Thus, by strategically integrating computational capabilities closer to the data source, within the edge and fog layers, it is possible to facilitate local data processing, reducing the necessity of transmitting all data to centralized cloud infrastructure. This not only addresses latency concerns but also optimizes bandwidth usage, making it particularly beneficial in scenarios where network resources are limited. Furthermore, the decentralized nature of edge/ fog computing contributes to enhanced security and privacy by allowing sensitive data to be processed closer to their origin. Insummary, the cooperative interconnection between IoT applications, data management, and edge/fog computing represents a strategic approach to meet the evolving demands of efficient, real-time, and secure data processing within the dynamic landscape of the Internet of Things. In addition, the requirements of big data processing, such as the training of machine learning models [19; 20; 21], stream processing [22], and event processing [23; 24], raise scalability issues and require significant computing and storage resources [25; 26]. Fortunately, big data management and processing platforms such as Apache Spark [27], Apache Flink [28], and Apache OpenWhisk [29] address these concerns by facilitating distribution and collaboration. In such systems, big data processing tasks are performed using the collaborative power of nodes in clusters. With recent increases in the resource capacity of edge/fog devices and the capability of big data processing platforms, it has become increasingly important to explore the deployment of big data processing platforms on resource-constrained edge/fog devices. The opportunity to combine recent advances in edge/fog devices' computing capabilities and big data processing platforms motivates the work described in this paper. To address data processing across all computing environments, while previous works have focused on specific data processing paradigms (batch, stream, or function processing) [30; 31; 32; 33; 34; 35; 36] and the capabilities of resource-constrained devices [37; 38; 39], our research addresses the need for a comprehensive examination of the performance of these platforms across three paradigms: batch processing, stream processing, and function processing. This comprehensive approach, coupled with the inclusion of various applications, such as image classification, object detection, and image resizing, adds a layer of comprehensiveness to the evaluation. This work helps to elucidate the efficiency gains and potential benefits that the collaborative deployment of local computing resources across different computing layers can offer in the field of big data processing. The specific research gap that our work addresses is the need for a comprehensive understanding of big data processing in diverse computing environments, considering different paradigms and applications. In this paper, we ask the following question: Can edge- or fog-layer devices host big data management platforms for data science applications, such as image classification, object detection, and event-based image processing? To answer this question, we present an extensive comparison between edge-/fog-layer big data management platforms and other cloud-based solutions regarding their performance. We evaluate three big data management and processing platforms--Apache Spark, Apache Flink, and Apache OpenWhisk--based on three different computing paradigms, namely batch processing, stream processing, and function processing, as demonstrated in Figure 1. Real-world applications such as image classification, object detection, and image processing are used to evaluate the big data platforms in edge, fog, and cloud layers.