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| data catalog vs metadata management: Universal Meta Data Models David Marco, Michael Jennings, 2004-03-25 * The heart of the book provides the complete set of models that will support most of an organization's core business functions, including universal meta models for enterprise-wide systems, business meta data and data stewardship, portfolio management, business rules, and XML, messaging, and transactions * Developers can directly adapt these models to their own businesses, saving countless hours of development time * Building effective meta data repositories is complicated and time-consuming, and few IT departments have the necessary expertise to do it right-which is why this book is sure to find a ready audience * Begins with a quick overview of the Meta Data Repository Environment and the business uses of meta data, then goes on to describe the technical architecture followed by the detailed models |
| data catalog vs metadata management: Introduction to Metadata , 2004 An overview of metadata: what it is, its types and uses, and how it can help to make Web resources more accessible and comprehensible. Contains articles, a glossary, and a list of acronyms relating to metadata. |
| data catalog vs metadata management: Non-Invasive Data Governance Robert S. Seiner, 2014-09-01 Data-governance programs focus on authority and accountability for the management of data as a valued organizational asset. Data Governance should not be about command-and-control, yet at times could become invasive or threatening to the work, people and culture of an organization. Non-Invasive Data Governance™ focuses on formalizing existing accountability for the management of data and improving formal communications, protection, and quality efforts through effective stewarding of data resources. Non-Invasive Data Governance will provide you with a complete set of tools to help you deliver a successful data governance program. Learn how: • Steward responsibilities can be identified and recognized, formalized, and engaged according to their existing responsibility rather than being assigned or handed to people as more work. • Governance of information can be applied to existing policies, standard operating procedures, practices, and methodologies, rather than being introduced or emphasized as new processes or methods. • Governance of information can support all data integration, risk management, business intelligence and master data management activities rather than imposing inconsistent rigor to these initiatives. • A practical and non-threatening approach can be applied to governing information and promoting stewardship of data as a cross-organization asset. • Best practices and key concepts of this non-threatening approach can be communicated effectively to leverage strengths and address opportunities to improve. |
| data catalog vs metadata management: Metadata Management with IBM InfoSphere Information Server Wei-Dong Zhu, Tuvia Alon, Gregory Arkus, Randy Duran, Marc Haber, Robert Liebke, Frank Morreale Jr., Itzhak Roth, Alan Sumano, IBM Redbooks, 2011-10-18 What do you know about your data? And how do you know what you know about your data? Information governance initiatives address corporate concerns about the quality and reliability of information in planning and decision-making processes. Metadata management refers to the tools, processes, and environment that are provided so that organizations can reliably and easily share, locate, and retrieve information from these systems. Enterprise-wide information integration projects integrate data from these systems to one location to generate required reports and analysis. During this type of implementation process, metadata management must be provided along each step to ensure that the final reports and analysis are from the right data sources, are complete, and have quality. This IBM® Redbooks® publication introduces the information governance initiative and highlights the immediate needs for metadata management. It explains how IBM InfoSphereTM Information Server provides a single unified platform and a collection of product modules and components so that organizations can understand, cleanse, transform, and deliver trustworthy and context-rich information. It describes a typical implementation process. It explains how InfoSphere Information Server provides the functions that are required to implement such a solution and, more importantly, to achieve metadata management. This book is for business leaders and IT architects with an overview of metadata management in information integration solution space. It also provides key technical details that IT professionals can use in a solution planning, design, and implementation process. |
| data catalog vs metadata management: Big Data Security Shibakali Gupta, Indradip Banerjee, Siddhartha Bhattacharyya, 2019-10-08 After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology. |
| data catalog vs metadata management: The Data Catalog Bonnie O'Neil, Lowell Fryman, 2020-03-16 Apply this definitive guide to data catalogs and select the feature set needed to empower your data citizens in their quest for faster time to insight. The data catalog may be the most important breakthrough in data management in the last decade, ranking alongside the advent of the data warehouse. The latter enabled business consumers to conduct their own analyses to obtain insights themselves. The data catalog is the next wave of this, empowering business users even further to drastically reduce time to insight, despite the rising tide of data flooding the enterprise. Use this book as a guide to provide a broad overview of the most popular Machine Learning (ML) data catalog products, and perform due diligence using the extensive features list. Consider graphical user interface (GUI) design issues such as layout and navigation, as well as scalability in terms of how the catalog will handle your current and anticipated data and metadata needs. ONeil & Frymanpresent a typology which ranges from products that focus on data lineage, curation and search, data governance, data preparation, and of course, the core capability of finding and understanding the data. The authors emphasize that machine learning is being adopted in many of these products, enabling a more elegant data democratization solution in the face of the burgeoning mountain of data that is engulfing organizations. Derek Strauss, Chairman/CEO, Gavroshe, and Former CDO, TD Ameritrade. This book is organized into three sections: Chapters 1 and 2 reveal the rationale for a data catalog and share how data scientists, data administrators, and curators fare with and without a data catalog; Chapters 3-10 present the many different types of data catalogs; Chapters 11 and 12 provide an extensive features list, current trends, and visions for the future. |
