Due to the growth of the Industrial Internet of Things (IIoT), Cyber Physical Systems (CPS) have emerged as pivotal technology. However, CPS has a need to share and transfer data in the different domains. In the realm of industrial automation, OPC UA (Unified Architecture) is widely used for the standard communication between the different devices made by different vendors of the plant site. We have proposed a scalable data pipeline in the platform of ecoKIa, a research project to improve energy efficiency for Small and Medium-sized Enterprises. First, we ingest an ontology-based OPC UA data access, which enables the interlinking of OPC UA information with our data model for the plant site. To avoid communication overhead, run-time data are loaded on-demand and stored into the database. Then, the storage database building blocks (BBs) are used by machine learning for energy consumption optimization. A proof of concept of our data pipeline is demonstrated through the heating thermostat demonstrator at the Process-to-Oder (P2O) Lab on TUD premises. Nevertheless, in future work, we will deploy our data pipeline as a container for better interoperability and flexibility.