For large datasets, the task of processing data by different techniques proves difficult because of the large dataset dimension. One way to tackle this issue is the use of PCA by transforming the original variables to a smaller number of uncorrelated variables. This monograph is inspired by this paradigm, introducing a template method which embeds both the items (rows) and the features (columns) of the database into the same low-dimensional vector space, regardless of the data type. Many well-known techniques in data analysis are used (e.g., non-negative matrix factorization, sparse and robust PCA, \(k\)-means, etc.). Moreover, there are implementations of these techniques using Python, Julia and Spark tools.