The Industrial Internet of Things (IIoT) generates vast amounts of data, often unstructured and heterogeneous. Processing and analyzing this data to gain insights and drive industrial automation requires sophisticated tools. This paper investigates the potential of various platforms for handling unstructured IIoT data, focusing on a qualitative comparative analysis of Node-RED, a visual programming tool, against established industrial solutions such as Apache NIFI, KNIME Analytics Platform, and Microsoft Azure Logic Apps. By evaluating these tools, we highlight their respective capabilities and limitations in managing unstructured data. Through this comparative analysis, we demonstrate the distinct features and performance of each tool and discuss their applicability in IIoT data management. The study shows that Node-RED is the most effective tool for handling unstructured IIoT data. Accordingly, we illustrate its use for a specific use case, i.e., value stream analysis.