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You can choose several data access frameworks when building Java enterprise applications that work with relational databases. But what about big data? This hands-on introduction shows you how Spring Data makes it relatively easy to build applications across a wide range of new data access technologies such as NoSQL and Hadoop. Through several sample projects, you’ll learn how Spring Data provides a consistent programming model that retains NoSQL-specific features and capabilities, and helps you develop Hadoop applications across a wide range of use-cases such as data analysis, event stream processing, and workflow. You’ll also discover the features Spring Data adds to Spring’s existing JPA and JDBC support for writing RDBMS-based data access layers. Learn about Spring’s template helper classes to simplify the use of database-specific functionality Explore Spring Data’s repository abstraction and advanced query functionality Use Spring Data with Redis (key/value store), HBase (column-family), MongoDB (document database), and Neo4j (graph database) Discover the GemFire distributed data grid solution Export Spring Data JPA-managed entities to the Web as RESTful web services Simplify the development of HBase applications, using a lightweight object-mapping framework Build example big-data pipelines with Spring Batch and Spring Integration
For more than 20 years, Network World has been the premier provider of information, intelligence and insight for network and IT executives responsible for the digital nervous systems of large organizations. Readers are responsible for designing, implementing and managing the voice, data and video systems their companies use to support everything from business critical applications to employee collaboration and electronic commerce.
Work with all aspects of batch processing in a modern Java environment using a selection of Spring frameworks. This book provides up-to-date examples using the latest configuration techniques based on Java configuration and Spring Boot. The Definitive Guide to Spring Batch takes you from the “Hello, World!” of batch processing to complex scenarios demonstrating cloud native techniques for developing batch applications to be run on modern platforms. Finally this book demonstrates how you can use areas of the Spring portfolio beyond just Spring Batch 4 to collaboratively develop mission-critical batch processes. You’ll see how a new class of use cases and platforms has evolved to have an impact on batch-processing. Data science and big data have become prominent in modern IT and the use of batch processing to orchestrate workloads has become commonplace. The Definitive Guide to Spring Batch covers how running finite tasks on cloud infrastructure in a standardized way has changed where batch applications are run. Additionally, you’ll discover how Spring Batch 4 takes advantage of Java 9, Spring Framework 5, and the new Spring Boot 2 micro-framework. After reading this book, you’ll be able to use Spring Boot to simplify the development of your own Spring projects, as well as take advantage of Spring Cloud Task and Spring Cloud Data Flow for added cloud native functionality. Includes a foreword by Dave Syer, Spring Batch project founder. What You'll LearnDiscover what is new in Spring Batch 4 Carry out finite batch processing in the cloud using the Spring Batch project Understand the newest configuration techniques based on Java configuration and Spring Boot using practical examples Master batch processing in complex scenarios including in the cloud Develop batch applications to be run on modern platforms Use areas of the Spring portfolio beyond Spring Batch to develop mission-critical batch processes Who This Book Is For Experienced Java and Spring coders new to the Spring Batch platform. This definitive book will be useful in allowing even experienced Spring Batch users and developers to maximize the Spring Batch tool.
This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. It also focuses on high level concepts such as definitions of Big Data from different angles; surveys in research and applications; and existing tools, mechanisms, and systems in practice. Each chapter is independent from the other chapters, allowing users to read any chapter directly. After examining the practical side of Big Data, this book presents theoretical perspectives. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Big Data Concepts, Theories and Applications is designed as a reference for researchers and advanced level students in computer science, electrical engineering and mathematics. Practitioners who focus on information systems, big data, data mining, business analysis and other related fields will also find this material valuable.
Manage vRealize operations manager 6.6 effectively using this comprehensive guide. Key Features Get complete control of capacity management in your virtual environment Display the most appropriate performance metrics and assemble your own dashboard Analyze and process data from different sources into a single repository Optimize vRealize Automation workload placement Book Description In the modern IT world, the criticality of managing the health, efficiency, and compliance of virtualized environments is more important than ever. With vRealize Operations Manager 6.6, you can make a difference to your business by being reactive rather than proactive. Mastering vRealize Operations Manager helps you streamline your processes and customize the environment to suit your needs. You will gain visibility across all devices in the network and retain full control. With easy-to-follow, step-by-step instructions and support images, you will quickly master the ability to manipulate your data and display it in a way that best suits you and your business or technical requirements. This book not only covers designing, installing, and upgrading vRealize Operations 6.6, but also gives you a deep understanding of its building blocks: badges, alerts, super metrics, views, dashboards, management packs, and plugins. With the new vRealize Operations 6.6 troubleshooting capabilities, capacity planning, intelligent workload placement, and additional monitoring capabilities, this book is aimed at ensuring you get the knowledge to manage your virtualized environment as effectively as possible. What you will learn Discover advanced vRealize Operations concepts and design your processes effectively for the underlying architecture Plan and install a new version or upgrade from a previous one Apply proven capacity management theories and techniques in practical, real-world environments Manipulate data and metrics to display them in the most effective way possible Create custom views and dashboards fit for any use case Explore how policies have evolved in vRealize Operations 6.6 and how to apply them in the most effective manner Who this book is for If you are an administrator of a virtual environment and have used vRealize Operations before but want to gain a professional understanding by easily implementing complex tasks with it, then this book is for you.
With this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market.Comparing and contrasting the dif
This book constitutes the refereed proceedings of the ACM/IFIP/USENIX 14th International Middleware Conference, held in Beijing, China, in December 2013. The 24 revised full papers presented were carefully reviewed and selected from 189 submissions. The papers cover a wide range of topics including design, implementation, deployment and evaluation of middleware for next-generation platforms such as cloud computing, social networks and large-scale storage and distributed systems. The middleware solutions introduced provide features such as availability, efficiency, scalability, fault-tolerance, trustworthy operation and support security and privacy needs.