The analysis of streaming PCA has gained significant traction through the analysis of an early simple variant: Oja's algorithm, which implements online projected gradient descent for the trace objective. Several other streaming PCA algorithms have been developed, each with their own performance guarantees or empirical studies, and the question arises whether there is a relationship between the algorithms. We show that the Grassmannian Rank-One Subspace Estimation (GROUSE) algorithm is indeed equivalent to Oja's algorithm in the sense that, at each iteration, given a step size for one of the algorithms, we may construct a step size for the other algorithm that results in an identical update. This allows us to apply all results on one algorithm to the other. In particular, we have (1) better global convergence guarantees of GROUSE to the global minimizer of the PCA objective with full data; and (2) local convergence guarantees for Oja's algorithm with incomplete or compressed data.