In today's digital era, technology has revolutionized the world, particularly through the rapid growth of digital data from Industrial Internet of Things (IIoT) sensors. This data deluge requires automated workflows for efficient handling and analysis to draw meaningful predictions and conclusions. Small and medium-sized enterprises (SMEs) face technical barriers in ingesting industrial data but are eager to integrate legacy factory machines and adopt IIoT for data-driven processes [1]. To address these challenges, we propose creating a scalable, flexible data pipeline within the ecoKI platform1. This pipeline ensures efficient data acquisition from multiple sources, streamlined processing, and predictive analytics. A robust data pipeline architecture is crucial to seamlessly handle data ingestion, processing, storage, and analysis of the massive volume of IIoT data [2,3,4]. By leveraging the ecoKI platform, SMEs can build a structured, scalable, and flexible data pipeline that efficiently processes and analyzes IIoT data, giving organizations a competitive edge in today's data-centric landscape.