Detecting and monitoring radiation level is one of the critical duties for governments and researchers because of the high threats it oppose to humans. It was challenging in the past century to have a centralized radiation monitoring system until the rise of IoT (Internet of Things). Radiation level is measured using wireless sensors that outputs data which are transferred to a back-end server that monitors radiation and alerts when high radiation levels are detected, the server also stores the data for further analysis. The traditional data warehousing systems cannot handle this type of data any more due to (1) data collection speed, (2) rapid data growth, and (3) data diversity. With the rise of Big Data notion, new technologies are developed to handle data with similar characteristics. In this paper, we proposed RaDEn a scalable and fault-tolerant radiation data engineering system that relies on Big Data technologies such as Hadoop, Kafka, Spark, and Hive. The system is responsible of (1) reading data from sensors and other sources, (2) monitor the radiation level in real-time, (3) storing the data, and (4) providing on-demand data retrieval to users. In addition, we have implemented our system and conducted experiments in a real case scenario in collaboration with the department of environmental radiation control at the Lebanese Atomic Energy Commission (LAEC-
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