The maritime industry has become a major catalyst of globalisation. Political and economic actors meet various challenges regarding cargo shipping, fishing, and passenger transport. The Automatic Identification System (AIS) records and broadcasts the location of numerous vessels which supplies huge amounts of information that can be used to analyse fluxes and their behavior. However, the exploitation of these numerous messages requires tools adapted to Big Data. Acknowledgment of origin, destination, travel duration, and distance of each vessel can help transporters to manage their fleet and ports to analyse fluxes and track specific containers based on their previous locations. Thanks to the historical AIS messages provided by the Danish Maritime Authority and ARLAS PROC/ML, Gisaïa’s open-source and scalable processing platform based on Apache SPARK, we are able to apply our pipeline of processes and extract this information from the millions of AIS messages. We use a Hidden Markov Model (HMM) to identify when a vessel is still or moving and we create “courses”, embodying the travel of the vessel. Then we can derive the travel indicators. Authors and Affiliations – GAUTIER, Willi (1) Data science Department, Gisaïa, France, GAUDAN, Sylvain (2) Chief Technical Officer, Gisaïa, France FALQUIER, Sébastien (3), Data science Department, Gisaïa, France Track – Academic Topic – Academic Level – 2 - Basic. General basic knowledge is required. Language of the Presentation – English