Advertisement
| dan linstedt data vault book: Building a Scalable Data Warehouse with Data Vault 2.0 Daniel Linstedt, Michael Olschimke, 2015-09-15 The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. Building a Scalable Data Warehouse covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: - How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. - Important data warehouse technologies and practices. - Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. - Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast - Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse - Demystifies data vault modeling with beginning, intermediate, and advanced techniques - Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0 |
| dan linstedt data vault book: Data Architecture: A Primer for the Data Scientist W.H. Inmon, Daniel Linstedt, 2014-11-26 Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can't be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You'll be able to: - Turn textual information into a form that can be analyzed by standard tools. - Make the connection between analytics and Big Data - Understand how Big Data fits within an existing systems environment - Conduct analytics on repetitive and non-repetitive data - Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it - Shows how to turn textual information into a form that can be analyzed by standard tools - Explains how Big Data fits within an existing systems environment - Presents new opportunities that are afforded by the advent of Big Data - Demystifies the murky waters of repetitive and non-repetitive data in Big Data |
| dan linstedt data vault book: Building a Scalable Data Warehouse with Data Vault 2.0 Daniel Linstedt, Michael Olschimke, 2015-09-15 The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. Building a Scalable Data Warehouse covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: - How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. - Important data warehouse technologies and practices. - Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. - Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast - Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse - Demystifies data vault modeling with beginning, intermediate, and advanced techniques - Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0 |
| dan linstedt data vault book: Super Charge Your Data Warehouse Dan Linstedt, 2011-11-11 Do You Know If Your Data Warehouse Flexible, Scalable, Secure and Will It Stand The Test Of Time And Avoid Being Part Of The Dreaded Life Cycle? The Data Vault took the Data Warehouse world by storm when it was released in 2001. Some of the world's largest and most complex data warehouse situations understood the value it gave especially with the capabilities of unlimited scaling, flexibility and security. Here is what industry leaders say about the Data Vault The Data Vault is the optimal choice for modeling the EDW in the DW 2.0 framework - Bill Inmon, The Father of Data Warehousing The Data Vault is foundationally strong and an exceptionally scalable architecture - Stephen Brobst, CTO, Teradata The Data Vault should be considered as a potential standard for RDBMS-based analytic data management by organizations looking to achieve a high degree of flexibility, performance and openness - Doug Laney, Deloitte Analytics Institute I applaud Dan's contribution to the body of Business Intelligence and Data Warehousing knowledge and recommend this book be read by both data professionals and end users - Howard Dresner, From the Foreword - Speaker, Author, Leading Research Analyst and Advisor You have in your hands the work, experience and testing of 2 decades of building data warehouses. The Data Vault model and methodology has proven itself in hundreds (perhaps thousands) of solutions in Insurance, Crime-Fighting, Defense, Retail, Finance, Banking, Power, Energy, Education, High-Tech and many more. Learn the techniques and implement them and learn how to build your Data Warehouse faster than you have ever done before while designing it to grow and scale no matter what you throw at it. Ready to Super Charge Your Data Warehouse? |
| dan linstedt data vault book: Fact Oriented Modeling with FCO-IM Jan Pieter Zwart, Marco Engelbart, Stijn Hoppenbrouwers, 2015-10-01 This book offers a complete basic course in Fully Communication Oriented Information Modeling (FCO-IM), a Fact Oriented Modeling (FOM) data modeling technique. The book is suitable for self-study by beginner FCO-IM modelers, whether or not experienced in other modeling techniques. An elaborate case study is used as illustration throughout the book. The book also illustrates how data models in other techniques can be derived from an elementary FCO-IM model. The context of fact oriented modeling is given as well, and perspectives on information modeling indicate related areas of application and further reading. |
| dan linstedt data vault book: The Vault Unleashed James Raper, 2008-11-21 The Vault Unleashed leads managers and those new to the Business Intelligence field on a journey through the landscape. Successful Business Intelligence, or BI, is more than just technology. BI is the melding of people of multiple skills, using a variety of methods and empowering technology to solve business needs. This is best illustrated thru examples.This BI novel uses a fictitious island nation in the South Atlantic faced with threats to its very existence. The story takes the reader thru a connection of the dots from perception of a potential problem thru management execution of solutions. Augmenters must be brought on board. The core team must transfer knowledge to the new augmenters in the background and methodologies necessary to develop insights and make decisions. Most enterprises today are faced with similar challenges in our global economy. Your competition may not be state supported or as aggressive as in this tale. But for today's managers their counterparts are just as real. |
