Business Metadata: Capturing Enterprise Knowledge is the first book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and management. Written by Bill Inmon, one of the fathers of the data warehouse and well-known author, the book is filled with war stories, examples, and cases from current projects. It includes a complete metadata acquisition methodology and project plan to guide readers every step of the way, and sample unstructured metadata for use in self-testing and developing skills. This book is recommended for IT professionals, including those in consulting, working on systems that will deliver better knowledge management capability. This includes people in these positions: data architects, data analysts, SOA architects, metadata analysts, repository (metadata data warehouse) managers as well as vendors that have a metadata component as part of their systems or tools.

Business Metadata: Capturing Enterprise Knowledge

Computers & Technology

Inmon, W.H.

O’Neil, Bonnie

Fryman, Lowell

Web Development & Design

Morgan Kaufmann

DW 2.0: The Architecture for the Next Generation of Data Warehousing is the first book on the new generation of data warehouse architecture, DW 2.0, by the father of the data warehouse. The book describes the future of data warehousing that is technologically possible today, at both an architectural level and technology level. The perspective of the book is from the top down: looking at the overall architecture and then delving into the issues underlying the components. This allows people who are building or using a data warehouse to see what lies ahead and determine what new technology to buy, how to plan extensions to the data warehouse, what can be salvaged from the current system, and how to justify the expense at the most practical level. This book gives experienced data warehouse professionals everything they need in order to implement the new generation DW 2.0. It is designed for professionals in the IT organization, including data architects, DBAs, systems design and development professionals, as well as data warehouse and knowledge management professionals. * First book on the new generation of data warehouse architecture, DW 2.0. * Written by the "father of the data warehouse", Bill Inmon, a columnist and newsletter editor of The Bill Inmon Channel on the Business Intelligence Network. * Long overdue comprehensive coverage of the implementation of technology and tools that enable the new generation of the DW: metadata, temporal data, ETL, unstructured data, and data quality control.

DW 2.0: The Architecture for the Next Generation of Data Warehousing (Morgan Kaufman Series in Data Management Systems)

Business & Money

Strauss, Derek

Neushloss, Genia

Management & Leadership

This book brings all of the elements of database design together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of database design methodology ? from ER and UML techniques, to conceptual data modeling and table transformation, to storing XML and querying moving objects databases. The proposed book expertly combines the finest database design material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of database design. This book represents a quick and efficient way to unite valuable content from leading database design experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources.

Database Design: Know It All (Morgan Kaufmann Know It All)

Simsion, Graeme

Morgan, Tony

O'Neil, Patrick

O'Neil, Elizabeth

Schneider, Markus

Witt, Graham

Halpin, Terry

Harrington, Jan L.

Lightstone, Sam S.

Teorey, Toby J.

Melton, Jim

Nadeau, Thomas P.

Buxton, Stephen

Güting, Ralf Hartmut

Networking & Cloud Computing

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: <ul>
<li>Turn textual information into a form that can be analyzed by standard tools.</li>
<li>Make the connection between analytics and Big Data</li>
<li>Understand how Big Data fits within an existing systems environment </li>
<li>Conduct analytics on repetitive and non-repetitive data</li>
</ul>
<ul>
<li>Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it</li>
<li>Shows how to turn textual information into a form that can be analyzed by standard tools</li>
<li>Explains how Big Data fits within an existing systems environment </li>
<li>Presents new opportunities that are afforded by the advent of Big Data </li>
<li>Demystifies the murky waters of repetitive and non-repetitive data in Big Data</li>
</ul>

Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault

Linstedt, Daniel

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.
<ul>
<li>New case studies include expanded coverage of textual management and analytics</li>
<li>New chapters on visualization and big data</li>
<li>Discussion of new visualizations of the end-state architecture</li>
</ul>

Data Architecture: A Primer for the Data Scientist: A Primer for the Data Scientist

Levins, Mary

Academic Press

The data warehouse is a method of storing historical and integrated data for use in decision support systems (DSS). The data warehouse provides a source of integrated enterprise-wide historical data. This book describes how to use a data warehouse once it has been constructed. The authors discuss how to use information to capture and maintain a competitive advantage and how to migrate legacy systems to a data warehouse.

