In this work, we present LENTA (Longitudinal Exploration for Network Traffic Analysis), a system that supports the network analysts to easily identify traffic generated by services and applications running on the web, being them benign or possibly malicious. First, LENTA simplifies analysts' job by letting them observe few hundreds of clusters instead of the original hundred thousands of single URLs. Second, it implements a self-learning methodology, where a semi-supervised approach lets the system grow its knowledge, which is used in turn to automatically associate traffic to previously observed services and identify new traffic generated by possibly suspicious applications. This lets the analysts easily observe changes in the traffic, like the birth of new services, or unexpected activities. We follow a data driven approach, running LENTA on real data. Traffic is analyzed in batches of 24-hour worth of traffic. We show that LENTA allows the analyst to easily understand which services are running on their network, highlights malicious traffic and changes over time, greatly simplifying the view and understanding of the traffic.