The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It is designed to analyze healthcare data, such as electronic health records and administrative claims, converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). HADES performs advanced analytics for characterization, population-level causal effect estimation, and patient-level prediction, often in federated data networks where patient-level data remains local while aggregated statistics are shared. The software is designed to run across various technical environments and platforms, ensuring reliability through continuous integration and unit tests. HADES follows OHDSI best practices and is widely used in published studies, influencing regulatory decisions. The paper outlines HADES' principles, architecture, packages, and adoption metrics, emphasizing its role in advancing observational research and clinical decision-making.The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It is designed to analyze healthcare data, such as electronic health records and administrative claims, converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). HADES performs advanced analytics for characterization, population-level causal effect estimation, and patient-level prediction, often in federated data networks where patient-level data remains local while aggregated statistics are shared. The software is designed to run across various technical environments and platforms, ensuring reliability through continuous integration and unit tests. HADES follows OHDSI best practices and is widely used in published studies, influencing regulatory decisions. The paper outlines HADES' principles, architecture, packages, and adoption metrics, emphasizing its role in advancing observational research and clinical decision-making.