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| data management and storage: The Holy Grail of Data Storage Management Jon William Toigo, 2000 This book discusses and develops models intended for the reader as a starting point in conceptulizing, planning, integrating, and managing storage capabilities in a distributed environment. |
| data management and storage: Data Management for Researchers Kristin Briney, 2015-09-01 A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline. —Robert Buntrock, Chemical Information Bulletin |
| data management and storage: Information Storage and Management EMC Education Services, 2012-04-30 The new edition of a bestseller, now revised and update throughout! This new edition of the unparalleled bestseller serves as a full training course all in one and as the world's largest data storage company, EMC is the ideal author for such a critical resource. They cover the components of a storage system and the different storage system models while also offering essential new material that explores the advances in existing technologies and the emergence of the Cloud as well as updates and vital information on new technologies. Features a separate section on emerging area of cloud computing Covers new technologies such as: data de-duplication, unified storage, continuous data protection technology, virtual provisioning, FCoE, flash drives, storage tiering, big data, and more Details storage models such as Network Attached Storage (NAS), Storage Area Network (SAN), Object Based Storage along with virtualization at various infrastructure components Explores Business Continuity and Security in physical and virtualized environment Includes an enhanced Appendix for additional information This authoritative guide is essential for getting up to speed on the newest advances in information storage and management. |
| data management and storage: Big Data Fei Hu, 2016-04-27 Although there are already some books published on Big Data, most of them only cover basic concepts and society impacts and ignore the internal implementation details-making them unsuitable to R&D people. To fill such a need, Big Data: Storage, Sharing, and Security examines Big Data management from an R&D perspective. It covers the 3S desi |
| data management and storage: Advanced Data Management Lena Wiese, 2015-10-29 Advanced data management has always been at the core of efficient database and information systems. Recent trends like big data and cloud computing have aggravated the need for sophisticated and flexible data storage and processing solutions. This book provides a comprehensive coverage of the principles of data management developed in the last decades with a focus on data structures and query languages. It treats a wealth of different data models and surveys the foundations of structuring, processing, storing and querying data according these models. Starting off with the topic of database design, it further discusses weaknesses of the relational data model, and then proceeds to convey the basics of graph data, tree-structured XML data, key-value pairs and nested, semi-structured JSON data, columnar and record-oriented data as well as object-oriented data. The final chapters round the book off with an analysis of fragmentation, replication and consistency strategies for data management in distributed databases as well as recommendations for handling polyglot persistence in multi-model databases and multi-database architectures. While primarily geared towards students of Master-level courses in Computer Science and related areas, this book may also be of benefit to practitioners looking for a reference book on data modeling and query processing. It provides both theoretical depth and a concise treatment of open source technologies currently on the market. |
| data management and storage: Information Storage and Management EMC Education Services, 2010-01-06 The spiraling growth of digital information makes the ISM book a must have addition to your IT reference library. This exponential growth has driven information management technology to new levels of sophistication and complexity, exposing a skills gap that challenge IT managers and professionals alike. The ISM book, written by storage professionals from EMC Corporation, takes an ‘open’ approach to teaching information storage and management, focusing on concepts and principles – rather that product specifics – that can be applied in all IT environments The book enables existing and aspiring IT professionals, students, faculty, and those simply wishing to gain deeper insight to this emerging pillar of IT infrastructure to achieve a comprehensive understanding of all segments of information storage technology. Sixteen chapters are organized into four sections. Advanced topics build upon the topics learned in previous chapters. Section 1, Information Storage and Management for Today’s World: Four chapters cover information growth and challenges, define a storage system and its environment, review the evolution of storage technology, and introduce intelligent storage systems. Section 2, Storage Options and Protocols: Six chapters cover the SCSI and Fibre channel architecture, direct-attached storage (DAS), storage area networks (SANs), network-attached storage (NAS), Internet Protocol SAN (IP-SAN), content-addressed storage (CAS), and storage virtualization. Section 3, Business Continuity and Replication: Four chapters introduce business continuity, backup and recovery, local data replication, and remote data replication. Section 4, Security and Administration: Two chapters cover storage security and storage infrastructure monitoring and management. The book’s supplementary web site provides up-to-date information on additional learning aids and storage certification opportunities. |
