* Covers the most up-to-date versions of the technology. * Teaches subject using real world tutorials. * Subject is taught in a very concise, no-nonsense fashion, wasting little time on history and other fluff. Book is therefore compact and portable. It gives you just what you need. * Demystifies what is sometimes thought of as an intimidating subject. * Covers Apache for Windows, Mac/Unix, and Linux. * Full book support available online and via e-mail.

This book is a step-by-step tutorial for anyone wanting to learn Apache Tomcat 7 from scratch. There are plenty of illustrations and examples to escalate you from a novice to an expert with minimal strain. If you are a J2EE administrator, migration administrator, technical architect, or a project manager for a web hosting domain, and are interested in Apache Tomcat 7, then this book is for you. If you are someone responsible for installation, configuration, and management of Tomcat 7, then too, this book will be of help to you.

This book takes you on a fantastic journey to discover the attributes of big data using Apache Hive. Key Features Grasp the skills needed to write efficient Hive queries to analyze the Big Data Discover how Hive can coexist and work with other tools within the Hadoop ecosystem Uses practical, example-oriented scenarios to cover all the newly released features of Apache Hive 2.3.3 Book Description In this book, we prepare you for your journey into big data by frstly introducing you to backgrounds in the big data domain, alongwith the process of setting up and getting familiar with your Hive working environment. Next, the book guides you through discovering and transforming the values of big data with the help of examples. It also hones your skills in using the Hive language in an effcient manner. Toward the end, the book focuses on advanced topics, such as performance, security, and extensions in Hive, which will guide you on exciting adventures on this worthwhile big data journey. By the end of the book, you will be familiar with Hive and able to work effeciently to find solutions to big data problems What you will learn Create and set up the Hive environment Discover how to use Hive's definition language to describe data Discover interesting data by joining and filtering datasets in Hive Transform data by using Hive sorting, ordering, and functions Aggregate and sample data in different ways Boost Hive query performance and enhance data security in Hive Customize Hive to your needs by using user-defined functions and integrate it with other tools Who this book is for If you are a data analyst, developer, or simply someone who wants to quickly get started with Hive to explore and analyze Big Data in Hadoop, this is the book for you. Since Hive is an SQL-like language, some previous experience with SQL will be useful to get the most out of this book.

Whether you are a novice to ZooKeeper or already have some experience, you will be able to master the concepts of ZooKeeper and its usage with ease. This book assumes you to have some prior knowledge of distributed systems and high-level programming knowledge of C, Java, or Python, but no experience with Apache ZooKeeper is required.

If you are a data analyst, developer, or simply someone who wants to use Hive to explore and analyze data in Hadoop, this is the book for you. Whether you are new to big data or an expert, with this book, you will be able to master both the basic and the advanced features of Hive. Since Hive is an SQL-like language, some previous experience with the SQL language and databases is useful to have a better understanding of this book.

Create your own massively scalable Cassandra database with highly responsive database queries About This Book Create a Cassandra cluster and tweak its configuration to get the best performance based on your environment Analyze the key concepts and architecture of Cassandra, which are essential to create highly responsive Cassandra databases A fast-paced and step-by-step guide on handling huge amount of data and getting the best out of your database applications Who This Book Is For If you are a developer who is working with Cassandra and you want to deep dive into the core concepts and understand Cassandra's non-relational nature, then this book is for you. A basic understanding of Cassandra is expected. What You Will Learn Install and set up your Cassandra Cluster using various installation types Use Cassandra Query Language (CQL) to design Cassandra database and tables with various configuration options Design your Cassandra database to be evenly loaded with the lowest read/write latencies Employ the available Cassandra tools to monitor and maintain a Cassandra cluster Debug CQL queries to discover why they are performing relatively slowly Choose the best-suited compaction strategy for your database based on your usage pattern Tune Cassandra based on your deployment operation system environment In Detail Apache Cassandra Essentials takes you step-by-step from from the basics of installation to advanced installation options and database design techniques. It gives you all the information you need to effectively design a well distributed and high performance database. You'll get to know about the steps that are performed by a Cassandra node when you execute a read/write query, which is essential to properly maintain of a Cassandra cluster and to debug any issues. Next, you'll discover how to integrate a Cassandra driver in your applications and perform read/write operations. Finally, you'll learn about the various tools provided by Cassandra for serviceability aspects such as logging, metrics, backup, and recovery. Style and approach This step-by-step guide is packed with examples that explain the core concepts as well as advanced concepts, techniques, and usages of Apache Cassandra.

Apache Mahout is a scalable machine learning library with algorithms for clustering, classification, and recommendations. It empowers users to analyze patterns in large, diverse, and complex datasets faster and more scalably. This book is an all-inclusive guide to analyzing large and complex datasets using Apache Mahout. It explains complicated but very effective machine learning algorithms simply, in relation to real-world practical examples. Starting from the fundamental concepts of machine learning and Apache Mahout, this book guides you through Apache Mahout's implementations of machine learning techniques including classification, clustering, and recommendations. During this exciting walkthrough, real-world applications, a diverse range of popular algorithms and their implementations, code examples, evaluation strategies, and best practices are given for each technique. Finally, you will learn vdata visualization techniques for Apache Mahout to bring your data to life.