| dan linstedt data vault book: The Business of Data Vault Modeling Daniel Lindstedt, Kent Graziano, Hans Hultgren, 2009 |
| dan linstedt data vault book: The Elephant in the Fridge: Guided Steps to Data Vault Success through Building Business-Centered Models John Giles, You want the rigor of good data architecture at the speed of agile? Then this is the missing link - your step-by-step guide to Data Vault success. Success with a Data Vault starts with the business and ends with the business. Sure, there’s some technical stuff in the middle, and it is absolutely essential - but it’s not sufficient on its own. This book will help you shape the business perspective, and weave it into the more technical aspects of Data Vault modeling. You can read the foundational books and go on courses, but one massive risk still remains. Dan Linstedt, the founder of the Data Vault, very clearly directs those building a Data Vault to base its design on an “enterprise ontology”. And Hans Hultgren similarly stresses the importance of the business concepts model. So it’s important. We get that. But: What on earth is an enterprise ontology/business concept model, ‘cause I won’t know if I’ve got one if I don’t know what I’m looking for? If I can’t find one, how do I get my hands on such a thing? Even if I have one of these wonderful things, how do I apply it to get the sort of Data Vault that’s recommended? It’s actually not as hard as some would fear to answer all of these questions, and it’s certainly worth the effort. This book just might save you a world of pain. It’s a supplement to other material on Data Vault modeling, but it’s the vital missing link to finding simplicity for Data Vault success. “Data Warehousing in the context of large healthcare organizations is notoriously difficult – largely in part due to challenges around Health Information Modeling. The Elephant in the Fridge provides clear and rational perspectives that have allowed my team to breakthrough some of our circular debates and ‘too hard basket’ challenges. Success in this space will require technical and health professionals to co-design solutions around a shared information model. This book is written in a way that they can both consume. John’s experience and emphasis on art of Information Modelling is a welcome complement to other works on Data Vault. I wish we had this book a year ago!” Benson Choy, Enterprise Information Architect, eHealth Queensland. “John has a wonderful way of explaining complicated topics in an uncomplicated way. If you’ve heard about taxonomies and are wondering how to apply these to your Data Warehouse, then this is the book for you. Data modelling is about the business, and John explains how this can be achieved by using proven business templates which help to quickly and efficiently define a solid data model. Don’t re-invent the wheel, but start with the model patterns that are already available! This is a highly relevant book, because it helps to match the information delivery to business expectations - by correctly applying taxonomies and (business) model archetypes in a clear and simple way.” Roelant Vos, General Manager - Enterprise Data Management “It has been a delight and privilege working with John on a Data Vault project. I have enjoyed his practical and pragmatic approach and readiness to share his wealth of knowledge. This is an excellent book written based on real life experience. It provides valuable and timely insights into Data Vault modeling and delivery, and can be easily understood by those who are new to the Data Vault. The book contains practical advice and sample patterns to help people get started on a Data Vault project and avoid costly mistakes.” Natalia Bulashenko, Information Solution Architect “This book covers off one of the key aspects of Data Vault – the Modelling. Far too many DV practitioners come from a technical database background and lack the fundamental Data Modelling skills necessary to identify the “Key Business Concepts” that are core to successful DV projects. Far too many of us start with the low hanging fruit that we have available to us – the source systems. John explains why this is an extremely bad approach with examples, identifying the signs that the project is heading into trouble and then describes what needs to be done to get back on track. This book is NOT about the implementation phase, it’s about setting the corner stone for the whole project. Get this right and you are on the road to a successful project.” Peter Dudley, Consultant, Interactive Innovations |
| dan linstedt data vault book: Practical Data Science Andreas François Vermeulen, 2018-02-21 Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling ofpolyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers |
| dan linstedt data vault book: Data Architecture: A Primer for the Data Scientist W.H. Inmon, Daniel Linstedt, Mary Levins, 2019-04-30 Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the bigger picture and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. - New case studies include expanded coverage of textual management and analytics - New chapters on visualization and big data - Discussion of new visualizations of the end-state architecture |
| dan linstedt data vault book: Pentaho Kettle Solutions Matt Casters, Roland Bouman, Jos van Dongen, 2010-09-02 A complete guide to Pentaho Kettle, the Pentaho Data lntegration toolset for ETL This practical book is a complete guide to installing, configuring, and managing Pentaho Kettle. If you’re a database administrator or developer, you’ll first get up to speed on Kettle basics and how to apply Kettle to create ETL solutions—before progressing to specialized concepts such as clustering, extensibility, and data vault models. Learn how to design and build every phase of an ETL solution. Shows developers and database administrators how to use the open-source Pentaho Kettle for enterprise-level ETL processes (Extracting, Transforming, and Loading data) Assumes no prior knowledge of Kettle or ETL, and brings beginners thoroughly up to speed at their own pace Explains how to get Kettle solutions up and running, then follows the 34 ETL subsystems model, as created by the Kimball Group, to explore the entire ETL lifecycle, including all aspects of data warehousing with Kettle Goes beyond routine tasks to explore how to extend Kettle and scale Kettle solutions using a distributed “cloud” Get the most out of Pentaho Kettle and your data warehousing with this detailed guide—from simple single table data migration to complex multisystem clustered data integration tasks. |
