How to work with data streams (pipelines) using generators?

Working with data streams in the form of pipelines using generators is one of the most elegant and efficient programming patterns in Python for processing large volumes of information. The main idea is to create a chain of generator functions that return elements one by one using the yield keyword, rather than loading the entire data array into the computer's RAM.

This approach provides lazy evaluation, where each element is processed on the fly only when there is a real need for it at the next stage of the pipeline. This drastically reduces RAM consumption, allowing you to easily and stably process gigabyte log files, database dumps, or streaming network data on regular computers with limited resources.

To build a pipeline, you need to write separate functions for each processing stage, for example, a file reading function, a row filtering function by condition, a data parsing function, and a results saving function. Then these functions are neatly connected into a single call chain, where the output of one generator becomes the input argument for the next generator in the sequence.

In practice, this looks as follows: a function reads a file line by line and yields lines, a filtering function discards empty lines, and a transformation function splits the text into fields and forms a dictionary. The result is a flexible, modular, and easily extensible architecture where individual components can be reused in other tasks, tested in isolation, and combined in various orders.