| data catalog vs metadata management: Towards Interoperable Research Infrastructures for Environmental and Earth Sciences Zhiming Zhao, Margareta Hellström, 2020-07-24 This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions. |
| data catalog vs metadata management: 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 catalog vs metadata management: The Enterprise Data Catalog Ole Olesen-Bagneux, 2023-02-15 Combing the web is simple, but how do you search for data at work? It's difficult and time-consuming, and can sometimes seem impossible. This book introduces a practical solution: the data catalog. Data analysts, data scientists, and data engineers will learn how to create true data discovery in their organizations, making the catalog a key enabler for data-driven innovation and data governance. Author Ole Olesen-Bagneux explains the benefits of implementing a data catalog. You'll learn how to organize data for your catalog, search for what you need, and manage data within the catalog. Written from a data management perspective and from a library and information science perspective, this book helps you: Learn what a data catalog is and how it can help your organization Organize data and its sources into domains and describe them with metadata Search data using very simple-to-complex search techniques and learn to browse in domains, data lineage, and graphs Manage the data in your company via a data catalog Implement a data catalog in a way that exactly matches the strategic priorities of your organization Understand what the future has in store for data catalogs |
| data catalog vs metadata management: Metadata Richard P. Smiraglia, 2005 Part 1 introduces metadata concepts(i. e. understanding metadata and its schemes; metadata and bibliographic control). Part 2 focuses on several metadata schemes such as Dublin Core. |
| data catalog vs metadata management: Metadata and Semantic Research Emmanouel Garoufallou, Andreas Vlachidis, 2023-08-09 This book constitutes the refereed post proceedings of the 16th Research Conference on Metadata and Semantic Research, MTSR 2022, held in London, UK, during November 7–11, 2022. The 21 full papers and 4 short papers included in this book were carefully reviewed andselected from 79 submissions. They were organized in topical sections as follows: metadata, linked data, semantics and ontologies - general session, and track on Knowledge IT Artifacts (KITA), Track on digital humanities and digital curation, and track on cultural collections and applications, track on digital libraries, information retrieval, big, linked, social & open data, and metadata, linked data, semantics and ontologies - general session, track on agriculture, food & environment, and metadata, linked Data, semantics and ontologies - general, track on open repositories, research information systems & data infrastructures, and metadata, linked data, semantics and ontologies - general, metadata, linked data, semantics and ontologies - general session, and track on european and national projects. |
| data catalog vs metadata management: The Definitive Guide to Data Integration Pierre-Yves BONNEFOY, Emeric CHAIZE, Raphaël MANSUY, Mehdi TAZI, 2024-03-29 Learn the essentials of data integration with this comprehensive guide, covering everything from sources to solutions, and discover the key to making the most of your data stack Key Features Learn how to leverage modern data stack tools and technologies for effective data integration Design and implement data integration solutions with practical advice and best practices Focus on modern technologies such as cloud-based architectures, real-time data processing, and open-source tools and technologies Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe Definitive Guide to Data Integration is an indispensable resource for navigating the complexities of modern data integration. Focusing on the latest tools, techniques, and best practices, this guide helps you master data integration and unleash the full potential of your data. This comprehensive guide begins by examining the challenges and key concepts of data integration, such as managing huge volumes of data and dealing with the different data types. You’ll gain a deep understanding of the modern data stack and its architecture, as well as the pivotal role of open-source technologies in shaping the data landscape. Delving into the layers of the modern data stack, you’ll cover data sources, types, storage, integration techniques, transformation, and processing. The book also offers insights into data exposition and APIs, ingestion and storage strategies, data preparation and analysis, workflow management, monitoring, data quality, and governance. Packed with practical use cases, real-world examples, and a glimpse into the future of data integration, The Definitive Guide to Data Integration is an essential resource for data eclectics. By the end of this book, you’ll have the gained the knowledge and skills needed to optimize your data usage and excel in the ever-evolving world of data.What you will learn Discover the evolving architecture and technologies shaping data integration Process large data volumes efficiently with data warehousing Tackle the complexities of integrating large datasets from diverse sources Harness the power of data warehousing for efficient data storage and processing Design and optimize effective data integration solutions Explore data governance principles and compliance requirements Who this book is for This book is perfect for data engineers, data architects, data analysts, and IT professionals looking to gain a comprehensive understanding of data integration in the modern era. Whether you’re a beginner or an experienced professional enhancing your knowledge of the modern data stack, this definitive guide will help you navigate the data integration landscape. |
