The Big Data eBook Bundle

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.

• Access 482 pages & 14 hours and 27 minutes of content 24/7
• Explore the new features of Hadoop 3 along with HDFS, YARN, and MapReduce
• Get well-versed with the analytical capabilities of Hadoop ecosystem using practical examples
• Learn how to integrate Hadoop with R and Python for more efficient big data processing
• Learn to use Hadoop with Apache Spark and Apache Flink for real-time data analytics
• Explore setting up a Hadoop cluster on AWS cloud
• Dive into performing big data analytics on AWS using Elastic Map Reduce

With organizations handling large amounts of data on a regular basis, MySQL has become a popular solution to handle this structured Big Data. In this book, you will see how DBAs can use MySQL 8 to handle billions of records, and load and retrieve data with performance comparable or superior to commercial DB solutions with higher costs. Many organizations today depend on MySQL for their websites and a Big Data solution for their data archiving, storage, and analysis needs. By the end of this book, you will know how to efficiently use MySQL 8 to manage data for your Big Data applications.

• Access 266 pages & 7 hours and 58 minutes of content 24/7
• Explore the features of MySQL 8 & how they can be leveraged to handle Big Data
• Unlock the new features of MySQL 8 for managing structured & unstructured Big Data
• Dive into integrating MySQL 8 & Hadoop for efficient data processing
• Learn how to perform aggregation using MySQL 8 for optimum data utilization
• Explore different kinds of join & union in MySQL 8 to process Big Data efficiently
• Walk through accelerating Big Data processing w/ Memcached
• Learn how to integrate MySQL w/ the NoSQL API
• Explore implementing replication to build highly available solutions for Big Data

The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users. This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. By the end of this book, you will have all the knowledge you need to build expert Big Data systems.

• Access 394 pages & 11 hours and 49 minutes of content 24/7
• Learn how to build an efficient enterprise Big Data strategy centered around Apache Hadoop
• Gain a thorough understanding of using Hadoop w/ various Big Data frameworks such as Apache Spark, Elasticsearch & more
• Learn how to set up & deploy your Big Data environment on premises or on the cloud w/ Apache Ambari
• Explore designing effective streaming data pipelines & building your own enterprise search solutions

SAS has been recognized by Money Magazine and Payscale as one of the top business skills to learn in order to advance one’s career. Through innovative data management, analytics, and business intelligence software and services, SAS helps customers solve their business problems by allowing them to make better decisions faster. This book introduces the reader to the SAS and how they can use SAS to perform efficient analysis on any size data, including Big Data.

• Access 266 pages & 7 hours & 58 minutes of content 24/7
• Explore combining SAS w/ platforms such as Hadoop, SAP HANA & Cloud Foundry-based platforms for efficient Big Data analytics
• Learn how to use the web browser-based SAS Studio & iPython Jupyter Notebook interfaces with SAS
• Dive into practical, real-world examples on predictive modeling, forecasting, optimizing & reporting your Big Data analysis w/ SAS

Big Data analytics is the process of examining large and complex data sets that often exceed computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. This book will begin with a brief look at the Big Data world and its current industry standards, with an introduction to the R language and presenting its development, structure, applications in the real world, and its shortcomings.

• Access 506 pages & 15 hours and 10 minutes of content 24/7
• Learn about the current state of Big Data processing using the R programming language & its powerful statistical capabilities
• Explore deploying Big Data analytics platforms w/ selected Big Data tools supported by R in a cost-effective & time-saving manner
• Walk through applying the R language to real-world Big Data problems on a multi-node Hadoop cluster
• Explore the compatibility of R with Hadoop, Spark, SQL & NoSQL databases, and H2O platform

This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This title is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world.

• Access 418 pages & 12 hours and 32 minutes of content 24/7
• Start from simple analytic tasks on big data
• Get into more complex tasks w/ predictive analytics on big data using machine learning
• Learn real-time analytics tasks
• Understand the concepts with examples and case studies
• Learn how to prepare & refine data for analysis
• Explore creating charts in order to understand data

In this age of big data, companies have a larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for Big Data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis.

• Access 383 pages & 11 hours and 31 minutes of content 24/7
• Learn how to manage Artificial Intelligence techniques for big data w/ Java
• Explore building smart systems to analyze data for enhanced customer experience
• Learn to use Artificial Intelligence frameworks for big data
• Understand complex problems w/ algorithms & Neuro-Fuzzy systems
• Learn to design stratagems to leverage data using Machine Learning process
• Explore applying Deep Learning techniques to prepare data for modeling

Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization’s data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that.

• Access 412 pages & 12 hours and 21 minutes of content 24/7
• Get a 360-degree view into the world of Big Data, data science & machine learning
• Dive into a broad range of technical & business Big Data analytics topics that caters to the interests of the technical experts as well as corporate IT executives
• Get hands-on experience w/ industry-standard Big Data & machine learning tools such as Hadoop, Spark, MongoDB, KDB+ and R
• Learn how to create production-grade machine learning BI Dashboards using R & R Shiny w/ step-by-step instructions

Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.

• Access 898 pages & 26 hours and 56 minutes of content 24/7
• Understand object-oriented & functional programming concepts of Scala
• Develop an in-depth understanding of Scala collection APIs
• Explore working w/ RDD & DataFrame to learn Spark’s core abstractions
• Explore analyzing structured & unstructured data using SparkSQL and GraphX
• Dive into Scalable & fault-tolerant streaming application development using Spark structured streaming

Frank Kane’s Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you’ll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses.

• Access 296 pages & 8 hours and 52 minutes of content 24/7
• Find out how you can identify Big Data problems as Spark problems
• Learn how to install & run Apache Spark on your computer or on a cluster
• Dive into Analyzing large data sets across many CPUs using Spark’s Resilient Distributed Datasets
• Explore Implementing machine learning on Spark using the MLlib library
• Learn how to process continuous streams of data in real time using the Spark streaming module
• Explore performing complex network analysis using Spark’s GraphX library
• Learn to use Amazon’s Elastic MapReduce service to run your Spark jobs on a cluster