| dan linstedt data vault book: Agile Data Warehousing for the Enterprise Ralph Hughes, 2015-09-19 Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work illustrates the agile interpretations of the remaining software engineering disciplines: - Requirements management benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked. - Data engineering receives two new hyper modeling techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs. - Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines. Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way. - Learn how to quickly define scope and architecture before programming starts - Includes techniques of process and data engineering that enable iterative and incremental delivery - Demonstrates how to plan and execute quality assurance plans and includes a guide to continuous integration and automated regression testing - Presents program management strategies for coordinating multiple agile data mart projects so that over time an enterprise data warehouse emerges - Use the provided 120-day road map to establish a robust, agile data warehousing program |
| dan linstedt data vault book: Data Engineering with dbt Roberto Zagni, 2023-06-30 Use easy-to-apply patterns in SQL and Python to adopt modern analytics engineering to build agile platforms with dbt that are well-tested and simple to extend and run Purchase of the print or Kindle book includes a free PDF eBook Key Features Build a solid dbt base and learn data modeling and the modern data stack to become an analytics engineer Build automated and reliable pipelines to deploy, test, run, and monitor ELTs with dbt Cloud Guided dbt + Snowflake project to build a pattern-based architecture that delivers reliable datasets Book Descriptiondbt Cloud helps professional analytics engineers automate the application of powerful and proven patterns to transform data from ingestion to delivery, enabling real DataOps. This book begins by introducing you to dbt and its role in the data stack, along with how it uses simple SQL to build your data platform, helping you and your team work better together. You’ll find out how to leverage data modeling, data quality, master data management, and more to build a simple-to-understand and future-proof solution. As you advance, you’ll explore the modern data stack, understand how data-related careers are changing, and see how dbt enables this transition into the emerging role of an analytics engineer. The chapters help you build a sample project using the free version of dbt Cloud, Snowflake, and GitHub to create a professional DevOps setup with continuous integration, automated deployment, ELT run, scheduling, and monitoring, solving practical cases you encounter in your daily work. By the end of this dbt book, you’ll be able to build an end-to-end pragmatic data platform by ingesting data exported from your source systems, coding the needed transformations, including master data and the desired business rules, and building well-formed dimensional models or wide tables that’ll enable you to build reports with the BI tool of your choice.What you will learn Create a dbt Cloud account and understand the ELT workflow Combine Snowflake and dbt for building modern data engineering pipelines Use SQL to transform raw data into usable data, and test its accuracy Write dbt macros and use Jinja to apply software engineering principles Test data and transformations to ensure reliability and data quality Build a lightweight pragmatic data platform using proven patterns Write easy-to-maintain idempotent code using dbt materialization Who this book is for This book is for data engineers, analytics engineers, BI professionals, and data analysts who want to learn how to build simple, futureproof, and maintainable data platforms in an agile way. Project managers, data team managers, and decision makers looking to understand the importance of building a data platform and foster a culture of high-performing data teams will also find this book useful. Basic knowledge of SQL and data modeling will help you get the most out of the many layers of this book. The book also includes primers on many data-related subjects to help juniors get started. |
| dan linstedt data vault book: Advanced Research in Technologies, Information, Innovation and Sustainability Teresa Guarda, Filipe Portela, Gustavo Gatica, 2025-03-12 This three-volume set, CCIS 2345-2347, constitutes the revised selected papers from the 4th International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability 2024, ARTIIS 2024, held in Santiago de Chile, Chile, during October 21-23, 2024. The 83 full papers and 8 short papers included in these proceedings were carefully reviewed and selected from 238 submissions. These papers are categorized under the following topical sections:- Part I: Computing Solutions Part II: Data Intelligence Part III: Sustainability; Ethics, Security, and Privacy |
