Microservice-based systems introduce significant complexity due to the distributed interactions among many independently deployed services. As a result, analyzing monitoring data and distributed traces has become an important foundation for techniques in anomaly detection, fault localization, and reliability assessment. However, research in this area is often limited by the lack of realistic, publicly available datasets that include both fault-free and faulty executions. In this paper, we present a comprehensive dataset collected from TrainTicket under simulated load, including distributed traces, metrics, and application logs. To support research on fault analysis, data were gathered not only from a clean reference version of TrainTicket but also from nine versions containing seeded faults, enabling controlled comparisons between correct and faulty executions. We describe the dataset generation process, detail the organization and content of the data, and provide a set of baseline metrics that characterize its structure and variability. The dataset aims to support researchers in developing and evaluating methods for microservice trace analysis, anomaly detection, and fault localization.