| data catalog vs metadata management: Master Data Management David Loshin, 2010-07-28 The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure |
| data catalog vs metadata management: Managing Data in Motion April Reeve, 2013-02-26 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 |
| data catalog vs metadata management: Querying XML Jim Melton, Stephen Buxton, 2011-04-08 XML has become the lingua franca for representing business data, for exchanging information between business partners and applications, and for adding structure–and sometimes meaning—to text-based documents. XML offers some special challenges and opportunities in the area of search: querying XML can produce very precise, fine-grained results, if you know how to express and execute those queries.For software developers and systems architects: this book teaches the most useful approaches to querying XML documents and repositories. This book will also help managers and project leaders grasp how “querying XML fits into the larger context of querying and XML. Querying XML provides a comprehensive background from fundamental concepts (What is XML?) to data models (the Infoset, PSVI, XQuery Data Model), to APIs (querying XML from SQL or Java) and more. * Presents the concepts clearly, and demonstrates them with illustrations and examples; offers a thorough mastery of the subject area in a single book. * Provides comprehensive coverage of XML query languages, and the concepts needed to understand them completely (such as the XQuery Data Model).* Shows how to query XML documents and data using: XPath (the XML Path Language); XQuery, soon to be the new W3C Recommendation for querying XML; XQuery's companion XQueryX; and SQL, featuring the SQL/XML * Includes an extensive set of XQuery, XPath, SQL, Java, and other examples, with links to downloadable code and data samples. |
| data catalog vs metadata management: The Enterprise Big Data Lake Alex Gorelik, 2019-02-21 The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries |
| data catalog vs metadata management: Data Governance: The Definitive Guide Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021-03-08 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness. |
| data catalog vs metadata management: Mastering Data security and governance Cybellium Ltd, A Blueprint for Safeguarding Data in a Connected World In an era where data breaches and privacy concerns make headlines, the importance of robust data security and effective governance cannot be overstated. Mastering Data Security and Governance serves as your comprehensive guide to understanding and implementing strategies that protect sensitive information while ensuring compliance and accountability in today's interconnected landscape. About the Book: In a world where data is a valuable currency, organizations must prioritize data security and governance to build trust with their customers, partners, and stakeholders. Mastering Data Security and Governance delves into the critical concepts, practices, and technologies required to establish a resilient data protection framework while maintaining transparency and adhering to regulatory requirements. Key Features: Security Fundamentals: Lay the foundation with a clear explanation of data security principles, including encryption, access controls, authentication, and more. Understand the threats and vulnerabilities that can compromise data integrity and confidentiality. Governance Frameworks: Explore the intricacies of data governance, including data ownership, classification, and policies. Learn how to establish a governance framework that fosters responsible data management and usage. Compliance and Regulations: Navigate the complex landscape of data regulations and compliance standards, such as GDPR, HIPAA, and CCPA. Discover strategies for aligning your data practices with legal requirements. Risk Management: Learn how to assess and mitigate risks related to data breaches, cyberattacks, and unauthorized access. Develop incident response plans to minimize the impact of security incidents. Data Privacy: Dive into the realm of data privacy, understanding the rights of individuals over their personal information. Explore techniques for anonymization, pseudonymization, and ensuring consent-based data processing. Cloud Security: Explore the unique challenges and solutions for securing data in cloud environments. Understand how to leverage cloud security services and best practices to protect your data. Identity and Access Management: Delve into identity management systems, role-based access controls, and multi-factor authentication to ensure only authorized users have access to sensitive data. Emerging Technologies: Stay ahead of the curve by exploring how AI, blockchain, and other emerging technologies are impacting data security and governance. Understand their potential benefits and challenges. Why This Book Matters: As the digital landscape expands, so do the risks associated with data breaches and mismanagement. Mastering Data Security and Governance empowers businesses, IT professionals, and security practitioners to fortify their defenses against data threats, establish transparent governance practices, and navigate the evolving regulatory landscape. Secure Your Data Future: Data is the lifeblood of the digital age, and its security and responsible management are paramount. Mastering Data Security and Governance equips you with the knowledge and tools needed to build a robust security posture and establish effective governance, ensuring that your data remains safe, compliant, and trustworthy in an increasingly interconnected world. Your journey to safeguarding valuable data begins here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| data catalog vs metadata management: Executing Data Quality Projects Danette McGilvray, 2021-05-27 Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online |
