- Generate realistic data for your SQL Server database that match to your column definitions
- identify performance issues or system deficits
- create load tests under realistic conditions and simulate the production environment
- specify userdefined parameters for every column type: ex. Value lists,
random generation, nullable
- culture independent data generation
- preview your generated content
- export generated content as sql 'INSERT INTO' script
- one click insertion in your database table
Screenshots & Videos
When development enters the stabilization and performance phase, the quality of the end product depends heavily on the testing process. In these conditions, a real life test environment offers the opportunity of assessing the scalability and performance of the tool prior to a formal delivery to the client by simulating the production environment.
The usage of the client’s database for testing purposes is usually under the influence of increased importing or transferring costs translated in time and resources, backed up by the risks associated with confidential information disclosure.
Through its Data Generator feature, SQLDog Utility Suite offers an intuitive, user-friendly test data creation process that can easily identify system vulnerabilities and performance issues associated with the integration of the developed component within the real production environment.
Considering a reporting tool developed to be applied on a client database that consists of more than 5,000 lines and 50 columns, which detain the entire clients’ portfolio information, the developer can easily create the framework of the database by defining a test database parenting a test table that contains the needed number of rows and columns. Once the framework has been generated, the developer can start to populate the columns with realistic data using the generation parameters.
The developer can begin with reproducing the ClientID number using the Sequential Increment option, starting from ClientID = 1 and incrementing by 1 each ClientID until client number 5,000 receives its corresponding ID number. Knowing that the real database contains clients located in 12 countries, the developer will further generate a similar column variable by using the random generator option with a minimum value equal to 1 and a maximum value equal to 12, distributed randomly among clients.
The unique random generator is useful when defining the current sales volume of each client, starting from a minimum value of 100 monetary units and reaching up to 15,000 monetary units. The estimated cost of each product sold represents 70 percent of the selling price, which could help the developer create a new variable using a query based option by subtracting 30 percent of the sales volume in order to estimate the production cost.
Based on a generic list of names, the database can be enriched by importing the names and surnames of the clients from the pre-defined list. The developer does not forget to add some Null value columns that will prove useful when testing the tool on working with Null values.
For a faster data generation process, similar columns can be updated in the same time using the multiple selection check boxes existent in the columns area. The developer can also use a culture independent data generation approach. Once all columns have been defined in terms of generation parameters, the testing data environment can be previewed and reassessed for prior testing changes and adjustments, if needed. The database can also be subjected to automatic insertion or exporting as SQL script (as Insert).
After recurrent sets of testing, the database can be adapted as to fully test and prepare a defect free final product to be delivered to the client.