This article discusses concepts for data modeling with MongoDB and how they differ from relational databases.

There is an assumption in database design that all design can be represented in data models. I have not actually heard this assumption stated, but I have not heard it challenged either. It is true that data modelers will question whether a denormalized data model intended for direct implementation as a database truly represents the enterprise’s view of its data. This is often a reason for disagreement between those responsible for logical data models and those responsible for instantiating physical databases. However, ardent purists never act in a way that questions whether a sound logical data model can capture everything that is needed to understand a database design.

This article explains why enterprise-level information knowledge management will never be attained by producing a comprehensive set of data models.

In spite of the issues and obstacles involved, I firmly believe that the use of views in databases affords the best hope of bringing developers and DBAs together, facilitating the application development process, and bridging the object-relational gap. I encourage developers and DBAs everywhere to seriously consider this approach.

As you may already know, “Oslo” is a Microsoft effort to make it easier to build data-driven applications. “Oslo” consists of three major pieces:
* A modeling language: ”M”
* A modeling and data visualization tool: “Quadrant”
* A repository: a SQL Server database
This paper is the view of “Oslo” from a data perspective. The components most important to an enterprise and databases, “M” and the repository, will be discussed first; we’ll take the data visualization tool just introduced in the “Oslo” May CTP, “Quadrant”, for a spin towards the end of the paper.

This book excerpt develops the classes needed to implement functionality for a sample application. The chapter covers object-relational mapping, how to install HSQLDB, database design, writing a Data Definition Language (DDL) script using Ant, and setting up and understanding more advanced areas of Hibernate.

Just as a transformation is required to convert an entity/relationship model into a relational database design, so is one required to convert an entity/relationship model into an object-oriented design. This may involve an automated process of attaching class names to role names, as well as manual efforts to add UML design adornments such as navigation and composition (and, of course, behavior). Because the meaning of the models is different, should the notations be different? There are strong arguments for making it so, but these articles attempted to show that this is not required for the models to make sense. Whatever notation is used, precise, semantically clear models can be produced. To do so is worthwhile, regardless of the particular experiences of the modeler.

There are four data models that form the overall data model topology within an enterprise

This article explains an application development approach advocated by many proponents of agile application development that can cause future problems for developers, while potentially sacrificing the integrity and reusability of the data.

This article continues the discussion of a central question about data modeling: Is it a descriptive or a creative (design1) process? In the first part, we looked at the data modeling environment, and data modeling problems. Here we cover two further dimensions: process and product, including the “one right answer” issue.