Building a Training-Data Ingestion Pipeline

The pipeline transformed raw internet data into a format the model-training workflow could use. It covered scraping, cleaning, deduplication, formatting, and chunking before handing data to the next part of the system.

There were two developers maintaining and extending the pipeline. We divided implementation tasks, but we discussed the architecture together and shared responsibility for the complete system.

Each processing stage was event-driven. New data entered a queue, moved through one stage, and then passed to the next. When a stage encountered corrupt or unusable source data, the pipeline applied a fixed rejection policy rather than allowing that data to continue.

Failures that could be recovered went into dedicated queues. We also versioned the pipeline as its processing logic improved. That allowed us to replace a stage with a newer version and rerun a defined period, such as a week of data, when updated processing was required.

The production workload was 2 to 3 TB per day. The design could scale further, although the business requirement at the time did not call for a larger operating volume.