Radiation pollution has been always a critical concern, since it can cause a huge damage to humans and for nature. To minimize the damage, governments are collecting and monitoring radiation level using advanced systems. In the past years, Big data technologies such as distributed file systems, NoSQL databases and stream processing technologies was implemented in the radiation monitoring systems to improve their abilities to handle huge volume of data coming from different sources in a high speed. As Big data technologies are being improved frequently to handles the fast growth of the data, these systems need to be updated and improved periodically to adopt new technologies and to guarantee a higher control over radiation exposure. In this paper, we proposed a system called ORADIEX which is an improvement of our previous published work RaDEn [2]. It has the ability to (1) reading data from sensors and different sources, (2) processing data in real-time, (3) stores raw radiation data as it comes from sources, (4) clean data and stores it in a time-series database, (5) visualize and monitor data in real-time, (6) send alert when a high radiation level is detected and (7) allow performing advanced data retrieval operations over raw and processed data. In addition, this system was implemented and tested using a real dataset provided by the Lebanese Atomic Energy Commission (LAEC-CNRS).