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The Jiangmen Underground Neutrino Observatory (JUNO) [1] in southern China has been designed to determine the neutrino mass ordering and precisely measure the oscillation parameters. JUNO plans to start datataking in 2025, with an expected event rate of approximately 1 kHz. This translates to around 60 MB of byte-stream raw data being produced every second, resulting in data volumes of 2PB per year. To address the challenges posed by this massive amount of data, JUNO is conducting data challenges on its distributed computing resources. The data challenges aim to achieve several objectives, including understanding the offline requirements, accurately estimating the necessary resources, identifying potential bottlenecks within the involved systems, and improving overall performance. The ultimate goal is to demonstrate the effectiveness of the JUNO computing model and ensure the smooth operation of the entire data processing chain, encompassing raw data transfer, simulation, reconstruction, and analysis. Furthermore, the data challenges seek to verify the availability and effectiveness of monitoring systems for each activity.