How to use Synthetic Data to catch more bugs with Neosync
Blog post from Neon
Developers face increasing pressure to create robust, scalable, and secure applications, with synthetic data emerging as a pivotal solution for testing and bug detection. Unlike production data, which carries privacy risks and only reflects processed scenarios, synthetic data is generated using machine learning models and simulation algorithms to mimic real-world data statistically and structurally without sensitive information. This allows developers to explore diverse data sets, automate testing, and conduct performance assessments without security concerns. Tools like Neosync enable rapid data generation, enhancing test coverage and application resilience. As synthetic data tools continue to advance, they present a compelling option for developers aiming to improve their workflows and application reliability.