The interTwin project is developing a robust Digital Twin Environment (DTE) that integrates real-time data, simulations, and system orchestration. Key challenges include connecting workflow orchestration with real-time data systems, interfacing with cloud and HPC infrastructure, and providing scalable data processing and accessible APIs. Core components include the Infrastructure Manager for deploying Digital Twins, OSCAR for triggering real-time jobs in Kubernetes, and SQAaaS for automated quality assurance in ML workflows. Ophidia supports multidimensional data analytics with provenance tracking, while yProv manages workflow traceability in coordination with SQAaaS, openEO, and Ophidia. openEO provides an API for Earth Observation workflows, now enhanced for real-time data via OSCAR. To support scalable ML, the itwinai Toolkit offers an open-source solution for running ML workloads on HPC and cloud systems, featuring unified logging, provenance tracking, and quality metrics. This document describes the final release of the core components in terms of functionalities, integration with the DTE ecosystem, and testings performed.