| data management and storage: Data Management at Scale Piethein Strengholt, 2020-07-29 As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| data management and storage: Data Access and Storage Management for Embedded Programmable Processors Francky Catthoor, K. Danckaert, 2002-03-31 Índice abreviado: 1. DTSE in programmable architectures 2. Related compiler work on data transfer and storage management 3. Global loop transformations 4. System-level storage requirement estimation 5. Automated data reuse exploration techniques 6. Storage cycle budget distribution 7. Cache optimization 8. Demonstrator designs 9. Conclussions and future work. |
| data management and storage: Scientific Data Management Arie Shoshani, Doron Rotem, 2019-08-30 Dealing with the volume, complexity, and diversity of data currently being generated by scientific experiments and simulations often causes scientists to waste productive time. Scientific Data Management: Challenges, Technology, and Deployment describes cutting-edge technologies and solutions for managing and analyzing vast amounts of data, helping scientists focus on their scientific goals. The book begins with coverage of efficient storage systems, discussing how to write and read large volumes of data without slowing the simulation, analysis, or visualization processes. It then focuses on the efficient data movement and management of storage spaces and explores emerging database systems for scientific data. The book also addresses how to best organize data for analysis purposes, how to effectively conduct searches over large datasets, how to successfully automate multistep scientific process workflows, and how to automatically collect metadata and lineage information. This book provides a comprehensive understanding of the latest techniques for managing data during scientific exploration processes, from data generation to data analysis. Enhanced by numerous detailed color images, it includes real-world examples of applications drawn from biology, ecology, geology, climatology, and more. Check out Dr. Shoshani discuss the book during an interview with International Science Grid This Week (iSGTW): http: //www.isgtw.org/?pid=1002259 |
| data management and storage: Storage Management in Data Centers Volker Herminghaus, Albrecht Scriba, 2009-04-21 Storage Management in Data Centers helps administrators tackle the complexity of data center mass storage. It shows how to exploit the potential of Veritas Storage Foundation by conveying information about the design concepts of the software as well as its architectural background. Rather than merely showing how to use Storage Foundation, it explains why to use it in a particular way, along with what goes on inside. Chapters are split into three sections: An introductory part for the novice user, a full-featured part for the experienced, and a technical deep dive for the seasoned expert. An extensive troubleshooting section shows how to fix problems with volumes, plexes, disks and disk groups. A snapshot chapter gives detailed instructions on how to use the most advanced point-in-time copies. A tuning chapter will help you speed up and benchmark your volumes. And a special chapter on split data centers discusses latency issues as well as remote mirroring mechanisms and cross-site volume maintenance. All topics are covered with the technical know how gathered from an aggregate thirty years of experience in consulting and training in data centers all over the world. |
| data management and storage: Effective Document and Data Management Bob Wiggins, 2016-04-29 Effective Document and Data Management illustrates the operational and strategic significance of how documents and data are captured, managed and utilized. Without a coherent and consistent approach the efficiency and effectiveness of the organization may be undermined by less poor management and use of its information. The third edition of the book is restructured to take this broader view and to establish an organizational context in which information is management. Along the way Bob Wiggins clarifies the distinction between information management, data management and knowledge management; helps make sense of the concept of an information life cycle to present and describe the processes and techniques of information and data management, storage and retrieval; uses worked examples to illustrate the coordinated application of data and process analysis; and provides guidance on the application of appropriate project management techniques for document and records management projects. The book will benefit a range of organizations and people, from those senior managers who need to develop coherent and consistent business and IT strategies; to information professionals, such as records managers and librarians who will gain an appreciation of the impact of the technology and of how their particular areas of expertise can best be applied; to system designers, developers and implementers and finally to users. The author can be contacted at curabyte@gmail.com for further information. |
| data management and storage: Principles of Database Management Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens, 2018-07-12 Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science. |
