Synthetic Data Generation for Realtime Data Pipelines

Abstract

This study discusses synthetic data synthesis for real-time data pipeline enhancements. Many companies can scale, cost-effectively, and privately train and test machine learning models using synthetic data. Key applications include advanced simulations, model effectiveness, and privacy. Despite data realism, computational complexity, and domain-specific requirements, generative models and integration approaches are promising. Legal and ethical issues must be resolved for acceptance. This study proves synthetic data's effectiveness, dependability, and regulatory compliance, revolutionising data-driven systems.