| data catalog vs metadata management: Cloud Data Architectures Demystified Ashok Boddeda, 2023-09-27 Learn using Cloud data technologies for improving data analytics and decision-making capabilities for your organization KEY FEATURES ● Get familiar with the fundamentals of data architecture and Cloud computing. ● Design and deploy enterprise data architectures on the Cloud. ● Learn how to leverage AI/ML to gain insights from data. DESCRIPTION Cloud data architectures are a valuable tool for organizations that want to use data to make better decisions. By understanding the different components of Cloud data architectures and the benefits they offer, organizations can select the right architecture for their needs. This book is a holistic guide for using Cloud data technologies to ingest, transform, and analyze data. It covers the entire data lifecycle, from collecting data to transforming it into actionable insights. The readers will get a comprehensive overview of Cloud data technologies and AI/ML algorithms. The readers will learn how to use these technologies and algorithms to improve decision-making, optimize operations, and identify new opportunities. By the end of the book, you will have a comprehensive understanding of loud data architectures and the confidence to implement effective solutions that drive business success. WHAT YOU WILL LEARN ● Learn the fundamental principles of data architecture. ● Understand the working of different cloud ecosystems such as AWS, Azure & GCP. ● Explore different Snowflake data services. ● Learn how to implement data governance policies and procedures. ● Use artificial intelligence (AI) and machine learning (ML) to gain insights from data. WHO THIS BOOK IS FOR This book is for executives, IT professionals, and data enthusiasts who want to learn more about Cloud data architectures. It does not require any prior experience, but a basic understanding of data concepts and technology landscapes will be helpful. TABLE OF CONTENTS 1. Data Architectures and Patterns 2. Enterprise Data Architectures 3. Cloud Fundamentals 4. Azure Data Eco-system 5. AWS Data Services 6. Google Data Services 7. Snowflake Data Eco-system 8. Data Governance 9. Data Intelligence: AI-ML Modeling and Services |
| data catalog vs metadata management: Software Business Sami Hyrynsalmi, |
| data catalog vs metadata management: Practical Lakehouse Architecture Gaurav Ashok Thalpati, 2024-07-24 This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures. Practical Lakehouse Architecture shows you how to: Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution Understand the differences between traditional and lakehouse data architectures Differentiate between various file formats and table formats Design lakehouse architecture layers for storage, compute, metadata management, and data consumption Implement data governance and data security within the platform Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case Make critical design decisions and address practical challenges to build a future-ready data platform Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse |
| data catalog vs metadata management: The Cloud Data Lake Rukmani Gopalan, 2022-12-12 More organizations than ever understand the importance of data lake architectures for deriving value from their data. Building a robust, scalable, and performant data lake remains a complex proposition, however, with a buffet of tools and options that need to work together to provide a seamless end-to-end pipeline from data to insights. This book provides a concise yet comprehensive overview on the setup, management, and governance of a cloud data lake. Author Rukmani Gopalan, a product management leader and data enthusiast, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance. Learn the benefits of a cloud-based big data strategy for your organization Get guidance and best practices for designing performant and scalable data lakes Examine architecture and design choices, and data governance principles and strategies Build a data strategy that scales as your organizational and business needs increase Implement a scalable data lake in the cloud Use cloud-based advanced analytics to gain more value from your data |
| data catalog vs metadata management: 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 catalog vs metadata management: Standards and Standardization: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2015-02-28 Effective communication requires a common language, a truth that applies to science and mathematics as much as it does to culture and conversation. Standards and Standardization: Concepts, Methodologies, Tools, and Applications addresses the necessity of a common system of measurement in all technical communications and endeavors, in addition to the need for common rules and guidelines for regulating such enterprises. This multivolume reference will be of practical and theoretical significance to researchers, scientists, engineers, teachers, and students in a wide array of disciplines. |
