Describe the business logic of the segmentation service: how did the data processing pipeline work?

In the segmentation service, the business logic was built around a data processing pipeline that sequentially transformed and filtered input data to identify target user segments.

The pipeline typically included the following stages:

1. Data collection — obtaining raw data from various sources (logs, databases, APIs).
2. Cleaning and normalization — removing duplicates, filling in missing values, standardizing data formats.
3. Feature aggregation and calculation — computing metrics, creating new features important for segmentation.
4. Applying segmentation rules — filtering and grouping users based on specified criteria (e.g., activity, demographics).
5. Saving results — storing segments in a database or cache for further use.

Each stage was implemented as a separate module or function, allowing easy scaling and modification of the pipeline. In Golang, this is often implemented using channels and goroutines for parallel data processing, which increases performance.