| dan linstedt data vault book: Hands-On Big Data Modeling James Lee, Tao Wei, Suresh Kumar Mukhiya, 2018-11-30 Solve all big data problems by learning how to create efficient data models Key FeaturesCreate effective models that get the most out of big dataApply your knowledge to datasets from Twitter and weather data to learn big dataTackle different data modeling challenges with expert techniques presented in this bookBook Description Modeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements. To start with, you’ll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you’ll work with structured and semi-structured data with the help of real-life examples. Once you’ve got to grips with the basics, you’ll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You’ll also learn to create graph data models and explore data modeling with streaming data using real-world datasets. By the end of this book, you’ll be able to design and develop efficient data models for varying data sizes easily and efficiently. What you will learnGet insights into big data and discover various data modelsExplore conceptual, logical, and big data modelsUnderstand how to model data containing different file typesRun through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modelingCreate data models such as Graph Data and Vector SpaceModel structured and unstructured data using Python and RWho this book is for This book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful. |
| dan linstedt data vault book: Oracle SQL Developer Data Modeler for Database Design Mastery Heli Helskyaho, 2015-05-22 Design Databases with Oracle SQL Developer Data Modeler In this practical guide, Oracle ACE Director Heli Helskyaho explains the process of database design using Oracle SQL Developer Data Modeler—the powerful, free tool that flawlessly supports Oracle and other database environments, including Microsoft SQL Server and IBM DB2. Oracle SQL Developer Data Modeler for Database Design Mastery covers requirement analysis, conceptual, logical, and physical design, data warehousing, reporting, and more. Create and deploy high-performance enterprise databases on any platform using the expert tips and best practices in this Oracle Press book. Configure Oracle SQL Developer Data Modeler Perform requirement analysis Translate requirements into a formal conceptual data model and process models Transform the conceptual (logical) model into a relational model Manage physical database design Generate data definition language (DDL) scripts to create database objects Design a data warehouse database Use subversion for version control and to enable a multiuser environment Document an existing database Use the reporting tools in Oracle SQL Developer Data Modeler Compare designs and the database |
| dan linstedt data vault book: Databases Illuminated Catherine M. Ricardo, Susan D. Urban, Karen C. Davis, 2022-03-09 Databases Illuminated, Fourth Edition is designed to help students integrate theoretical material with practical knowledge, using an approach that applies theory to practical database implementation. |
| dan linstedt data vault book: The Business of Data Vault Modeling Daniel Lindstedt, Kent Graziano, Hans Hultgren, 2009 |
| dan linstedt data vault book: Preventing Litigation Nelson (Nick) E. Brestoff, William H. Inmon, 2015-08-25 Preventing Litigation, for the first time, explains how to build an early warning system to identify the risk of litigation before the damage is done, and proves that there is big value in less litigation. This book puts everyone where they should be: at the top of the cliff. The authors are subject matter experts, one in litigation, the other in computer science, and each has more than four decades of training and experience in their respective fields. Together, they present a way forward to a transformative revolution for the slow-moving world of law for the benefit of the fast-paced environment of the business world. Any business adopting the teachings of this pioneering, game-changing book will have a competitive advantage. |
| dan linstedt data vault book: Data Warehouse and Data Mining K. Gurnadha Gupta, Alampally Sreedevi, S. Sai Kumar, K. Parish Venkata Kumar, T. Kumaresan , 2021-03-01 |
| dan linstedt data vault book: The Elephant in the Fridge John Giles, 2019-04-15 You want the rigor of good data architecture at the speed of agile? Then this is the missing link - your step-by-step guide to Data Vault success. Success with a Data Vault starts with the business and ends with the business. Sure, there's some technical stuff in the middle, and it is absolutely essential - but it's not sufficient on its own. This book will help you shape the business perspective, and weave it into the more technical aspects of Data Vault modeling. You can read the foundational books and go on courses, but one massive risk still remains. Dan Linstedt, the founder of the Data Vault, very clearly directs those building a Data Vault to base its design on an enterprise ontology. And Hans Hultgren similarly stresses the importance of the business concepts model. So it's important. We get that. But: What on earth is an enterprise ontology/business concept model, 'cause I won't know if I've got one if I don't know what I'm looking for? If I can't find one, how do I get my hands on such a thing? Even if I have one of these wonderful things, how do I apply it to get the sort of Data Vault that's recommended? It's actually not as hard as some would fear to answer all of these questions, and it's certainly worth the effort. This book just might save you a world of pain. It's a supplement to other material on Data Vault modeling, but it's the vital missing link to finding simplicity for Data Vault success. |
| dan linstedt data vault book: DAMA-DMBOK. Свод знаний по управлению данными Коллектив авторов, 2020-11-16 Главная задача книги – определить набор руководящих принципов и описать их применение в функциональных областях управления данными. Издание всесторонне описывает проблемы, возникающие в процессе управления данными, и предлагает способы их решения. В нем подробно описаны широко принятые практики, методы и приемы, функции, роли, результаты и метрики.«DAMA-DMBOK: Свод знаний по управлению данными. Второе издание» предоставляет специалистам по управлению данными, ИТ-специалистам, руководителям, преподавателям и исследователям обширный материал для совершенствования работы с информационными активами и корпоративными данными. |
