Previous sections covered the architecture of data transformations and we also learned how to transform data in many ways. When we work on data transformation tasks (and on all other tasks during data analysis life cycle), we also need to consider performance of our tasks. This is important because when we miss it, tasks become resource consuming and users also become disappointed due to delayed data. This section shows some techniques of how to reach desired performance.
Performance considerations
Writing correct code
When the buzzword performance is mentioned, plenty of people start thinking about indexes. However, there are also many simple but feasible things to be considered. As well as correctly (de)normalized base tables...
