Pervasive Internet of Things (IoT) is a research paradigm that has attracted considerable attention nowadays. The main aim of pervasive IoT is that in the future, the everyday objects (devices) would be accessible, sensed, and interconnected inside the global structure of the Internet. But in most of the pervasive IoT applications, the resources of an IoT device such as storage, processing, and energy are limited; as such there is a need for management of resources in such applications. Multiple aspects related to the data such as the type of data, size of data, number of transmission and reception of data packets, the structure of data, etc are taken into consideration while managing the resources of pervasive IoT applications. Therefore data management is essential for the management of limited resources in such applications. This paper presents the recent studies and related information in data management for pervasive IoT applications having limited resources. This paper also proposes a parallelization based data management framework for resource-constrained pervasive applications of IoT. The comparison of the proposed framework is done with the sequential approach through simulations and empirical data analysis. The results show an improvement in energy, processing, and storage requirements for the processing of data on the IoT device in the proposed framework as compared to the sequential approach.