Recently, the data mining community has focused on a new challenging model where data arrives sequentially in the form of continuous rapid streams. It is often referred to as data streams or streaming data. Many real-world applications data are more appropriately handled by the data stream model than by traditional static databases. Such applications can be: stock tickers, network traffic measurements, transaction flows in retail chains, click streams, sensor networks and telecommunications call records. In this paper we propose a new approach, called SPEED (Sequential Patterns Efficient Extraction in Data streams), to identify sequential patterns in a data stream. To the best of our knowledge this is the first approach defined for mining sequential patterns in streaming data. The main originality of our mining method is that we use a novel data structure to maintain frequent sequential patterns coupled with a fast pruning strategy. At any time, users can issue requests for frequent sequences over an arbitrary time interval. Furthermore, our approach produces an approximate support answer with an assurance that it will not bypass a user-defined frequency error threshold. Finally the proposed method is analyzed by a series of experiments on different datasets.