| data management and storage: Exploring Research Data Management Andrew Cox, Eddy Verbaan, 2018-05-11 Research Data Management (RDM) has become a professional topic of great importance internationally following changes in scholarship and government policies about the sharing of research data. Exploring Research Data Management provides an accessible introduction and guide to RDM with engaging tasks for the reader to follow and develop their knowledge. Starting by exploring the world of research and the importance and complexity of data in the research process, the book considers how a multi-professional support service can be created then examines the decisions that need to be made in designing different types of research data service from local policy creation, training, through to creating a data repository. Coverage includes: A discussion of the drivers and barriers to RDM Institutional policy and making the case for Research Data Services Practical data management Data literacy and training researchers Ethics and research data services Case studies and practical advice from working in a Research Data Service. This book will be useful reading for librarians and other support professionals who are interested in learning more about RDM and developing Research Data Services in their own institution. It will also be of value to students on librarianship, archives, and information management courses studying topics such as RDM, digital curation, data literacies and open science. |
| data management and storage: Design Patterns for Cloud Native Applications Kasun Indrasiri, Sriskandarajah Suhothayan, 2021-05-17 With the immense cost savings and scalability the cloud provides, the rationale for building cloud native applications is no longer in question. The real issue is how. With this practical guide, developers will learn about the most commonly used design patterns for building cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development. You'll learn how to incrementally design, develop, and deploy large and effective cloud native applications that you can manage and maintain at scale with minimal cost, time, and effort. Authors Kasun Indrasiri and Sriskandarajah Suhothayan highlight use cases that effectively demonstrate the challenges you might encounter at each step. Learn the fundamentals of cloud native applications Explore key cloud native communication, connectivity, and composition patterns Learn decentralized data management techniques Use event-driven architecture to build distributed and scalable cloud native applications Explore the most commonly used patterns for API management and consumption Examine some of the tools and technologies you'll need for building cloud native systems |
| data management and storage: Frontiers in Massive Data Analysis National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Applied and Theoretical Statistics, Committee on the Analysis of Massive Data, 2013-09-03 Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data. |
| data management and storage: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration |
| data management and storage: Core Data Marcus Zarra, 2013 Core Data is Apple's recommended way to persist data: it's easy to use, built-in, and integrated with iCloud. It's intricate, powerful, and necessary--and this book is your guide to harnessing its power. Learn fundamental Core Data principles such as thread and memory management, discover how to use Core Data in your iPhone, iPad, and OS X projects by using NSPredicate to filter data, and see how to add iCloud to your applications. Cocoa expert Marcus Zarra walks you through developing a full-featured application based around the Core Data APIs. You'll build up a single application throughout the book, learning key Core Data principles such as NSPredicate, thread management, and memory management. Geared toward intermediate to advanced developers, this book gets you comfortable with the basics of Core Data. Then you'll delve deep into the details of the API. You'll explore not only how to get Core Data integrated into your application properly, but even better, how to work with the API's flexibility to create convenience methods to improve your application's maintainability. Learn how to reduce your number of mapping models, integrate your Core Data app with Spotlight and Quick Look, connect your application with sync services, and find out how to use Core Data in a multithreaded environment. By the end of the book, you'll have built a full-featured application, gained a complete understanding of Core Data, and learned how to integrate your application into the iPhone/iPad platform. This second edition updates all examples for OS X Mountain Lion and iOS 6, gets you up to speed on changes in multithreading, and provides new chapters covering iCloud and NSFetchedResultsController. What You NeedMac OS X Mountain Lion and iOS 6. This book is for intermediate-level iOS developers. |
| data management and storage: Using IBM Spectrum Copy Data Management with IBM FlashSystem A9000 or A9000R and SAP HANA Axel Westphal, Bert Dufrasne, Markus Oscheka, IBM Redbooks, 2017-08-29 Data is the currency of the new economy, and organizations are increasingly tasked with finding better ways to protect, recover, access, share, and use it. IBM SpectrumTM Copy Data Management is aimed at using existing data in a manner that is efficient, automated, scalable. It helps you manage all of those snapshot and IBM FlashCopy® images made to support DevOps, data protection, disaster recovery, and Hybrid Cloud computing environments. This IBM® RedpaperTM publication specifically addresses IBM Spectrum Copy Data Management in combination with IBM FlashSystem® A9000 or A9000R when used for Automated Disaster Recovery of SAP HANA. |