| dan linstedt data vault book: DAMA-DMBOK: Guía Del Conocimiento Para La Gestión De Datos (Spanish Edition) DAMA International, La Guía del Conocimiento para la Gestión de Datos (DAMA-DMBOK2) presenta una visión exhaustiva de los desafíos, complejidades y valor de la gestión eficaz de los datos. Las organizaciones de hoy en día reconocen que la gestión de los datos es fundamental para su éxito. Reconocen que los datos tienen valor y quieren aprovechar ese valor. A medida que nuestra capacidad y deseo de crear y explotar datos ha aumentado, también lo ha hecho la necesidad de prácticas de gestión de datos confiables. La segunda edición de la Guía del Conocimiento para la Gestión de Datos de DAMA International actualiza y aumenta el exitoso DMBOK1. DMBOK2, un libro de referencia accesible y autorizado, escrito por los principales pensadores en el campo y ampliamente revisado por los miembros de DAMA, reúne materiales que describen exhaustivamente los desafíos de la gestión de datos y cómo cumplirlos mediante: · Definir un conjunto de principios rectores para la gestión de datos y describir cómo se pueden aplicar estos principios dentro de las áreas funcionales de gestión de datos. · Proporcionar un marco de referencia funcional para la implementación de prácticas de gestión de datos empresariales, incluyendo prácticas, métodos y técnicas ampliamente adoptadas, funciones, roles, entregables y métricas. · Establecer un vocabulario común para los conceptos de gestión de datos y servir de base para las mejores prácticas para los profesionales de la gestión de datos. DAMA-DMBOK2 proporciona a los profesionales de la gestión de datos y de TI, a ejecutivos, trabajadores del conocimiento, educadores e investigadores un marco para gestionar sus datos y madurar su infraestructura de información, basado en estos principios: · Los datos son un activo con propiedades únicas · El valor de los datos puede y debe expresarse en términos económicos · Gestionar los datos significa gestionar la calidad de los datos · Se necesitan metadatos para gestionar los datos · Se necesita planificación para gestionar los datos · La gestión de datos es multifuncional y requiere una amplia gama de habilidades y experiencia · La gestión de datos requiere una perspectiva empresarial · La gestión de datos debe tener en cuenta una serie de perspectivas · La gestión de datos es la gestión del ciclo de vida de los datos · Los diferentes tipos de datos tienen diferentes requerimientos de ciclo de vida · La gestión de datos incluye la gestión de los riesgos asociados a los datos · Los requerimientos de gestión de datos deben impulsar las decisiones sobre tecnología de la información · Una gestión eficaz de los datos requiere un compromiso de liderazgo Los capítulos incluyen: · Gestión de Datos · Manejo Ético de los Datos · Gobierno de Datos · Arquitectura de Datos · Modelado y Diseño de Datos · Almacenamiento de Datos y Operaciones · Seguridad de Datos · Integración de Datos e Interoperabilidad · Gestión de Documentos y Contenidos · Datos Maestros y de Referencia · Data Warehousing e Inteligencia de Negocios · Gestión de Metadatos · Calidad de Datos · Big Data y Ciencia de Datos · Evaluación de la Madurez de la Gestión de Datos · Organización de la Gestión de Datos y Expectativas de Roles · Gestión de Datos y Gestión del Cambio Organizacional La estandarización de las disciplinas de gestión de datos ayudará a los profesionales de la gestión de datos a desempeñarse de forma más eficaz y consistente. También permitirá a los líderes de la organización reconocer el valor y las contribuciones de las actividades de gestión de datos. |
| dan linstedt data vault book: The DataOps Revolution Simon Trewin, 2021-08-05 DataOps is a new way of delivering data and analytics that is proven to get results. It enables IT and users to collaborate in the delivery of solutions that help organisations to embrace a data-driven culture. The DataOps Revolution: Delivering the Data-Driven Enterprise is a narrative about real world issues involved in using DataOps to make data-driven decisions in modern organisations. The book is built around real delivery examples based on the author’s own experience and lays out principles and a methodology for business success using DataOps. Presenting practical design patterns and DataOps approaches, the book shows how DataOps projects are run and presents the benefits of using DataOps to implement data solutions. Best practices are introduced in this book through the telling of a story, which relates how a lead manager must find a way through complexity to turn an organisation around. This narrative vividly illustrates DataOps in action, enabling readers to incorporate best practices into everyday projects. The book tells the story of an embattled CIO who turns to a new and untested project manager charged with a wide remit to roll out DataOps techniques to an entire organisation. It illustrates a different approach to addressing the challenges in bridging the gap between IT and the business. The approach presented in this story lines up to the six IMPACT pillars of the DataOps model that Kinaesis (www.kinaesis.com) has been using through its consultants to deliver successful projects and turn around failing deliveries. The pillars help to organise thinking and structure an approach to project delivery. The pillars are broken down and translated into steps that can be applied to real-world projects that can deliver satisfaction and fulfillment to customers and project team members. |
| dan linstedt data vault book: Data Architecture W. H. Inmon, Dan Linstedt, 2014 Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can't be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You'll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it Shows how to turn textual information into a form that can be analyzed by standard tools. Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data. |
