-

commerce store to give insights to users as to how these algorithms can be used in the real

world.

Chapter 3, Data Visualization, helps you to understand what different types of charts there

are for data analysis, how to use them, and why. With this understanding, we can make better

decisions when exploring our data. This chapter also contains lots of code samples to show

the different types of charts built using Apache Spark and the JFreeChart library.

Chapter 4, Basics of Machine Learning, helps you to understand the basic theoretical

concepts behind machine learning, such as what exactly is machine learning, how it is used,

examples of its use in real life, and the different forms of machine learning. If you are new to

the field of machine learning, or want to brush up your existing knowledge on it, this chapter

is for you. Here I will also show how, as a developer, you should approach a machine

learning problem, including topics on feature extraction, feature selection, model testing,

model selection, and more.

Chapter 5, Regression on Big Data, explains how you can use linear regression to predict

continuous values and how you can do binary classification using logistic regression. A real-

world case study of house price evaluation based on the different features of the house is used

to explain the concepts of linear regression. To explain the key concepts of logistic

regression, a real-life case study of detecting heart disease in a patient based on different

features is used.

Chapter 6, Naive Bayes and Sentimental Analysis, explains a probabilistic machine learning

model called Naive Bayes and also briefly explains another popular model called the support

vector machine. The chapter starts with basic concepts such as Bayes Theorem and then

explains how these concepts are used in Naive Bayes. I then use the model to predict the

sentiment whether positive or negative in a set of tweets from Twitter. The same case study is

then re-run using the support vector machine model.

Chapter 7, Decision Trees, explains that decision trees are like flowcharts and can be

programmatically built using concepts such as Entropy or Gini Impurity. The golden egg in

this chapter is a case study that shows how we can predict whether a person's loan application

will be approved or not using decision trees.

Chapter 8, Ensembling on Big Data, explains how ensembling plays a major role in

improving the performance of the predictive results. I cover different concepts related to

ensembling in this chapter, including techniques such as how multiple models can be joined

together using bagging or boosting thereby enhancing the predictive outputs. We also cover

the highly popular and accurate ensemble of models, random forests and gradient-boosted

trees. Finally, we predict loan default by users in a dataset of a real-world Lending Club (a

real online lending company) using these models.

Chapter 9, Recommendation Systems, covers the particular concept that has made machine

learning so popular and it directly impacts business as well. In this chapter, we show what

recommendation systems are, what they can do, and how they are built using machine

learning. We cover both types of recommendation systems: content-based and collaborative,

and also cover their good and bad points. Finally, we cover two case studies using the

MovieLens dataset to show recommendations to users for movies that they might like to see.

Chapter 10, Clustering and Customer Segmentation on Big Data, speaks about clustering and

how it can be used by a real-world e-commerce store to segment their customers based on

how valuable they are. I have covered both k-Means clustering and bisecting k-Means

clustering, and used both of them in the corresponding case study on customer segmentation.

Chapter 11, Massive Graphs on Big Data, covers an interesting topic, graph analytics. We

start with a refresher on graphs, with basic concepts, and later go on to explore the different

forms of analytics that can be run on the graphs, whether path-based analytics involving

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