| data management and storage: Secure Searchable Encryption and Data Management Brij B. Gupta, Mamta, 2021-03-16 With the advent of the IT revolution, the volume of data produced has increased exponentially and is still showing an upward trend. This data may be abundant and enormous, but it’s a precious resource and should be managed properly. Cloud technology plays an important role in data management. Storing data in the cloud rather than on local storage has many benefits, but apart from these benefits, there are privacy concerns in storing sensitive data over third-party servers. These concerns can be addressed by storing data in an encrypted form; however, while encryption solves the problem of privacy, it engenders other serious issues, including the infeasibility of the fundamental search operation and a reduction in flexibility when sharing data with other users, amongst others. The concept of searchable encryption addresses these issues. This book provides every necessary detail required to develop a secure, searchable encryption scheme using both symmetric and asymmetric cryptographic primitives along with the appropriate security models to ensure the minimum security requirements for real-world applications. |
| data management and storage: Storage Virtualization Tom Clark, 2005 Storage virtualization has come of age, offering IT professionals powerful new ways to simplify infrastructure, streamline management, improve utilization, and reduce costs. Now, the author of the best-selling storage books IP SANs and Designing Storage Area Networks presents an up-to-the-minute, vendor-neutral overview of storage virtualization in all its forms. |
| data management and storage: Big Data Management Peter Ghavami, 2020-11-09 Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations. |
| data management and storage: In-Memory Data Management Hasso Plattner, Alexander Zeier, 2011-03-08 In the last 50 years the world has been completely transformed through the use of IT. We have now reached a new inflection point. Here we present, for the first time, how in-memory computing is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Analytical data resides in warehouses, synchronized periodically with transactional systems. This separation makes flexible, real-time reporting on current data impossible. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. We describe techniques that allow analytical and transactional processing at the speed of thought and enable new ways of doing business. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes by leveraging in-memory computing. |
| data management and storage: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data management and storage: Data Management for Social Scientists Nils B. Weidmann, 2023-03-09 Equips social scientists with the tools and techniques to conduct quantitative research in the age of big data. |
| data management and storage: Network Storage James O'Reilly, 2016-10-14 Network Storage: Tools and Technologies for Storing Your Company's Data explains the changes occurring in storage, what they mean, and how to negotiate the minefields of conflicting technologies that litter the storage arena, all in an effort to help IT managers create a solid foundation for coming decades. The book begins with an overview of the current state of storage and its evolution from the network perspective, looking closely at the different protocols and connection schemes and how they differentiate in use case and operational behavior. The book explores the software changes that are motivating this evolution, ranging from data management, to in-stream processing and storage in virtual systems, and changes in the decades-old OS stack. It explores Software-Defined Storage as a way to construct storage networks, the impact of Big Data, high-performance computing, and the cloud on storage networking. As networks and data integrity are intertwined, the book looks at how data is split up and moved to the various appliances holding that dataset and its impact. Because data security is often neglected, users will find a comprehensive discussion on security issues that offers remedies that can be applied. The book concludes with a look at technologies on the horizon that will impact storage and its networks, such as NVDIMMs, The Hybrid Memory Cube, VSANs, and NAND Killers. - Puts all the new developments in storage networking in a clear perspective for near-term and long-term planning - Offers a complete overview of storage networking, serving as a go-to resource for creating a coherent implementation plan - Provides the details needed to understand the area, and clears a path through the confusion and hype that surrounds such a radical revolution of the industry |
| data management and storage: NoSQL Distilled Pramod J. Sadalage, Martin Fowler, 2013 'NoSQL Distilled' is designed to provide you with enough background on how NoSQL databases work, so that you can choose the right data store without having to trawl the whole web to do it. It won't answer your questions definitively, but it should narrow down the range of options you have to consider. |
| data management and storage: Spatial Data Management Nikos Mamoulis, 2012 Spatial database management deals with the storage, indexing, and querying of data with spatial features, such as location and geometric extent. Many applications require the efficient management of spatial data, including Geographic Information Systems, Computer Aided Design, and Location Based Services. The goal of this book is to provide the reader with an overview of spatial data management technology, with an emphasis on indexing and search techniques. It first introduces spatial data models and queries and discusses the main issues of extending a database system to support spatial data. It presents indexing approaches for spatial data, with a focus on the R-tree. Query evaluation and optimization techniques for the most popular spatial query types (selections, nearest neighbor search, and spatial joins) are portrayed for data in Euclidean spaces and spatial networks. The book concludes by demonstrating the ample application of spatial data management technology on a wide range of related application domains: management of spatio-temporal data and high-dimensional feature vectors, multi-criteria ranking, data mining and OLAP, privacy-preserving data publishing, and spatial keyword search. Table of Contents: Introduction / Spatial Data / Indexing / Spatial Query Evaluation / Spatial Networks / Applications of Spatial Data Management Technology |