Using the Data Warehouse

Hackathorn, Richard D.

wiley

The data warehousing bible updated for the new millennium Updated and expanded to reflect the many technological advances occurring since the previous edition, this latest edition of the data warehousing "bible" provides a comprehensive introduction to building data marts, operational data stores, the Corporate Information Factory, exploration warehouses, and Web-enabled warehouses. Written by the father of the data warehouse concept, the book also reviews the unique requirements for supporting e-business and explores various ways in which the traditional data warehouse can be integrated with new technologies to provide enhanced customer service, sales, and support-both online and offline-including near-line data storage techniques.

Building the Data Warehouse

Everything you need to know about building, using, managing, and getting the most out of an operational data store. "...the combination of the Operational Data Store along with the data warehouse 'flesh out' the modern information architecture of systems. Together these two architectural combine...to form a complete picture of modern systems architecture." W. H. Inmon. The operational data store is a dynamic architectural construct specifically designed for doing high-speed, integrated operational processing. It achieves at the operational level what the data warehouse does at the strategic/managerial level. Now, from a team of experts headed by W. H. Inmon, the "father of the data warehouse," here is the first comprehensive guide to understanding, building, and using operational data stores. Providing in-depth coverage of all practical and theoretical issues surrounding the operational data store, it tells you: <ul>
<li>What the operational data store is, how it differs from the data warehouse, where it fits within modern systems architecture.</li> <li>How the operational data store relates to decision support and executive information systems data.</li> <li>Hardware platforms and configurations, related database design techniques, the role of CASE and DBMS technologies, and other crucial technical considerations.</li> <li>How to design and build an operational data store—including step-by-step guidelines, a comprehensive work plan, and numerous illustrative case studies.</li> <li>Techniques for using the operational data store in conjunction with the data warehouse to provide companies with a powerful competitive edge.</li> <li>And much more. Find out what an operational data store can do for your company and how to build one tailored to your organization's unique operational style and day-to-day information needs in Building the Operational Data Store. </li> </ul>

Building the Operational Data Store

Imhoff, Claudia

Battas, Greg

Everything you need to know to design, develop, and build your data warehouse The data warehouse solves the problem of getting information out of legacy systems quickly and efficiently. If designed and built right, data warehouses can provide significant freedom of access to data, thereby delivering enormous benefits to any organization. In this unique handbook, W. H. Inmon, "the father of the data warehouse," provides detailed discussion and analysis of all major issues related to the design and construction of the data ware-house, including granularity of data, partitioning data, metadata, lack of creditability of decision support systems (DSS) data, the system of record, migration, and more. This Second Edition of Building the Data Warehouse is revised and expanded to include new techniques and applications of data warehouse technology and update existing topics to reflect the latest thinking. It includes a useful review checklist to help evaluate the effectiveness of the design and explains * How to design and build a data warehouse-including step-by-step guidelines * Migration to the data warehouse from existing architectures * Techniques for using the data warehouse to provide companies with a powerful competitive edge * How to monitor the data warehouse * The role of the data warehouse in reengineering * How to get the most from both star joins and data models * How data marts and OLAP fit with data warehousing techniques * And much more Find out what a data warehouse can do for your company and how to build one tailored to your organization's needs in Building the Data Warehouse, Second Edition.

Proven data models that save companies' time and money developing data architectures, databases and data warehouses. The Data Model Resource Book provides a common set of data models for specific functions common to most businesses, such as sales, marketing, order processing, contracts, shipments, invoicing, work efforts, budgeting, and accounting. Readers can apply more than one data model to their own company to meet specific data needs. A CD-ROM ,sold separately, contains the SQL for all of the data models contained in the book in Oracle, Sybase, Informix, Microsoft SQL Server and ANSI compatable formats.

The Data Model Resource Book: A Library of Logical Data Models and Data Warehouse Designs

Silverston, Len

Graziano, Kent

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