Data Management At Scale

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Data Management at Scale

Author : Piethein Strengholt
Publisher : O'Reilly Media
Page : 348 pages
File Size : 49,6 Mb
Release : 2020-07-29
Category : Computers
ISBN : 9781492054757

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Data Management at Scale by Piethein Strengholt Pdf

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 at Scale

Author : Piethein Strengholt
Publisher : "O'Reilly Media, Inc."
Page : 412 pages
File Size : 50,9 Mb
Release : 2023-04-10
Category : Computers
ISBN : 9781098138837

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Data Management at Scale by Piethein Strengholt Pdf

As data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable. Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization. Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabric Go deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data product design, and more Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata

Data Management at Scale

Author : Piethein Strengholt
Publisher : "O'Reilly Media, Inc."
Page : 404 pages
File Size : 43,7 Mb
Release : 2020-07-29
Category : Computers
ISBN : 9781492054733

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Data Management at Scale by Piethein Strengholt Pdf

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

Web-Scale Data Management for the Cloud

Author : Wolfgang Lehner,Kai-Uwe Sattler
Publisher : Springer Science & Business Media
Page : 193 pages
File Size : 47,5 Mb
Release : 2013-04-06
Category : Computers
ISBN : 9781461468561

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Web-Scale Data Management for the Cloud by Wolfgang Lehner,Kai-Uwe Sattler Pdf

The efficient management of a consistent and integrated database is a central task in modern IT and highly relevant for science and industry. Hardly any critical enterprise solution comes without any functionality for managing data in its different forms. Web-Scale Data Management for the Cloud addresses fundamental challenges posed by the need and desire to provide database functionality in the context of the Database as a Service (DBaaS) paradigm for database outsourcing. This book also discusses the motivation of the new paradigm of cloud computing, and its impact to data outsourcing and service-oriented computing in data-intensive applications. Techniques with respect to the support in the current cloud environments, major challenges, and future trends are covered in the last section of this book. A survey addressing the techniques and special requirements for building database services are provided in this book as well.

Data Mesh

Author : Zhamak Dehghani
Publisher : "O'Reilly Media, Inc."
Page : 379 pages
File Size : 45,8 Mb
Release : 2022-03-08
Category : Computers
ISBN : 9781492092346

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Data Mesh by Zhamak Dehghani Pdf

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale. Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance. Get a complete introduction to data mesh principles and its constituents Design a data mesh architecture Guide a data mesh strategy and execution Navigate organizational design to a decentralized data ownership model Move beyond traditional data warehouses and lakes to a distributed data mesh

Cloud Data Management

Author : Liang Zhao,Sherif Sakr,Anna Liu,Athman Bouguettaya
Publisher : Springer
Page : 202 pages
File Size : 48,6 Mb
Release : 2014-07-08
Category : Computers
ISBN : 9783319047652

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Cloud Data Management by Liang Zhao,Sherif Sakr,Anna Liu,Athman Bouguettaya Pdf

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.

Frontiers in Massive Data Analysis

Author : 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
Publisher : National Academies Press
Page : 190 pages
File Size : 44,8 Mb
Release : 2013-09-03
Category : Mathematics
ISBN : 9780309287814

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Frontiers in Massive Data Analysis by 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 Pdf

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.

Model Management and Analytics for Large Scale Systems

Author : Bedir Tekinerdogan,Önder Babur,Loek Cleophas,Mark van den Brand,Mehmet Aksit
Publisher : Academic Press
Page : 344 pages
File Size : 50,9 Mb
Release : 2019-09-14
Category : Computers
ISBN : 9780128166505

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Model Management and Analytics for Large Scale Systems by Bedir Tekinerdogan,Önder Babur,Loek Cleophas,Mark van den Brand,Mehmet Aksit Pdf

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management. Identifies key problems and offers solution approaches and tools that have been developed or are necessary for model management and analytics Explores basic theory and background, current research topics, related challenges and the research directions for model management and analytics Provides a complete overview of model management and analytics frameworks, the different types of analytics (descriptive, diagnostics, predictive and prescriptive), the required modelling and method steps, and important future directions

Web Data Management

Author : Serge Abiteboul,Ioana Manolescu,Philippe Rigaux,Marie-Christine Rousset,Pierre Senellart
Publisher : Cambridge University Press
Page : 128 pages
File Size : 49,6 Mb
Release : 2011-11-28
Category : Computers
ISBN : 9781139505055

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Web Data Management by Serge Abiteboul,Ioana Manolescu,Philippe Rigaux,Marie-Christine Rousset,Pierre Senellart Pdf

The Internet and World Wide Web have revolutionized access to information. Users now store information across multiple platforms from personal computers to smartphones and websites. As a consequence, data management concepts, methods and techniques are increasingly focused on distribution concerns. Now that information largely resides in the network, so do the tools that process this information. This book explains the foundations of XML with a focus on data distribution. It covers the many facets of distributed data management on the Web, such as description logics, that are already emerging in today's data integration applications and herald tomorrow's semantic Web. It also introduces the machinery used to manipulate the unprecedented amount of data collected on the Web. Several 'Putting into Practice' chapters describe detailed practical applications of the technologies and techniques. The book will serve as an introduction to the new, global, information systems for Web professionals and master's level courses.

