Get up and running with OpenStack Swift, the free, open source solution for deploying high-performance object storage clusters at scale. In this practical guide, Joe Arnold, co-founder and CEO of SwiftStack, brings you up-to-speed on the basic concepts of object storage and walks you through what you need to know to plan, build, operate, and measure the performance of your own Swift storage system. Object storage is essential today with the growth of web, mobile, and software-as-a-service (SaaS) applications. This book helps you through the process, with separate sections on application development, installation, administration, and troubleshooting. Learn Swift’s concepts for organizing, distributing, and serving data Explore basic and advanced features of the Swift RESTful API Delve into Swift’s many client libraries, including useful Python features Write middleware to customize and simplify your storage system Understand requirements for planning a Swift deployment—including your specific use case Learn options for coaxing the best performance from your cluster Get best practices for daily operations, such as monitoring and planning capacity additions Pick up techniques for testing and benchmarking your Swift cluster

If you are an IT administrator and you want to enter the world of cloud storage using OpenStack Swift, then this book is ideal for you. Basic knowledge of Linux and server technology is beneficial to get the most out of the book.

OpenStack was created with the audacious goal of being the ubiquitous software choice for building public and private cloud infrastructures. In just over a year, it's become the most talked-about project in open source. This concise book introduces OpenStack's general design and primary software components in detail, and shows you how to start using it to build cloud infrastructures. If you're a developer, technologist, or system administrator familiar with cloud offerings such as Rackspace Cloud or Amazon Web Services, Deploying OpenStack shows you how to obtain and deploy OpenStack software in a few controlled scenarios. Learn about OpenStack Compute (known as “Nova”), OpenStack Object Store ("Swift"), and OpenStack Image Service ("Glance") Understand common pitfalls in architecting, deploying, and implementing your cloud infrastructure with OpenStack Determine which version of the OpenStack code base best suits your deployment needs Define your deployment scenario and finalize key design choices Install Nova on a single node with either the StackOps distro or an Ubuntu package Be familiar with important configuration options and important administrative commands

An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.

Because of the explosion of unstructured data that is generated by individuals and organizations, a new storage paradigm that is called object storage has been developed. Object storage stores data in a flat namespace that scales to trillions of objects. The design of object storage also simplifies how users access data, supporting new types of applications and allowing users to access data by using various methods, including mobile devices and web applications. Data distribution and management are also simplified, allowing greater collaboration across the globe. OpenStack Swift is an emerging open source object storage software platform that is widely used for cloud storage. IBM® Spectrum Scale, which is based on IBM General Parallel File System (IBM GPFSTM) technology, is a high-performance and proven product that is used to store data for thousands of mission-critical commercial installations worldwide. Throughout this IBM RedpaperTM publication, IBM SpectrumTM Scale is used to refer to GPFS. The examples in this paper are based on IBM Spectrum ScaleTM V4.2.2. IBM Spectrum Scale also automates common storage management tasks, such as tiering and archiving at scale. Together, IBM Spectrum Scale and OpenStack Swift provide an enterprise-class object storage solution that efficiently stores, distributes, and retains critical data. This paper provides instructions about setting up and configuring IBM Spectrum Scale Object Storage that is based on OpenStack Swift. It also provides an initial set of preferred practices that ensure optimal performance and reliability. This paper is intended for administrators who are familiar with IBM Spectrum Scale and OpenStack Swift components.

This book constitutes the refereed conference proceedings of the 31st British International Conference on Databases, BICOD 2017 - formerly known as BNCOD (British National Conference on Databases) - held in London, UK, in July 2017. The 17 revised full papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics such as data cleansing, data integration, data wrangling, data mining and knowledge discovery, graph data and knowledge graphs, intelligent data analysis, approximate and flexible querying, data provenance and ontology-based data access. They are organized in the following topical sections: data wrangling and data integration; data analysis and data mining; graph data querying and analysis; multidimensional data and data quality; and distributed and multimedia data management.