| data management and storage: Database Cloud Storage Nitin Vengurlekar, Prasad Bagal, 2013-07-06 Implement a Centralized Cloud Storage Infrastructure with Oracle Automatic Storage Management Build and manage a scalable, highly available cloud storage solution. Filled with detailed examples and best practices, this Oracle Press guide explains how to set up a complete cloud-based storage system using Oracle Automatic Storage Management. Find out how to prepare hardware, build disk groups, efficiently allocate storage space, and handle security. Database Cloud Storage: The Essential Guide to Oracle Automatic Storage Management shows how to monitor your system, maximize throughput, and ensure consistency across servers and clusters. Set up and configure Oracle Automatic Storage Management Discover and manage disks and establish disk groups Create, clone, and administer Oracle databases Consolidate resources with Oracle Private Database Cloud Control access, encrypt files, and assign user privileges Integrate replication, file tagging, and automatic failover Employ pre-engineered private cloud database consolidation tools Check for data consistency and resync failed disks Code examples in the book are available for download |
| data management and storage: Data Management for Researchers Kristin Briney, 2015 A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem - an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle - a framework for data's place within the research process and how data's role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management - covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data - an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data - explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis - covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data - many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage - deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data - digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data - addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data - as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline. --Robert Buntrock, Chemical Information Bulletin |
| data management and storage: Developments in Data Storage S. N. Piramanayagam, Tow C. Chong, 2011-10-11 A timely text on the recent developments in data storage, from a materials perspective Ever-increasing amounts of data storage on hard disk have been made possible largely due to the immense technological advances in the field of data storage materials. Developments in Data Storage: Materials Perspective covers the recent progress and developments in recording technologies, including the emerging non-volatile memory, which could potentially become storage technologies of the future. Featuring contributions from experts around the globe, this book provides engineers and graduate students in materials science and electrical engineering a solid foundation for grasping the subject. The book begins with the basics of magnetism and recording technology, setting the stage for the following chapters on existing methods and related research topics. These chapters focus on perpendicular recording media to underscore the current trend of hard disk media; read sensors, with descriptions of their fundamental principles and challenges; and write head, which addresses the advanced concepts for writing data in magnetic recording. Two chapters are devoted to the highly challenging area in hard disk drives of tribology, which deals with reliability, corrosion, and wear-resistance of the head and media. Next, the book provides an overview of the emerging technologies, such as heat-assisted magnetic recording and bit-patterned media recording. Non-volatile memory has emerged as a promising alternative storage option for certain device applications; two chapters are dedicated to non-volatile memory technologies such as the phase-change and the magnetic random access memories. With a strong focus on the fundamentals along with an overview of research topics, Developments in Data Storage is an ideal reference for graduate students or beginners in the field of magnetic recording. It also serves as an invaluable reference for future storage technologies including non-volatile memories. |
| data management and storage: Cloud Data Management Liang Zhao, Sherif Sakr, Anna Liu, Athman Bouguettaya, 2016-09-03 In practice, the design and architecture of a cloud varies among cloud providers. We present a generic evaluation framework for the performance, availability and reliability characteristics of various cloud platforms. We describe a generic benchmark architecture for cloud databases, specifically NoSQL database as a service. It measures the performance of replication delay and monetary cost. Service Level Agreements (SLA) represent the contract which captures the agreed upon guarantees between a service provider and its customers. The specifications of existing service level agreements (SLA) for cloud services are not designed to flexibly handle even relatively straightforward performance and technical requirements of consumer applications. We present a novel approach for SLA-based management of cloud-hosted databases from the consumer perspective and an end-to-end framework for consumer-centric SLA management of cloud-hosted databases. The framework facilitates adaptive and dynamic provisioning of the database tier of the software applications based on application-defined policies for satisfying their own SLA performance requirements, avoiding the cost of any SLA violation and controlling the monetary cost of the allocated computing resources. In this framework, the SLA of the consumer applications are declaratively defined in terms of goals which are subjected to a number of constraints that are specific to the application requirements. The framework continuously monitors the application-defined SLA and automatically triggers the execution of necessary corrective actions (scaling out/in the database tier) when required. The framework is database platform-agnostic, uses virtualization-based database replication mechanisms and requires zero source code changes of the cloud-hosted software applications. |
