The Industrial Internet of Things (IIoT) promises automation, efficiency, and data-driven decision-making by real-time data collection and analysis. However, traditional IIoT architectures are cloud-centric and, therefore, struggle to handle large volumes of data, edge bandwidth constraints, and data confidentiality. Distributed edge-to-cloud computing emerges as a potential solution, also paving the ground for edge-to-cloud data analytics and distributed Artificial Intelligence (AI) to obtain insights for decision-making and predictive maintenance. Despite the potential, however, there is a lack of comprehensive studies identifying key requirements for distributed edge-to-cloud IIoT and analyzing to what extent emerging IoT platforms meet those requirements. The scope of this article is to survey existing literature to identify key requirements in IIoT from the perspective of distributed edge-to-cloud computing. We provide a comparative analysis of three prominent IoT platforms, namely ThingsBoard, Eclipse Ditto, and Microsoft Azure IoT, and assess how these platforms meet the key IIoT requirements. Finally, we identify open challenges and potential research opportunities based on the insights gained from the analysis of the three IoT platforms, thereby setting the stage for future work.