As the concepts of IoT systems deepen, the world inclines towards safety, energy management, sustainability, efficiency, predictability and prevention. With an estimated global market spending on IoT of about $15 trillion by 2030, seamless integration of IoT devices and big data platforms is considered vital to ensure a robust, efficient, and secure IoT ecosystem. Despite its wide application the integration of IoT and big data frameworks is hindered by heterogeneous devices and protocols, semantic incompatibility, and scalability gaps between real-time IoT streams and Big Data systems. Data quality issues, lack of unified standards across edge, fog and cloud layers, security model mismatch, and fragmented data formats and vendor-specific ecosystems are additional challenges that create significant obstacles to seamless data exchange, unified analytics and reliable end-to-end integration. Through a conceptual research method of study, this paper highlights the interoperability of the IoT framework and big data analytics, the application areas of the framework providing sustainable solutions, challenges and opportunities that reflect the need for advancements of the framework strategies followed by recommendations to effectively mitigate the existing and future risks and challenges.