Object Oriented Database Information

Why choose an object oriented database such as Objectivity/DB? Here’s a simplification:

In your everyday database ("relational database") the data is stored in something like columns and rows. That’s an efficient, straightforward system for some applications, like the checkbook in your pocket or the spreadsheet on your desktop. The problem is, the best cutting-edge applications often use object programming languages like Java, C#, Visual Basic .NET, C++ and Smalltalk, which use data differently: these languages treat data as objects instead of columns and rows. Checkbook technology is out of its depth.

An object oriented database, on the other hand, stores data in the same format that these brilliant, flexible languages use – in other words, it stores data as objects -- so there’s no need to translate data back and forth as your application does its thing.

And that means power, accuracy and speed, glorious speed. The general rule: if you have a business need for it, and you have complex data, and you need things done quickly (and by that we mean that you need a product developed quickly, or that it needs to work fast, or both), an object oriented database is the only way to go.

Cutting-edge companies with the most demanding data needs have already adopted object oriented databases. Objectivity/DB has proven its value in business sectors such as:

• Government
• Government Military Command & Control
• Government Security & Intelligence
• Manufacturing, Process Control & Automation
• Life Sciences & Medical Equipment
• Data-Intensive Sciences
• Financial Services
• Telecommunications
• Oil and Gas Enterprises

Objectivity's solution resolves the difficulties you would encounter, or may have already experienced with other DBMSs. Objectivity/DB and enables:

• feeding your applications as fast as you want, ingesting without run over.
• storing data in a robust database platform, with the benefits of fusing data from different sources. Easily navigate and querying the network of relationships between data.
• understanding complex data, particularly when there’s a need to map and measure relationships and flows between knowledge-processing entities.
• performing complex visual and mathematical analysis of relationships.