nowadays, data is exploding at a faster rate than computer architectures can handle. for that reason, mathematical techniques to analyze large-scale data need be developed. stochastic iterative algorithms have gained interest due to their low memory footprint and adaptability for large-scale data. in this talk, we will present the randomized kaczmarz algorithm for solving extremely large linear systems of the form ax=y. in the spirit of large-scale data, this talk will act under the assumption that the entire data matrix a cannot be loaded into memory in a single instance. we consider different settings including when a only factorization of a is available, when a is missing information, and a time-varying model. we will also present applications of these kaczmarz variants to problems in data science.