Most projects benefit from having a data model. Read this article for an overview of the most common types.

Data models are used to represent real-world entities, but often have limitations. Avoid common data modeling mistakes for data integrity.

One of the most important steps in desiging a database is establishing the data model. Part one of a two-part article describes how to create a logical model.

When databases are built from a well-designed data model, the resulting structures provide increased value to the organization. The value derived from the data model exhibits itself in the form of ...

Unified data modeling supports features like document schema of NoSQL databases and reverse engineering of data from an existing database. It also supports visual refactoring of existing databases.

The end goal of database design is to be able to transform a logical data model into an actual physical database. A logical data model is required before you can even begin to design a physical ...

A data modeling software helps craft a high-performance database, generate reports that can be useful for stakeholders and create data definition (a.k.a. data description) language (DDL). Good ...

A multimodel database provides a single back end that supports multiple data models. It’s all about being able to choose the best data model for the job with a single storage substrate.

Increasingly, customers are demanding that their databases take on multiple chores. Emergence of multi-model databases providing developers multiple paths through APIs is the latest evidence of ...

This approach features a centralized database linked to other data stores with a common data model that carries information from one point to another, without the need to rewrite code.