The Open Handbook of Linguistic Data Management

Author : Andrea L. Berez-Kroeker,Bradley McDonnell,Eve Koller,Lauren B. Collister
Publisher : MIT Press
Page : 687 pages
File Size : 55,5 Mb
Release : 2022-01-18
Category : Language Arts & Disciplines
ISBN : 9780262362177

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The Open Handbook of Linguistic Data Management by Andrea L. Berez-Kroeker,Bradley McDonnell,Eve Koller,Lauren B. Collister Pdf

A guide to principles and methods for the management, archiving, sharing, and citing of linguistic research data, especially digital data. "Doing language science" depends on collecting, transcribing, annotating, analyzing, storing, and sharing linguistic research data. This volume offers a guide to linguistic data management, engaging with current trends toward the transformation of linguistics into a more data-driven and reproducible scientific endeavor. It offers both principles and methods, presenting the conceptual foundations of linguistic data management and a series of case studies, each of which demonstrates a concrete application of abstract principles in a current practice. In part 1, contributors bring together knowledge from information science, archiving, and data stewardship relevant to linguistic data management. Topics covered include implementation principles, archiving data, finding and using datasets, and the valuation of time and effort involved in data management. Part 2 presents snapshots of practices across various subfields, with each chapter presenting a unique data management project with generalizable guidance for researchers. The Open Handbook of Linguistic Data Management is an essential addition to the toolkit of every linguist, guiding researchers toward making their data FAIR: Findable, Accessible, Interoperable, and Reusable.

Large Scale and Big Data

Author : Sherif Sakr,Mohamed Gaber
Publisher : CRC Press
Page : 640 pages
File Size : 40,8 Mb
Release : 2014-06-25
Category : Computers
ISBN : 9781466581500

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Large Scale and Big Data by Sherif Sakr,Mohamed Gaber Pdf

Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.

Big Data Management, Technologies, and Applications

Author : Hu, Wen-Chen
Publisher : IGI Global
Page : 342 pages
File Size : 55,6 Mb
Release : 2013-10-31
Category : Computers
ISBN : 9781466647008

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Big Data Management, Technologies, and Applications by Hu, Wen-Chen Pdf

"This book discusses the exponential growth of information size and the innovative methods for data capture, storage, sharing, and analysis for big data"--Provided by publisher.

Linked Data Management

Author : Andreas Harth,Katja Hose,Ralf Schenkel
Publisher : CRC Press
Page : 566 pages
File Size : 51,6 Mb
Release : 2016-04-19
Category : Computers
ISBN : 9781466582415

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Linked Data Management by Andreas Harth,Katja Hose,Ralf Schenkel Pdf

Linked Data Management presents techniques for querying and managing Linked Data that is available on today's Web. The book shows how the abundance of Linked Data can serve as fertile ground for research and commercial applications.The text focuses on aspects of managing large-scale collections of Linked Data. It offers a detailed introduction to L

Modern Enterprise Business Intelligence and Data Management

Author : Alan Simon
Publisher : Morgan Kaufmann
Page : 96 pages
File Size : 46,7 Mb
Release : 2014-08-28
Category : Computers
ISBN : 9780128017456

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Modern Enterprise Business Intelligence and Data Management by Alan Simon Pdf

Nearly every large corporation and governmental agency is taking a fresh look at their current enterprise-scale business intelligence (BI) and data warehousing implementations at the dawn of the "Big Data Era"...and most see a critical need to revitalize their current capabilities. Whether they find the frustrating and business-impeding continuation of a long-standing "silos of data" problem, or an over-reliance on static production reports at the expense of predictive analytics and other true business intelligence capabilities, or a lack of progress in achieving the long-sought-after enterprise-wide "single version of the truth" – or all of the above – IT Directors, strategists, and architects find that they need to go back to the drawing board and produce a brand new BI/data warehousing roadmap to help move their enterprises from their current state to one where the promises of emerging technologies and a generation’s worth of best practices can finally deliver high-impact, architecturally evolvable enterprise-scale business intelligence and data warehousing. Author Alan Simon, whose BI and data warehousing experience dates back to the late 1970s and who has personally delivered or led more than thirty enterprise-wide BI/data warehousing roadmap engagements since the mid-1990s, details a comprehensive step-by-step approach to building a best practices-driven, multi-year roadmap in the quest for architecturally evolvable BI and data warehousing at the enterprise scale. Simon addresses the triad of technology, work processes, and organizational/human factors considerations in a manner that blends the visionary and the pragmatic. Takes a fresh look at true enterprise-scale BI/DW in the "Dawn of the Big Data Era" Details a checklist-based approach to surveying one’s current state and identifying which components are enterprise-ready and which ones are impeding the key objectives of enterprise-scale BI/DW Provides an approach for how to analyze and test-bed emerging technologies and architectures and then figure out how to include the relevant ones in the roadmaps that will be developed Presents a tried-and-true methodology for building a phased, incremental, and iterative enterprise BI/DW roadmap that is closely aligned with an organization’s business imperatives, organizational culture, and other considerations

Managing Data in Motion

Author : April Reeve
Publisher : Newnes
Page : 204 pages
File Size : 47,5 Mb
Release : 2013-02-26
Category : Computers
ISBN : 9780123977915

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Managing Data in Motion by April Reeve Pdf

Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types Explains, in non-technical terms, the architecture and components required to perform data integration Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"