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Data Modeling – Fundamentals for Data Warehousing  by editor William Mitchell, with over 15 year experience in the field of Data Warehousing.  With an overview of the Data Warehousing and Relational Databases, Data Modeling concepts and methods covered included Conceptual, Logical, and Physical Data Models, and overviews of Relational Modeling, Data Structure Diagrams, and Entity Relationship Models. Includes Chapters covering Referential Integrity, Data Normalization, and Meta Data Dictionary as well as a comparison of Data Modeling tools.  This is the perfect Data Modeling reference for modeler, developer, or project manager.

Title: Data Modeling – Fundamentals for Data Warehousing

Editor: William Mitchell

Pages: 160

Dimensions: 5.5″ x 8.5″.

Publisher: nereumedia


Purchase Data Modeling: Fundamentals for Data Warehousing by William Mitchell.

“Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the information system.

According to Hoberman, data modeling is the process of learning about the data, and the data model is the end result of the data modeling process.[2]

There are three different types of data models produced while progressing from requirements to the actual database to be used for the information system.[3] The data requirements are initially recorded as a conceptual data model which is essentially a set of technology independent specifications about the data and is used to discuss initial requirements with the business stakeholders. The conceptual model is then translated into a logical data model, which documents structures of the data that can be implemented in databases. Implementation of one conceptual data model may require multiple logical data models. The last step in data modeling is transforming the logical data model to a physical data model that organizes the data into tables, and accounts for access, performance and storage details. Data modeling defines not just data elements, but also their structures and the relationships between them.[4]

Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, e.g., using data modeling:

    to assist business analysts, programmers, testers, manual writers, IT package selectors, engineers, managers, related organizations and clients to understand and use an agreed semi-formal model the concepts of the organization and how they relate to one another
    to manage data as a resource
    for the integration of information systems
    for designing databases/data warehouses (aka data repositories)

Data modeling may be performed during various types of projects and in multiple phases of projects. Data models are progressive; there is no such thing as the final data model for a business or application. Instead a data model should be considered a living document that will change in response to a changing business. The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time.[5]“

[2]“Data Modeling for MongoDB”, Steve Hoberman, Technics Publications, LLC 2014
[3]Simison, Graeme. C. & Witt, Graham. C. (2005).Data Modeling Essentials.3rd Edition. Morgan Kauffman Publishers. ISBN 0-12-644551-6
[4]Data Integration Glossary[dead link], U.S. Department of Transportation, August 2001.
[5]Whitten, Jeffrey L.; Lonnie D. Bentley, Kevin C. Dittman. (2004). Systems Analysis and Design Methods. 6th edition. ISBN 0-256-19906-X.
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