| dan linstedt data vault book: Modeling the Agile Data Warehouse with Data Vault Hans Hultgren, 2015-09-11 Data Modeling for Agile Data Warehouse using Data Vault Modeling Approach. Includes Enterprise Data Warehouse Architecture. This is a complete guide to the data vault data modeling approach. The book also includes business and program considerations for the agile data warehousing and business intelligence program. There are over 200 diagrams and figures concerning modeling, core business concepts, architecture, business alignment, semantics, and modeling comparisons with 3NF and Dimensional modeling. |
| dan linstedt data vault book: The Data Vault Guru Patrick Cuba, 2020-10-06 The data vault methodology presents a unique opportunity to model the enterprise data warehouse using the same automation principles applicable in today's software delivery, continuous integration, continuous delivery and continuous deployment while still maintaining the standards expected for governing a corporation's most valuable asset: data. This book provides at first the landscape of a modern architecture and then as a thorough guide on how to deliver a data model that flexes as the enterprise flexes, the data vault. Whether the data is structured, semi-structured or even unstructured one thing is clear, there is always a model either applied early (schema-on-write) or applied late (schema-on-read). Today's focus on data governance requires that we know what we retain about our customers, the data vault provides that focus by delivering a methodology focused on all aspects about the customer and provides some of the best practices for modern day data compliance.The book will delve into every data vault modelling artefact, its automation with sample code, raw vault, business vault, testing framework, a build framework, sample data vault models, how to build automation patterns on top of a data vault and even offer an extension of data vault that provides automated timeline correction, not to mention variation of data vault designed to provide audit trails, metadata control and integration with agile delivery tools. |
| dan linstedt data vault book: 数据架构 英蒙, 2017 本书探讨数据的架构和如何在现有系统中最有效地利用数据,主题涵盖企业数据,大数据,数据仓库,Data Vault,业务系统和架构.主要内容包括:在分析和大数据之间建立关联,如何利用现有信息系统,如何导出重复型数据和非重复型数据,大数据以及使用大数据的商业价值等. |
| dan linstedt data vault book: A Comparison of the Impact of Data Vault and Dimensional Modelling on Data Warehouse Performance and Maintenance Marius Van Schalkwyk, North-West University (South Africa). Potchefstroom Campus, 2014 Data warehouse -- Dimensional modelling -- Data vault modelling -- Data warehouse modelling techniques -- Kimball -- Linstedt -- Query performance -- Load performance -- ETL performance -- Flexibility -- Storage requirements -- Datapakhuis -- Dimensionele modellering -- Datakluismodellering -- Datapakhuismodellering -- Navraagwerkverrigting -- Laaiwerkverrigting -- Aanpasbaarheid -- Stoorvereistes. |
| dan linstedt data vault book: Data Vault Day Course Book Hans Hultgren, 2017-03-01 This is the Data Vault Day (DVD) Course Book. It is intended to be used as part of the Genesee Academy Data Vault Day course. Data Vault Modeling is a from of data modeling which supports databases that are tasked with data integration from several different sources, that are required to maintain history for analytical and for audit reasons, or both. As such data vault modeling is an emerging standard for data warehousing, business intelligence, and big data deployments were the ultimate goal is structured data in a persisted data warehouse.Data Warehouses today typically leverage some form of Ensemble Modeling. These modeling approached separate parts of concepts that are subject to change (or subject to differing interpretations) from parts that do not change. This modeling paradigm of Unified Decomposition creates clusters or groupings of tables that together act as one concept (person, place, thing, event, etc.). Data Vault is the leading form of Ensemble Modeling. Please see GeneseeAcademy.com for more information on Data Vault courses. |
| dan linstedt data vault book: Building the Data Warehouse W. H. Inmon, 2005 The new edition of the classic bestseller that launched the data warehousing industry covers new approaches and technologies, many of which have been pioneered by Inmon himself In addition to explaining the fundamentals of data warehouse systems, the book covers new topics such as methods for handling unstructured data in a data warehouse and storing data across multiple storage media Discusses the pros and cons of relational versus multidimensional design and how to measure return on investment in planning data warehouse projects Covers advanced topics, including data monitoring and testing Although the book includes an extra 100 pages worth of valuable content, the price has actually been reduced from $65 to $55 |
| dan linstedt data vault book: Data Vault Day Course Materials Hans Hultgren, 2015-01-05 This book is a published version of the Data Vault Day (DVD) Course Materials. It is intended to be used as part of the Genesee Academy Data Vault Day course. Data Vault Modeling is a from of data modeling which supports databases that are tasked with data integration from several different sources, that are required to maintain history for analytical and for audit reasons, or both. As such data vault modeling is an emerging standard for data warehousing, business intelligence, and big data deployments were the ultimate goal is structured data in a persisted data warehouse.Data Warehouses today typically leverage some form of Ensemble Modeling. These modeling approached separate parts of concepts that are subject to change (or subject to differing interpretations) from parts that do not change. This modeling paradigm of Unified Decomposition creates clusters or groupings of tables that together act as one concept (person, place, thing, event, etc.). Data Vault is the leading form of Ensemble Modeling. Please see GeneseeAcademy.com for more information on Data Vault courses. |
