Gait analytics is utilized for a wide range of health-related applications, ranging from fitness tracking to medical diagnostics, and beyond. Inertial measurement units that are ubiquitously available in mobile devices provide an accessible means of gait analysis but differences in sensor specifications, placement, and recording protocols limit comparability across studies. This study developed and evaluated an analysis-centered data warehouse and standardized quality-assessed processing pipeline to facilitate the integration and joint analysis of heterogeneous gait accelerometer data. Six publicly available datasets including a total of 935 persons were integrated into a star-schema data warehouse, and feature comparability was assessed across datasets and sensor locations. Gender classification served as a proof-of-concept downstream analysis. Gait-cycle extraction, which serves as foundation for feature extraction, was successfully achieved with an accuracy above 99% for the dataset that provides ground truth information. Comparability varied across datasets and sensor configurations, with correlation coefficients between average gait cycles ranging from 0.5 to close to 1.0. Centrally positioned sensors generally show greater correspondence than peripheral sensors, although differences in statistical features persist even between sensors at nominally identical locations. Gender classification performance remains largely near chance level, and no consistent benefit from pooling datasets could be established. These findings demonstrate that heterogeneous gait accelerometer data can be integrated and processed within a common analytical framework, but standardized processing alone does not ensure comparability of the resulting features. The proposed open-source framework implemented in Python provides a foundation for reproducible cross-study gait analysis and for investigating residual heterogeneity associated with datasets and sensor configurations.