Currently we may have access to large databases, sometimes coined as Big Data, and for those large datasets simple econometric models will not do. When you have a million people in your database, such as insurance firms or telephone providers or charities, and you have collected information on these individuals for many years, you simply cannot summarize these data using a small-sized econometric model with just a few regressors. In this chapter we address diverse options for how to handle Big Data. We kick off with a discussion about what Big Data is and why it is special. Next, we discuss a few options such as selective sampling, aggregation, nonlinear models, and variable reduction. Methods such as ridge regression, lasso, elastic net, and artificial neural networks are also addressed; these latter concepts are nowadays described as so-called machine learning methods. We see that with these methods the number of choices rapidly increases, and that reproducibility can reduce. The analysis of Big Data therefore comes at a cost of more analysis and of more choices to make and to report.