| dan linstedt data vault book: Data Vault Fundamentals John Giles, 2017 We explain the data vault and position it with regard to Inmon and Kimball data warehouses. Hubs, links, and satellites are covered and business data vault and operational data vaults are explored. Common data modeling challenges are raised, including transactions, reference tables, and hierarchical links.--Resource description page. |
| dan linstedt data vault book: Data Vault Case Study (Recorded Live at Data Modeling Zone US) Dirk Lerner, 2020 Recorded live at Data Modeling Zone! Follow along with Data Vault expert Dirk Lerner and see how an organization used the Data Vault to create a data warehouse that was: more flexible, more agile, faster, and less complex. During this case study workshop, you will become part of the data modeling team where you will learn the Data Vault basics including hubs, links, and satellites, and then apply what you learn to several real-world hands-on examples. |
| dan linstedt data vault book: Data Vault Modeling A Complete Guide - 2020 Edition Gerardus Blokdyk, |
| dan linstedt data vault book: Building the Data Warehouse, 4th Ed William H. Inmon, 2005 Market_Desc: · IT, Database, and Data Warehouse Managers and Developers Special Features: · Building the Data Warehouse has sold nearly 40,000 copies in its first 3 editions· Inmon is widely recognized as the Father of the Data Warehouse and remains one of the two leading authorities in the industry he helped to invent· The new edition covers new approaches and technologies, many of which have been pioneered by Inmon himself· Price of this new edition will be reduced from $65 to $55, and 100 new pages added About The Book: This book provides a high-level, conceptual overview of the major components of data warehouse systems, as well as the core approaches used to design and build data warehouses. Topics covered in this book are methods for handling unstructured data in a data warehouse, storing data across multiple storage media, the pros and cons of relational vs. multidimensional design, data monitoring and testing. |
| dan linstedt data vault book: Data Vault Basics Kent Graziano, 2019 As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them.--Resource description page. |
| dan linstedt data vault book: Data Vault Certification Course Book Hans Hultgren, 2014-03-01 This is the coursebook for the Certified Data Vault Data Modeler CDVDM course. Data Vault modeling is a form of Data Modeling optimized for Agile Data Warehousing and Business Intelligence DWBI programs. As a form of Ensemble Modeling, the Data Vault approach is based on the Unified Decomposition pattern. This pattern models Core Business Concepts (main business entities, similar to business dimensions or enterprise business concepts) using multiple component parts. This separates the things that change (context, descriptions, states, ratings and status for example) from the things that don't change (business keys, static descriptors and core relationships for example). The result is a highly scalable, adaptable, and traceable data store that can capture all data over time with complete historization. |
| dan linstedt data vault book: Data Vault Certification CDVDM Book Hans Hultgren, 2015-01-15 This is the latest version of the coursebook for the Certified Data Vault Data Modeler CDVDM course. The CDVDM certification course is managed and delivered by Genesee Academy, LLC and its international training partners. Data Vault modeling is a form of Data Modeling optimized for Agile Data Warehousing and Business Intelligence DWBI programs. As a form of Ensemble Modeling, the Data Vault approach is based on the Unified Decomposition pattern. This pattern models Core Business Concepts (main entities, similar to business dimensions or enterprise business concepts) using multiple component parts. This separates the things that change (context, descriptions, states, ratings and status for example) from the things that don't change (business keys, status descriptors and core relationships for example). The result is a highly scalable, adaptable, and traceable data store that can capture all data over time with complete historization. |
| dan linstedt data vault book: Data Vault Modeling a Complete Guide - 2019 Edition Gerardus Blokdyk, 2018-12-20 What strategies can users deploy to develop a successful data warehouse architecture ? What are the other data sources that need to be integrated from a reporting perspective (either into the warehouse or directly as a source for reporting)? Who is the audience? What types of data ingestion pipelines do you have, at what frequency? Who is the audience? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Data Vault Modeling investments work better. This Data Vault Modeling All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Data Vault Modeling Self-Assessment. Featuring 1121 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Data Vault Modeling improvements can be made. In using the questions you will be better able to: - diagnose Data Vault Modeling projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Data Vault Modeling and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Data Vault Modeling Scorecard, you will develop a clear picture of which Data Vault Modeling areas need attention. Your purchase includes access details to the Data Vault Modeling self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Data Vault Modeling Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips. |
About DAN - Divers Alert Network
The world’s most recognized and respected dive safety organization, Divers Alert Network (DAN) has remained committed to the health and well-being of divers for 40 years.
