Research papers in big data management for the Internet of Things (IoT) focus on addressing the challenges of storing, processing, and analyzing massive, heterogeneous, and continuous streams of data generated by IoT devices. IoT ecosystems produce high-volume, high-velocity, and high-variety data from sensors, actuators, smart devices, and industrial machinery, which requires efficient management frameworks to extract actionable insights and enable real-time decision-making. Researchers have explored architectures that combine cloud, fog, and edge computing to handle data collection, preprocessing, storage, and analytics in a distributed and scalable manner. Data management strategies include stream processing, in-network aggregation, compression, indexing, and distributed storage to optimize bandwidth usage, reduce latency, and ensure reliability. Security and privacy mechanisms, including encryption, access control, and anonymization, are integrated to protect sensitive IoT data. Big data analytics techniques such as machine learning, deep learning, and predictive modeling are applied to IoT datasets for anomaly detection, predictive maintenance, behavior analysis, and optimization of industrial, healthcare, and smart city applications. Frameworks leveraging blockchain, distributed databases, and semantic interoperability are also proposed to enhance transparency, auditability, and integration across heterogeneous IoT platforms. Despite advancements, challenges remain in achieving scalable, energy-efficient, and low-latency data management while maintaining data quality, consistency, and security across large-scale IoT deployments. Overall, the literature emphasizes that effective big data management is a cornerstone for unlocking the full potential of IoT, enabling intelligent, reliable, and real-time decision-making in diverse applications.