| data catalog vs metadata management: Banking 4.0 Mohan Bhatia, 2022-05-21 This book shows banking professionals how to leverage the best practices in the industry to build a structured and coordinated approach towards the digitization of banking processes. It provides a roadmap and templates in order to industrialize the financial services firm over iterative cycles. To achieve the planned business and revenue results at the optimal costs, the digital transformation has to be calibrated and coordinated across both the front and back office, scaled and timed against external innovation benchmarks and Fintechs. To this end, data collection and evaluation must be ingrained, banking-specific artificial intelligence methods must be included, and all digitization approaches must be harmonized on an iterative basis with the experience gained. Spread over several chapters, this book provides a calibration and coordination framework for the delivery of the digital bank 4.0. |
| data catalog vs metadata management: Database and Expert Systems Applications - DEXA 2021 Workshops Gabriele Kotsis, A Min Tjoa, Ismail Khalil, Bernhard Moser, Atif Mashkoor, Johannes Sametinger, Anna Fensel, Jorge Martinez-Gil, Lukas Fischer, Gerald Czech, Florian Sobieczky, Sohail Khan, 2021-09-20 This volume constitutes the refereed proceedings of the workshops held at the 32nd International Conference on Database and Expert Systems Applications, DEXA 2021, held in a virtual format in September 2021: The 12th International Workshop on Biological Knowledge Discovery from Data (BIOKDD 2021), the 5th International Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems (IWCFS 2021), the 3rd International Workshop on Machine Learning and Knowledge Graphs (MLKgraphs 2021), the 1st International Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply (AI-CARES 2021), the 1st International Workshop on Time Ordered Data (ProTime2021), and the 1st International Workshop on AI System Engineering: Math, Modelling and Software (AISys2021). Due to the COVID-19 pandemic the conference and workshops were held virtually. The 23 papers were thoroughly reviewed and selected from 50 submissions, and discuss a range of topics including: knowledge discovery, biological data, cyber security, cyber-physical system, machine learning, knowledge graphs, information retriever, data base, and artificial intelligence. |
| data catalog vs metadata management: Data Engineering for Machine Learning Pipelines Pavan Kumar Narayanan, |
| data catalog vs metadata management: Grid and Cloud Computing: Concepts, Methodologies, Tools and Applications Management Association, Information Resources, 2012-04-30 This reference presents a vital compendium of research detailing the latest case studies, architectures, frameworks, methodologies, and research on Grid and Cloud Computing-- |
| data catalog vs metadata management: The Machine Learning Solutions Architect Handbook David Ping, 2022-01-21 Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building, and model training Learn how to implement bias detection, privacy, and explainability in ML model development Book DescriptionWhen equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional. What you will learn Apply ML methodologies to solve business problems Design a practical enterprise ML platform architecture Implement MLOps for ML workflow automation Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using an AI service and a custom ML model Use AWS services to detect data and model bias and explain models Who this book is for This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook. |
| data catalog vs metadata management: Strategic Blueprint for Enterprise Analytics Liang Wang, |
| data catalog vs metadata management: The Journey Continues: From Data Lake to Data-Driven Organization Mandy Chessell, Ferd Scheepers, Maryna Strelchuk, Ron van der Starre, Seth Dobrin, Daniel Hernandez, IBM Redbooks, 2018-02-19 This IBM RedguideTM publication looks back on the key decisions that made the data lake successful and looks forward to the future. It proposes that the metadata management and governance approaches developed for the data lake can be adopted more broadly to increase the value that an organization gets from its data. Delivering this broader vision, however, requires a new generation of data catalogs and governance tools built on open standards that are adopted by a multi-vendor ecosystem of data platforms and tools. Work is already underway to define and deliver this capability, and there are multiple ways to engage. This guide covers the reasons why this new capability is critical for modern businesses and how you can get value from it. |
| data catalog vs metadata management: Microsoft Certified: Dynamics 365 Fundamentals (CRM) (MB-910) Cybellium, Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| data catalog vs metadata management: Cloud Computing in Remote Sensing Lizhe Wang, Jining Yan, Yan Ma, 2019-07-11 This book provides the users with quick and easy data acquisition, processing, storage and product generation services. It describes the entire life cycle of remote sensing data and builds an entire high performance remote sensing data processing system framework. It also develops a series of remote sensing data management and processing standards. Features: Covers remote sensing cloud computing Covers remote sensing data integration across distributed data centers Covers cloud storage based remote sensing data share service Covers high performance remote sensing data processing Covers distributed remote sensing products analysis |
| data catalog vs metadata management: Delta Lake: The Definitive Guide Denny Lee, Tristen Wentling, Scott Haines, Prashanth Babu, 2024-10-30 Ready to simplify the process of building data lakehouses and data pipelines at scale? In this practical guide, learn how Delta Lake is helping data engineers, data scientists, and data analysts overcome key data reliability challenges with modern data engineering and management techniques. Authors Denny Lee, Tristen Wentling, Scott Haines, and Prashanth Babu (with contributions from Delta Lake maintainer R. Tyler Croy) share expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You'll also uncover how ACID transactions bring reliability to data lakehouses at scale. This book helps you: Understand key data reliability challenges and how Delta Lake solves them Explain the critical role of Delta transaction logs as a single source of truth Learn the Delta Lake ecosystem with technologies like Apache Flink, Kafka, and Trino Architect data lakehouses with the medallion architecture Optimize Delta Lake performance with features like deletion vectors and liquid clustering |