| data management and storage: Data Center Handbook Hwaiyu Geng, 2014-12-22 Provides the fundamentals, technologies, and best practices in designing, constructing and managing mission critical, energy efficient data centers Organizations in need of high-speed connectivity and nonstop systems operations depend upon data centers for a range of deployment solutions. A data center is a facility used to house computer systems and associated components, such as telecommunications and storage systems. It generally includes multiple power sources, redundant data communications connections, environmental controls (e.g., air conditioning, fire suppression) and security devices. With contributions from an international list of experts, The Data Center Handbook instructs readers to: Prepare strategic plan that includes location plan, site selection, roadmap and capacity planning Design and build green data centers, with mission critical and energy-efficient infrastructure Apply best practices to reduce energy consumption and carbon emissions Apply IT technologies such as cloud and virtualization Manage data centers in order to sustain operations with minimum costs Prepare and practice disaster reovery and business continuity plan The book imparts essential knowledge needed to implement data center design and construction, apply IT technologies, and continually improve data center operations. |
| data management and storage: Web Data Management Practices Athena Vakali, George Pallis, 2007-01-01 This book provides an understanding of major issues, current practices and the main ideas in the field of Web data management, helping readers to identify current and emerging issues, as well as future trends. The most important aspects are discussed: Web data mining, content management on the Web, Web applications and Web services--Provided by publisher. |
| data management and storage: Big Data Fei Hu, 2016 Examining Big Data management from an R&D perspective, this thorough resource covers the 3S designs?storage, sharing, and security?through detailed descriptions of Big Data concepts and implementations. -- |
| data management and storage: Patterns in Data Management Jens Dittrich, 2016-02-03 This book is not a standard textbook. This book was written extending and complementing preexisting educational videos I designed and recorded in winter 2013/14. The main goal of these videos was to use them in my flipped classroom Database Systems which is an intermediate-level university course designed for B.Sc. students in their third year or M.Sc. students of computer science and related disciplines. Though in general my students liked both the flipped classroom model and (most of) the videos, several students asked for an additional written script that would allow them to quickly lookup explanations for material in text that would otherwise be hard to re-find in the videos. Therefore, in spring 2015, I started working on such a course script which more and more evolved into something that I feel comfortable calling it a book. One central question I had to confront was: would I repeat all material from the videos in the textbook? In other words, would the book be designed to work without the videos? I quickly realized that writing such an old-fashioned text-oriented book, a textbook, wouldn't be the appropriate thing to do anymore in 2015. My videos as well as the accompanying material are freely available to everyone anyways. And unless you are sitting on the local train from Saarbr�cken to Neustadt, you will almost always have Internet access to watch them. In fact, downloading the videos in advance isn't terribly hard anyway. This observation changed the original purpose of what this book would be good for: not so much the primary source of the course's content, but a different view on that content, explaining that content where possible in other words. In addition, one goal was to be concise in the textual explanations allowing you to quickly re-find and remember things you learned from the videos without going through a large body of text. |
| data management and storage: Cloud Database Development and Management Lee Chao, 2013-07-26 Although today’s job market requires IT professionals to understand cloud computing theories and have hands-on skills for developing real-world database systems, there are few books available that integrate coverage of both. Filling this void, Cloud Database Development and Management explains how readers can take advantage of the cloud environment to develop their own fully functioning database systems without any additional investment in IT infrastructure. Filled with step-by-step instructions, examples, and hands-on projects, the book begins by providing readers with the required foundation in database systems and cloud-based database development tools. It supplies detailed instructions on setting up data storage on Windows Azure and also explains how readers can develop their own virtual machines with Windows Server 2012 as the guest