Scraping and Ingestion Pipelines at Production Reliability

The problem

A legal AI product is only as good as its data freshness. Sources change layouts, go offline, return partial responses, and rate-limit aggressively. A single failed daily run can mean stale case law surfacing to users, which is worse than no answer at all.

The approach

I engineered scraping and ingestion pipelines that ran daily across heterogeneous Turkish legal sources — legislation, case law, judicial notifications through email, and other structured and semi-structured sources. Each source had its own extraction strategy tuned to its structure, its own rate-limiting profile, and its own failure detection. The pipelines ran on GCP with Docker-based workers, concurrency controls to respect source limits, and batch processing for throughput.

Reliability engineering

Zero-failure daily operation throughout the period I owned the system. Failure detection that distinguished between transient errors (retry), source-side changes (alert), and systemic issues (halt and escalate). Monitoring and alerting through Sentry.

Stack

Python scrapers, Celery workers, Redis queues, Docker, GCP Compute Engine and Cloud Storage, Sentry for error tracking, GitHub Actions for deployment.