Big cities show a wide public transport network that allows people to travel within the cities. However, with the overcrowding of big urban areas, the demand for new mobility strategies has increasing. Every day, citizens need to commute fast, easily and comfortable, which is not always easy due to the complexity of the public transport network. Therefore, this paper aims to explore the ability of Big Data technologies to cope with data collected from public transportation, by inferring automatically and continuously, complex mobility patterns about human mobility, in the form of insightful indicators (such as connections, transshipments or pendular movements), creating a new perspective in public transports data analytics. With special focus on the Lisbon public transport network, the challenge addressed by this work, is to analyze the demand and supply side of transportation network of Lisbon metropolitan area, considering ticketing data provided by the different transportation operators, which until now were essentially obtained through observation methods and surveys.