In stream data processing, identifying whether an element has appeared before is a key challenge, particularly in real-time applications such as network monitoring and recommendation systems. To address storage and latency limitations, streaming filters serve as approximate membership structures, sacrificing some accuracy for space and speed while allowing false positives and false negatives. This chapter presents three notable streaming filters, each excelling in stability, space efficiency, and window flexibility.