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Subspace analysis (SA) is a widely used technique for coping with high-dimensional data and is becoming a fundamental step in the early treatment of many signal-processing tasks. However, traditional SA often requires a large amount of memory and computational resources, as it is equivalent to eigenspace determination. To address this issue, specialized streaming algorithms have been developed, allowing SA to be run on low-power devices, such as sensors or edge devices. Here, we present a classification and a comparison of these methods by providing a consistent description and highlighting their features and similarities. We also evaluate their performance in the task of subspace identification with a focus on computational complexity and memory footprint for different signal dimensions. Additionally, we test the implementation of these algorithms on common hardware platforms typically employed for …