| data catalog vs metadata management: Networking, Intelligent Systems and Security Mohamed Ben Ahmed, Horia-Nicolai L. Teodorescu, Tomader Mazri, Parthasarathy Subashini, Anouar Abdelhakim Boudhir, 2021-10-01 This book gathers best selected research papers presented at the International Conference on Networking, Intelligent Systems and Security, held in Kenitra, Morocco, during 01–02 April 2021. The book highlights latest research and findings in the field of ICT, and it provides new solutions, efficient tools, and techniques that draw on modern technologies to increase urban services. In addition, it provides a critical overview of the status quo, shares new propositions, and outlines future perspectives in networks, smart systems, security, information technologies, and computer science. |
| data catalog vs metadata management: Principles of Data Fabric Sonia Mezzetta, 2023-04-06 Apply Data Fabric solutions to automate Data Integration, Data Sharing, and Data Protection across disparate data sources using different data management styles. Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn to design Data Fabric architecture effectively with your choice of tool Build and use a Data Fabric solution using DataOps and Data Mesh frameworks Find out how to build Data Integration, Data Governance, and Self-Service analytics architecture Book Description Data can be found everywhere, from cloud environments and relational and non-relational databases to data lakes, data warehouses, and data lakehouses. Data management practices can be standardized across the cloud, on-premises, and edge devices with Data Fabric, a powerful architecture that creates a unified view of data. This book will enable you to design a Data Fabric solution by addressing all the key aspects that need to be considered. The book begins by introducing you to Data Fabric architecture, why you need them, and how they relate to other strategic data management frameworks. You'll then quickly progress to grasping the principles of DataOps, an operational model for Data Fabric architecture. The next set of chapters will show you how to combine Data Fabric with DataOps and Data Mesh and how they work together by making the most out of it. After that, you'll discover how to design Data Integration, Data Governance, and Self-Service analytics architecture. The book ends with technical architecture to implement distributed data management and regulatory compliance, followed by industry best practices and principles. By the end of this data book, you will have a clear understanding of what Data Fabric is and what the architecture looks like, along with the level of effort that goes into designing a Data Fabric solution. What you will learn Understand the core components of Data Fabric solutions Combine Data Fabric with Data Mesh and DataOps frameworks Implement distributed data management and regulatory compliance using Data Fabric Manage and enforce Data Governance with active metadata using Data Fabric Explore industry best practices for effectively implementing a Data Fabric solution Who this book is for If you are a data engineer, data architect, or business analyst who wants to learn all about implementing Data Fabric architecture, then this is the book for you. This book will also benefit senior data professionals such as chief data officers looking to integrate Data Fabric architecture into the broader ecosystem. |
| data catalog vs metadata management: Data Products and the Data Mesh Alberto Artasanchez, Data Products and the Data Mesh is a comprehensive guide that explores the emerging paradigm of the data mesh and its implications for organizations navigating the data-driven landscape. This book equips readers with the knowledge and insights needed to design, build, and manage effective data products within the data mesh framework. The book starts by introducing the core concepts and principles of the data mesh, highlighting the shift from centralized data architectures to decentralized, domain-oriented approaches. It delves into the key components of the data mesh, including federated data governance, data marketplaces, data virtualization, and adaptive data products. Each chapter provides in-depth analysis, practical strategies, and real-world examples to illustrate the application of these concepts. Readers will gain a deep understanding of how the data mesh fosters a culture of data ownership, collaboration, and innovation. They will explore the role of modern data architectures, such as data marketplaces, in facilitating decentralized data sharing, access, and monetization. The book also delves into the significance of emerging technologies like blockchain, AI, and machine learning in enhancing data integrity, security, and value creation. Throughout the book, readers will discover practical insights and best practices to overcome challenges related to data governance, scalability, privacy, and compliance. They will learn how to optimize data workflows, leverage domain-driven design principles, and harness the power of data virtualization to drive meaningful insights and create impactful data products. Data Products and the Data Mesh is an essential resource for data professionals, architects, and leaders seeking to navigate the complex world of data products within the data mesh paradigm. It provides a comprehensive roadmap for building a scalable, decentralized, and innovative data ecosystem that empowers organizations to unlock the full potential of their data assets and drive data-driven success. |
