Using data modeling to create automated test data
How does Datamaker™ use data modeling?
Data modeling is the analysis of data objects that are used in a business or other context and the identification of the relationships among these data objects. Data modeling is a first step in doing object-oriented programming. As a result of data modeling, you can then define the classes that provide the templates for program objects.
A simple approach to creating a data model that allows you to visualize the model is to draw a square (or any other symbol) to represent each individual data item that you know about (for example, a product or a product price) and then to express relationships between each of these data items with words such as "is part of" or "is used by" or "uses" and so forth. From such a total description, you can create a set of classes and subclasses that define all the general relationships. These then become the templates for objects that, when executed as a program, handle the variables of new transactions and other activities in a way that effectively represents the real world.
Grid-Tools – Using Data Modeling
Grid-Tools data modeling function provides a multi-function toolset that supports all major databases. From one simple to use tool you can model your database, check data quality, issue SQL, manage database objects, compare schemas and data, edit data and much more.
The key to good data is to have a clear picture of how the tables are related. Inserting a row without matching keys to parent tables means that your test data will be invalid. Grid-Tools data modeling function lets you quickly build up a picture of how tables link together. Our fast discovery techniques include database constraints, naming standards, scanning table joins and output from case tools. Once you have built your model you can validate that the data matches with your defined model. In addition, you can use the meta-model to check the quality of production, unit and system testing database. Schemas can also be compared to identify data structure differences between actual schemas and the project’s required schema.
