Using a Seed Data Generator for Realistic Test Data

When it comes to testing databases, generating realistic but randomly-generated test data can be a challenge. In many cases, replicating vast volumes of production data in development and test servers is not feasible due to logistical or legal constraints. To overcome this issue, developers and testers need access to data that accurately reflects the volume and nature of real-world data.

One way to achieve this is by using a seed data generator, such as SQL Data Generator. This powerful tool can produce high-quality, realistic test data that meets the demands of specific tests. However, it's essential to note that any changes made outside of migrations might be lost or cause errors.

In some cases, organizations invest heavily in User Acceptance Testing (UAT) environments, setting up infrastructure that mimics the production environment and using replicated production data. This approach ensures that acceptance testing, security testing, performance testing, error-handling testing, and stress/load testing can be performed in production-like conditions.

For certain types of tests, such as stress and load testing, generating test data in volumes comparable to future production requirements is crucial. To achieve this, developers need a fake data generator that can produce test data sets with characteristics and distribution similar to those of real-world data. This enables effective test-driven development from the start of the development cycle.

For example, configuring seed data for a blog using OnModelCreating can be achieved as follows:

```

modelBuilder.Entity().HasData(...);

```

Migrations only consider model changes when determining what operation should be performed to achieve the desired state. Using a seed data generator like SQL Data Generator can help developers and testers create realistic test data sets that meet specific testing requirements.

By adopting this approach, organizations can 'shift left' database testing, allowing it to be performed early in the development cycle. This ensures that essential database testing is not delayed until late in the deployment pipeline, uncovering fewer nasty surprises along the way.

Seed data generator