SQL Data Generator

SQL Data Generator

Pseudonymizing your data with SQL Data Generator

Imagine that you are the CIO of AdventureWorks. Out of a blue sky comes an order from Taxman that you supply details of all your sales, along with the tax charged to your customers. They want to be able to check that the total tax you pass on to them is correct, and is the Read more

SQL Data Generator

Realistic, simulated data for testing, development and prototypes

Generating realistic test data, which reflects accurately the nature and distribution of the data it is emulating, is a challenging task. The task is made more complex if you need to generate that data in different formats, for the different database technologies in use within your organization. This article proposes a scheme for using Redgate Read more

SQL Data Generator

Generating test data in JSON files using SQL Data Generator

SQL Data Generator is adept at filling SQL Server databases with ‘spoof’ data, for use during development and testing activities. However, what if instead of a SQL Server database full of fake data, you need a JSON file? Perhaps you need to run some tests in MongoDB, or Azure Cosmos DB. Maybe you need a Read more

SQL Clone

Protecting production data in non-production environments

A traditional IT problem in many organizations is that the development and operations teams each work in their own silo, and each tends to regard the other as having different, and often conflicting, goals. Developers are focused on the needs of just one application. Operations must keep the whole maze of production systems running throughout Read more

SQL Data Generator

Basic data masking for development work using SQL Clone and SQL Data Generator

This article describes a lightweight copy-and-generate approach for making a sanitized version of a production database available to development teams with SQL Clone and SQL Data Generator. We build the latest database version (schema only), then copy into it, from the production database, the data for any tables and columns that don’t require masking. For Read more

SQL Data Generator

Automatically build-and-fill multiple development databases using PowerShell and SQL Data Generator

When you are working with a database, you always need data. This is why you need to stock the database with data after you build it. Sometimes, you just want a large number of made-up customer details, sales figures or the like. You also will need columnar data, sometimes known as ‘static data’ or ‘enumerations’, Read more

SQL Data Generator

Automatically filling your SQL Server test databases with data

This article explains how you can use SQL Data Generator (SDG) to automate data provisioning for test databases, during the database development cycle. Having first created an SDG project file for a database, to define the data generation strategy, we can write a command line batch script, or PowerShell script, which uses a build script Read more

SQL Data Generator

How to generate realistic text data using SQL Data Generator

SQL Data Generator (SDG) is very handy for making a database come alive with what looks something like real data, and, once you specify the empty database, it will do its level best to oblige. To get the best results though, you need to provide SDG with some hints on how the data ought to Read more

SQL Data Generator

Generating realistic dates using SQL Data Generator and Python

When you’re generating test data, you have to fill in quite a few date fields. By default, SQL Data Generator (SDG) will generate random values for these date columns using a datetime generator, and allow you to specify the date range within upper and lower limits. This is fine, generally, but occasionally you need something Read more

SQL Data Generator

How to start producing realistic test data with SQL Data Generator

Many organizations invest heavily in their User Acceptance Testing (UAT) environments, setting up infrastructures that mimic the production environment, often using replicated production data. This is important because it means they can perform acceptance testing as well as security testing, performance testing, error-handling testing, and stress/load testing in production-like conditions, and so be confident they’ll Read more