In the advent of ITS (Intelligent Transportation Systems) the transportation sector generates large volumes of real-time data that needs to be collected, harmonized, interpreted, aggregated, and analysed. To this end, innovative big data processing and mining, together with optimization techniques, need to be developed and applied to better support real-time decision-making capabilities. This chapter presents an ETL (Extract-Transform-Load) architecture for intelligent transportation systems, addressing an application scenario on dynamic toll charging for highways. The ETL approach presented is responsible for preparing the data to be used by traffic prediction services, which will dynamically affect toll prices within different contexts. It relies on the adoption of “big data” technologies, to process and store large volumes of data from heterogeneous sources, provided by different highway operators, and is capable of handling real-time and historical data. The DATEX-II data model is adopted, enabling harmonization of traffic data provided by the highway operators.