| data catalog vs metadata management: Formal Ontology in Information Systems N. Aussenac-Gilles, T. Hahmann, A. Galton, 2024-01-16 FOIS is the flagship conference of the International Association for Ontology and its Applications, a non-profit organization which promotes interdisciplinary research and international collaboration at the intersection of philosophical ontology, linguistics, logic, cognitive science, and computer science. This book presents the papers delivered at FOIS 2023, the 13th edition of the Formal Ontology in Information Systems conference. The event was held as a sequentially-hybrid event, face-to-face in Sherbrooke, Canada, from 17 to 20 July 2023, and online from 18 to 20 September 2023. In total, 62 articles from 19 different countries were submitted, out of which 25 were accepted for inclusion in the conference and for publication; corresponding to an acceptance rate of 40 percent. The contributions are separated into the book’s three sections: (1) Foundational ontological issues; (2) Methodological issues around the development, alignment, verification and use of ontologies; and (3) Domain ontologies and ontology-based applications. In these sections, ontological aspects from a wide variety of fields are covered, primarily from various engineering domains including cybersecurity, manufacturing, petroleum engineering, and robotics, but also extending to the humanities, social sciences, medicine, and dentistry. A noticeable trend among the contributions in this edition of the conference is the recognition that improving the tools to analyze, align, and improve ontologies is of paramount importance in continuing to advance the field of formal ontology. The book will be of interest to all formal and applied ontology researchers, and to those who use formal ontologies and information systems as part of their work. |
| data catalog vs metadata management: Scalable Cloud Computing: Patterns for Reliability and Performance Peter Jones, 2024-10-14 Dive into the transformative world of cloud computing with Scalable Cloud Computing: Patterns for Reliability and Performance, your comprehensive guide to mastering the principles, strategies, and practices that define modern cloud environments. This carefully curated book navigates through the intricate landscape of cloud computing, from foundational concepts and architecture to designing resilient, scalable applications and managing complex data in the cloud. Whether you're a beginner seeking to understand the basics or an experienced professional aiming to enhance your skills, this book offers deep insights into ensuring reliability, optimizing performance, securing cloud environments, and much more. Explore the latest trends, including microservices, serverless computing, and emerging technologies that are pushing the boundaries of what's possible in the cloud. Through detailed explanations, practical examples, and real-world case studies, Scalable Cloud Computing: Patterns for Reliability and Performance equips you with the knowledge to architect and deploy robust applications that leverage the full potential of cloud computing. Unlock the secrets to optimizing costs, automating deployments with CI/CD, and navigating the complexities of data management and security in the cloud. This book is your gateway to becoming an expert in cloud computing, ready to tackle challenges and seize opportunities in this ever-evolving field. Join us on this journey to mastering cloud computing, where scalability and reliability are within your reach. |
THE MANY FACES OF METADATA MANAGEMENT— FROM …
Metadata management, he explained, focuses on understanding the data and what it is, then using that understanding to properly update and expand reports, dashboards, and other data …
DATA CATALOG: KEY TO A MODERN FRAMEWORK
Here we describe the key tenets of a modern data catalog and the enhanced outcomes from implementation of modern data catalogs. We’ll outline best practices and keys to success in …
Data Catalogs: A Systematic Literature Review and Guidelines …
In this paper, we contribute with a systematic literature review (SLR) on data catalogs to identify (1) necessary and optional conceptual compo-nents and (2) guidelines to implement a data …
Implementing Data Catalogs - Texas
Metadata management: Data catalogs provide a format to document the technical, business, and operational metadata associated with data assets throughout the data lifecycle, beginning with …
Data Catalogs Are the New Black in Data Management and …
Use data catalogs to curate the inventory of available distributed information assets and to map information supply chains, by making them an essential component of your data management …
How to Govern Glossaries, Dictionaries, and Data Catalogs
–Populating the Business Glossary, Data Dictionary, and Data Catalog –What It Means to Govern the Tools and the Metadata –Formalizing Accountability for Metadata
Accelerating Data Discovery and Governance: Unlocking …
Metadata Management: Stores and manages metadata for different types of data assets, including structured and unstructured data. Search & Discovery: Users can perform advanced …
Comprehensive and Comprehensible Data Catalogs: The …
Scalable data science requires access to metadata, which is increas-ingly managed by databases called data catalogs. With today’s data catalogs, users choose between designs that make it …
Microsoft Word - TAB B - DoD Metadata Guidance v23
Clear and consistent metadata management underpins the secure, interoperable data environments needed for decision advantage and is essential for implementing the DoD Data …
Cloud Data Governance and Catalog - Informatica
CDGC uses intelligent data element and entity classification for automated metadata management and extraction from heterogeneous sources. Data profiling and classification can …
Data Catalogs — Implementing Capabilities for Data Curation, …
Data Catalogs are information systems that provide a platform for all data-related roles of the enterprise. Based on the ingestion and lever-age of metadata different functions are provided …
10 steps to building a data catalog - Bitpipe
A data catalog collects metadata from databases, data warehouses, data lakes, BI systems and other sources and uses it to create a searchable inventory of data assets.