Home - Divers Alert Network
Feb 12, 2025 · DAN promotes diver safety worldwide through research, medicine, education & emergency support. Get answers: What should divers do for their own safety?
DAN Membership Options - Divers Alert Network
DAN now offers two levels of membership to give divers more choices than ever before. Select Enhanced Membership with higher coverage limits and a print subscription to Alert Diver …
DAN | JOIN NOW - Divers Alert Network
is your dive safety leader. © DAN, Inc. All rights reserved. About DAN; Contact Us
Asia Pacific - DAN World
As a DAN World member, you’ll enjoy a suite of valuable benefits designed to make you a safer, smarter diver. DAN World’s dive accident assistance packages are an affordable way for …
Membership & Coverage - DAN World
DAN offers divers affordable options to be covered in the event of a diving accident/illness with emergency medical evacuation assistance as well as coverage for treatment costs following a …
Dive Accident Insurance - Divers Alert Network
DAN dive accident insurance* is an affordable way for divers to obtain insurance against the costs of dive injuries that are often left uncovered by typical health insurance. Covers diving …
Home - DAN World
5 days ago · DAN promotes diver safety worldwide through research, medicine, education & emergency support. Get answers: What should divers do for their own safety?
Contact Us - Divers Alert Network
DAN helps divers in need of medical emergency assistance and promotes dive safety through research, education, products and services.
Membership & Insurance - Divers Alert Network
In event of a dive accident or injury, call local EMS first then call DAN. 24/7 Emergency Hotline +1 (919) 684-9111 (Collect calls accepted) DAN must arrange transportation for covered …
About DAN - Divers Alert Network
The world’s most recognized and respected dive safety organization, Divers Alert Network (DAN) has remained committed to the health and well-being of divers for 40 years.
Home - Divers Alert Network
Feb 12, 2025 · DAN promotes diver safety worldwide through research, medicine, education & emergency support. Get answers: What should divers do for their own safety?
DAN Membership Options - Divers Alert Network
DAN now offers two levels of membership to give divers more choices than ever before. Select Enhanced Membership with higher coverage limits and a print subscription to Alert Diver …
DAN | JOIN NOW - Divers Alert Network
is your dive safety leader. © DAN, Inc. All rights reserved. About DAN; Contact Us
Asia Pacific - DAN World
As a DAN World member, you’ll enjoy a suite of valuable benefits designed to make you a safer, smarter diver. DAN World’s dive accident assistance packages are an affordable way for …
Membership & Coverage - DAN World
DAN offers divers affordable options to be covered in the event of a diving accident/illness with emergency medical evacuation assistance as well as coverage for treatment costs following a …
Dive Accident Insurance - Divers Alert Network
DAN dive accident insurance* is an affordable way for divers to obtain insurance against the costs of dive injuries that are often left uncovered by typical health insurance. Covers diving …
Home - DAN World
5 days ago · DAN promotes diver safety worldwide through research, medicine, education & emergency support. Get answers: What should divers do for their own safety?
Contact Us - Divers Alert Network
DAN helps divers in need of medical emergency assistance and promotes dive safety through research, education, products and services.
Membership & Insurance - Divers Alert Network
In event of a dive accident or injury, call local EMS first then call DAN. 24/7 Emergency Hotline +1 (919) 684-9111 (Collect calls accepted) DAN must arrange transportation for covered …