operating system. The book’s wide-ranging coverage includes database design, database implementation, database deployment to the cloud environment, SQL Database, Table Storage service, Blob Storage service, Queue Storage service, and database application development. The text deals with all three aspects of database design: conceptual design, logical design, and physical design. It introduces the SQL language, explains how to use SQL to create database objects, and introduces the migration of the database between Windows Azure and the on-premises SQL Server. It also discusses the management tasks that keep both SQL Database and Windows Azure running smoothly. Detailing how to design, implement, and manage database systems in the cloud, the book provides you with tools that can make your cloud database development much more efficient and flexible. Its easy-to-follow instructions will help you develop the hands-on skills needed to store and manage critical business information and to make that data available anytime through the Internet. |
| data management and storage: Agile Data Warehouse Design Lawrence Corr, Jim Stagnitto, 2011-11 Agile Data Warehouse Design is a step-by-step guide for capturing data warehousing/business intelligence (DW/BI) requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling + brainstorming) with BI stakeholders. This book describes BEAM✲, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team. BEAM✲ provides tools and techniques that will encourage DW/BI designers and developers to move away from their keyboards and entity relationship based tools and model interactively with their colleagues. The result is everyone thinks dimensionally from the outset! Developers understand how to efficiently implement dimensional modeling solutions. Business stakeholders feel ownership of the data warehouse they have created, and can already imagine how they will use it to answer their business questions. Within this book, you will learn: ✲ Agile dimensional modeling using Business Event Analysis & Modeling (BEAM✲) ✲ Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun! ✲ Telling dimensional data stories using the 7Ws (who, what, when, where, how many, why and how) ✲ Modeling by example not abstraction; using data story themes, not crow's feet, to describe detail ✲ Storyboarding the data warehouse to discover conformed dimensions and plan iterative development ✲ Visual modeling: sketching timelines, charts and grids to model complex process measurement - simply ✲ Agile design documentation: enhancing star schemas with BEAM✲ dimensional shorthand notation ✲ Solving difficult DW/BI performance and usability problems with proven dimensional design patterns Lawrence Corr is a data warehouse designer and educator. As Principal of DecisionOne Consulting, he helps clients to review and simplify their data warehouse designs, and advises vendors on visual data modeling techniques. He regularly teaches agile dimensional modeling courses worldwide and has taught dimensional DW/BI skills to thousands of students. Jim Stagnitto is a data warehouse and master data management architect specializing in the healthcare, financial services, and information service industries. He is the founder of the data warehousing and data mining consulting firm Llumino. |
| data management and storage: Storage Networking Fundamentals Marc Farley, 2005 Unlike networking technology, where there is already a great deal of literature available, many professionals still need to understand the basic building blocks of storage networking. This book provides vendor-neutral, independent analysis and terminology. |
| data management and storage: Teaching Research Data Management Julia Bauder, 2022-01-03 Armed with this guide's strategies and concrete examples, subject librarians, data services librarians, and scholarly communication librarians will be inspired to roll up their sleeves and get involved with teaching research data management competencies to students and faculty. The usefulness of research data management skills bridges numerous activities, from data-driven scholarship and open research by faculty to documentation for grant reporting. And undergrads need a solid foundation in data management for future academic success. This collection gathers practitioners from a broad range of academic libraries to describe their services and instruction around research data. You will learn about such topics as integrating research data management into information literacy instruction; threshold concepts for novice learners of data management; four key competencies that are entry points for library-faculty collaboration in data instruction; an 8-step plan for outreach to faculty and grad students in engineering and the sciences; using RStudio to teach data management, data visualization, and research reproducibility; expanding data management instruction with adaptable modules for remote learning; designing a data management workshop series; developing a research guide on data types, open data repositories, and data storage; creating a data management plan assignment for STEM undergraduates; and data management training to ensure compliance with grant requirements. |
