an increase in the communication payload and costs. The storage of sensitive data in the cloud raises concerns about data vulnerability. In a centralized and shared environment, there is a risk of unauthorized access or data breaches. Regarding data privacy, the geographic locations of cloud servers may impact data privacy. Navigating international data protection laws and ensuring data residency compliance becomes a complex task in terms of protecting sensitive information. In addition, transferring large volumes of data to and from the cloud can incur significant costs. Organizations must carefully manage data transfer to reduce their expenses. These issues are particularly relevant in the context of data processing applications, such as healthcare and sensitive data handling, where the importance of security and low latency necessitates more focused consideration [10; 11; 12; 13; 14]. Concerns about sensitive information being exposed to remote cloud data centers highlight the limitations of cloud-based processing. To overcome this challenge, fog and edge computing, which move the processing and storage tasks to locations that are closer to the data source, have been introduced as complements to cloud computing to provide storage and computing capabilities [9; 15; 16]. In these types of computing, any local device, however small, is considered capable of some processing tasks. Edge/ fog computing helps users to perform data processing at lower latency and in the most appropriate way. At present, edge- and fog-layer devices are only used for some preprocessing tasks, such as filtering, feature processing, and compression. Computationally intensive tasks such as big data analytics and machine learning training are sent out to the cloud, as they cannot be efficiently run on low-performance local edge and fog devices [17; 18]. Data processing in a layer architecture allows real-time and lightweight tasks processed on edge devices that need high storage capacities and processing capabilities to be moved to the cloud. However, important drawbacks related to the high bandwidth consumption involved in sending large quantities of data between local devices and cloud providers emerge. If the data can be stored and processed locally, closer to the IoT devices, the data transmitted through the network would be significantly reduced. We can observe a recent breakthrough in edge- and fog-layer computing devices as the processing abilities of these devices have become increasingly efficient and capable [16]. In addition, most of the data or applications pass through the edge/ fog-layer devices as a gateway before connecting to the cloud. Edge/ fog computing is potentially a good solution for data processing problems, thanks to its recently developed resource-scaling capabilities. This provides the possibility of deploying edge- and fog-layer big data management platforms using local edge/ fog devices as computing and storage nodes. Such a big data management platform can not only help to reduce latency, bandwidth consumption, and cloud budgets but can also be applied in some environments without an Internet connection. IoT applications that span various domains, ranging from smart cities to industrial environments and healthcare systems, continuously produce massive volumes of data, including sensor readings, images, videos, and other types of information. Data processing platforms handle large volumes of diverse data, making them well-suited for the processing of the varied data generated by IoT sources. IoT deployments often involve distributed environments with numerous devices spread across different locations. The distributed computing models of big data processing allow them to efficiently process data across distributed clusters, accommodating the distributed nature of IoT deployments. As many IoT applications demand real-time or near-real-time processing to enable swift responses and decision making, centralized data management approaches encounter challenges in the efficient handling of information.