Best Practices in Metadata Management
More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the …
MetaCat - metadata catalog for data management systems
MetaCat is a project with the objective to build a metadata catalogue, which can be used in a HEP data management system. Although the main target user of the project happens to be …
Enterprise Data Catalog Architecture - Informatica
Enterprise Data Catalog enables Business and IT users to unleash the power of their enterprise data assets by providing a unified metadata view that includes technical
Databricks Unity Catalog Vs. Traditional Data Governance
Unity Catalog provides centralized metadata management, enabling easy search, tagging, and categorization of datasets, leading to faster collaboration and improved productivity. Many …
Comprehensive and Comprehensible Data Catalogs: The …
In this paper, we present 5W1H+R, a new catalog mental model that is comprehensive in the metadata it represents, and comprehensible in that it permits users to locate metadata easily. …
Data Catalog has been around for a few years in metadata
data catalog is to consolidate the metadata with all available datasets, and present them in the simplest and most straightforward way to expectant data consumers.
Data Catalog Vs Business Glossary - blog.amf
master data management, self-service data marketplaces, and the importance of metadata data catalog vs business glossary: Future And Fintech, The: Abcdi And Beyond Jun Xu, 2022-05-05 …
Intelligent Data Cataloging for Cloud Data Warehouses, Data …
With machine-learning capabilities built on top of comprehensive metadata management, Informatica Enterprise Data Catalog provides a common enterprise metadata foundation for …
THE MANY FACES OF METADATA MANAGEMENT— …
Metadata management, he explained, focuses on understanding the data and what it is, then using that understanding to properly update and expand reports, dashboards, and other data …
DATA CATALOG: KEY TO A MODERN FRAMEWORK
Here we describe the key tenets of a modern data catalog and the enhanced outcomes from implementation of modern data catalogs. We’ll outline best practices and keys to success in …
Data Catalogs: A Systematic Literature Review and …
In this paper, we contribute with a systematic literature review (SLR) on data catalogs to identify (1) necessary and optional conceptual compo-nents and (2) guidelines to implement a data …
Implementing Data Catalogs - Texas
Metadata management: Data catalogs provide a format to document the technical, business, and operational metadata associated with data assets throughout the data lifecycle, beginning with …
Data Catalogs Are the New Black in Data Management and …
Use data catalogs to curate the inventory of available distributed information assets and to map information supply chains, by making them an essential component of your data management …
How to Govern Glossaries, Dictionaries, and Data Catalogs
–Populating the Business Glossary, Data Dictionary, and Data Catalog –What It Means to Govern the Tools and the Metadata –Formalizing Accountability for Metadata
Accelerating Data Discovery and Governance: Unlocking …
Metadata Management: Stores and manages metadata for different types of data assets, including structured and unstructured data. Search & Discovery: Users can perform advanced …
Comprehensive and Comprehensible Data Catalogs: The …
Scalable data science requires access to metadata, which is increas-ingly managed by databases called data catalogs. With today’s data catalogs, users choose between designs that make it …
Microsoft Word - TAB B - DoD Metadata Guidance v23
Clear and consistent metadata management underpins the secure, interoperable data environments needed for decision advantage and is essential for implementing the DoD Data …
Cloud Data Governance and Catalog - Informatica
CDGC uses intelligent data element and entity classification for automated metadata management and extraction from heterogeneous sources. Data profiling and classification can …
Data Catalogs — Implementing Capabilities for Data …
Data Catalogs are information systems that provide a platform for all data-related roles of the enterprise. Based on the ingestion and lever-age of metadata different functions are provided …
10 steps to building a data catalog - Bitpipe
A data catalog collects metadata from databases, data warehouses, data lakes, BI systems and other sources and uses it to create a searchable inventory of data assets.
Best Practices in Metadata Management
More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the …
MetaCat - metadata catalog for data management systems
MetaCat is a project with the objective to build a metadata catalogue, which can be used in a HEP data management system. Although the main target user of the project happens to be …
Enterprise Data Catalog Architecture - Informatica
Enterprise Data Catalog enables Business and IT users to unleash the power of their enterprise data assets by providing a unified metadata view that includes technical
Databricks Unity Catalog Vs. Traditional Data Governance
Unity Catalog provides centralized metadata management, enabling easy search, tagging, and categorization of datasets, leading to faster collaboration and improved productivity. Many …
Comprehensive and Comprehensible Data Catalogs: The …
In this paper, we present 5W1H+R, a new catalog mental model that is comprehensive in the metadata it represents, and comprehensible in that it permits users to locate metadata easily. …
Data Catalog has been around for a few years in metadata
data catalog is to consolidate the metadata with all available datasets, and present them in the simplest and most straightforward way to expectant data consumers.
Data Catalog Vs Business Glossary - blog.amf
master data management, self-service data marketplaces, and the importance of metadata data catalog vs business glossary: Future And Fintech, The: Abcdi And Beyond Jun Xu, 2022-05 …
Intelligent Data Cataloging for Cloud Data Warehouses, Data …
With machine-learning capabilities built on top of comprehensive metadata management, Informatica Enterprise Data Catalog provides a common enterprise metadata foundation for …