| data management and storage: Oracle Automatic Storage Management: Under-the-Hood & Practical Deployment Guide Nitin Vengurlekar, Murali Vallath, Rich Long, 2007-11-26 Build and manage a scalable storage infrastructure with Oracle Automatic Storage Management Streamline data management and provisioning using Oracle Automatic Storage Management (Oracle ASM) and the detailed information contained in this exclusive Oracle Press resource. Written by a team of database experts, Oracle Automatic Storage Management: Under-the-Hood & Practical Deployment Guide explains how to build and maintain a dynamic, highly available Oracle database storage environment. Inside, you'll learn how to configure storage for Oracle ASM, build disk groups, use data striping and mirroring, and optimize performance. You'll also learn how to ensure consistency across server and storage platforms, maximize data redundancy, and administer Oracle ASM from the command line. Manage Oracle ASM Instances and configure Oracle RDBMS instances to leverage Oracle ASM Define, discover, and manage disk storage under Oracle ASM Create external, normal-redundancy, and high-redundancy disk groups Add and remove Oracle ASM storage without affecting RDMS instance availability Learn how Oracle ASM provides even I/O distribution Work with Oracle ASM directories, files, templates, and aliases Improve storage performance and integrity using the ASMLIB API Simplify system administration with the Oracle ASM command line interface Understand key internal Oracle ASM structures and algorithms |
What Is Data Management? Examples, Strategies & Challenges
Sep 15, 2023 · Data Warehouse Management–involves the collection and storage of data from multiple sources into a central repository, making it easier to analyze and report on. Data …
Data Storage Management | 7 Best Practices | ESF - Enterprise …
Apr 2, 2018 · Enterprise data storage management is easier said than done. The problem is, storage managers have a lot going on. Managing systems, dealing with IT and end-users, and everyday …
Top Trends in Data Storage Management for 2023 - Enterprise …
Jun 9, 2023 · 5. Automated Data Management. David Liberman, founder and CTO of productscience.ai, sees automation in data collection and storage optimization as a major trend …
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Jul 24, 2023 · Those looking for a data management platform that supports enterprises in implementing and maintaining strong data governance practices are advised to consider object …
6 Best Data Storage Solutions and Software for 2023
Sep 25, 2023 · While storage solutions and software encompass a very broad range of data management products, there are a few common functions that most storage systems should …
Best Storage Management Software
Jan 19, 2021 · Storage management software is a critical component of the IT landscape, providing tools and services that help organizations to deploy, manage and optimize data storage assets. …
10 Data Storage Tips | How to Improve Your Storage | ESF
Jan 28, 2015 · Enterprise Storage Forum offers practical information on data storage and protection from several different perspectives: hardware, software, on-premises services and cloud …
What Is Cold Data Storage? Storing Cold Data in the Cloud
Mar 25, 2021 · Cloud-based data storage generally falls into four storage classes or tiers: Hot storage is primary storage for frequently accessed production data. Warm storage stores slightly …
10 Top Data Storage Certifications - Enterprise Storage Forum
Sep 25, 2023 · Data storage and management services company NetApp offers multiple training and certifications for data professionals. The NetApp Certified Data Administrator program is …
12 Best Practices for Enterprise Data Storage Security
Aug 22, 2023 · It might even be natural if a flood or fire damages an office space. Data storage security means taking all of these possibilities into account and preparing for them. Here are 12 …
What Is Data Management? Examples, Strategies & Challenges
Sep 15, 2023 · Data Warehouse Management–involves the collection and storage of data from multiple sources into a central repository, making it easier to analyze and report on. Data …
Data Storage Management | 7 Best Practices | ESF - Enterprise …
Apr 2, 2018 · Enterprise data storage management is easier said than done. The problem is, storage managers have a lot going on. Managing systems, dealing with IT and end-users, and …
Top Trends in Data Storage Management for 2023 - Enterprise …
Jun 9, 2023 · 5. Automated Data Management. David Liberman, founder and CTO of productscience.ai, sees automation in data collection and storage optimization as a major …
Data Governance Trends and Best Practices for Storage …
Jul 24, 2023 · Those looking for a data management platform that supports enterprises in implementing and maintaining strong data governance practices are advised to consider object …
6 Best Data Storage Solutions and Software for 2023
Sep 25, 2023 · While storage solutions and software encompass a very broad range of data management products, there are a few common functions that most storage systems should …
Best Storage Management Software
Jan 19, 2021 · Storage management software is a critical component of the IT landscape, providing tools and services that help organizations to deploy, manage and optimize data …
10 Data Storage Tips | How to Improve Your Storage | ESF
Jan 28, 2015 · Enterprise Storage Forum offers practical information on data storage and protection from several different perspectives: hardware, software, on-premises services and …
What Is Cold Data Storage? Storing Cold Data in the Cloud
Mar 25, 2021 · Cloud-based data storage generally falls into four storage classes or tiers: Hot storage is primary storage for frequently accessed production data. Warm storage stores …
10 Top Data Storage Certifications - Enterprise Storage Forum
Sep 25, 2023 · Data storage and management services company NetApp offers multiple training and certifications for data professionals. The NetApp Certified Data Administrator program is …
12 Best Practices for Enterprise Data Storage Security
Aug 22, 2023 · It might even be natural if a flood or fire damages an office space. Data storage security means taking all of these possibilities into account and preparing for them. Here are 12 …