This IBM® RedpaperTM publication takes you on a journey that surveys cloud computing to answer several fundamental questions about storage cloud technology. What are storage clouds? How can a storage cloud help solve your current and future data storage business requirements? What can IBM do to help you implement a storage cloud solution that addresses these needs? This paper shows how IBM storage clouds use the extensive cloud computing experience, services, proven technologies, and products of IBM to support a smart storage cloud solution designed for your storage optimization efforts. Clients face many common storage challenges and some have variations that make them unique. It describes various successful client storage cloud implementations and the options that are available to meet your current needs and position you to avoid storage issues in the future. IBM CloudTM Services (IBM Cloud Managed Services® and IBM SoftLayer®) are highlighted as well as the contributions of IBM to OpenStack cloud storage. This paper is intended for anyone who wants to learn about storage clouds and how IBM addresses data storage challenges with smart storage cloud solutions. It is suitable for IBM clients, storage solution integrators, and IBM specialist sales representatives.

"A stereotype of computer science textbooks is that they are dry, boring, and sometimes even intimidating. As a result, they turn students’ interests off from the subject matter instead of enticing them into it. This textbook is the opposite of such a stereotype. The author presents the subject matter in a refreshing story-telling style and aims to bring the Internet-generation of students closer to her stories." --Yingcai Xiao, The University of Akron Introduction to Middleware: Web Services, Object Components, and Cloud Computing provides a comparison of different middleware technologies and the overarching middleware concepts they are based on. The various major paradigms of middleware are introduced and their pros and cons are discussed. This includes modern cloud interfaces, including the utility of Service Oriented Architectures. The text discusses pros and cons of RESTful vs. non-RESTful web services, and also compares these to older but still heavily used distributed object/component middleware. The text guides readers to select an appropriate middleware technology to use for any given task, and to learn new middleware technologies as they appear over time without being greatly overwhelmed by any new concept. The book begins with an introduction to different distributed computing paradigms, and a review of the different kinds of architectures, architectural styles/patterns, and properties that various researchers have used in the past to examine distributed applications and determine the quality of distributed applications. Then it includes appropriate background material in networking and the web, security, and encoding necessary to understand detailed discussion in this area. The major middleware paradigms are compared, and a comparison methodology is developed. Readers will learn how to select a paradigm and technology for a particular task, after reading this text. Detailed middleware technology review sections allow students or industry practitioners working to expand their knowledge to achieve practical skills based on real projects so as to become well-functional in that technology in industry. Major technologies examined include: RESTful web services (RESTful cloud interfaces such as OpenStack, AWS EC2 interface, CloudStack; AJAX, JAX-RS, ASP.NET MVC and ASP.NET Core), non-RESTful (SOAP and WSDL-based) web services (JAX-WS, Windows Communication Foundation), distributed objects/ components (Enterprise Java Beans, .NET Remoting, CORBA). The book presents two projects that can be used to illustrate the practical use of middleware, and provides implementations of these projects over different technologies. This versatile and class-tested textbook is suitable (depending on chapters selected) for undergraduate or first-year graduate courses on client server architectures, middleware, and cloud computing, web services, and web programming.

There’s a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you’ll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform. Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects. You’ll explore the vast landscape of tools available in the Hadoop and big data realm in a thorough technical primer before diving into: Infrastructure: Look at all component layers in a modern data platform, from the server to the data center, to establish a solid foundation for data in your enterprise Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT Taking Hadoop to the cloud: Learn the important architectural aspects of running a big data platform in the cloud while maintaining enterprise security and high availability

Today, new business models in the marketplace coexist with traditional ones and their well-established IT architectures. They generate new business needs and new IT requirements that can only be satisfied by new service models and new technological approaches. These changes are reshaping traditional IT concepts. Cloud in its three main variants (Public, Hybrid, and Private) represents the major and most viable answer to those IT requirements, and software-defined infrastructure (SDI) is its major technological enabler. IBM® technology, with its rich and complete set of storage hardware and software products, supports SDI both in an open standard framework and in other vendors' environments. IBM services are able to deliver solutions to the customers with their extensive knowledge of the topic and the experiences gained in partnership with clients. This IBM RedpaperTM publication focuses on software-defined storage (SDS) and IBM Storage Systems product offerings for software-defined environments (SDEs). It also provides use case examples across various industries that cover different client needs, proposed solutions, and results. This paper can help you to understand current organizational capabilities and challenges, and to identify specific business objectives to be achieved by implementing an SDS solution in your enterprise.