A data pipeline orchestrates the flow of data from collection to consumption. In a web scraping context, the pipeline typically includes: crawling and scraping (ingestion), HTML parsing and field extraction (transformation), deduplication and validation (cleansing), and storage in a relational database, data lake, or streaming system (loading). This is often described as an ETL (Extract, Transform, Load) or ELT pattern.
Pipeline stages run as discrete tasks connected by queues or orchestration tools (Apache Airflow, Prefect, Dagster). Each stage can be independently scaled, monitored, and retried. Failed stages write to dead-letter queues for investigation rather than dropping data silently.
For long-running scraping projects, pipeline observability is critical: metrics on throughput, error rates, latency per stage, and data quality scores allow engineers to detect regressions before they affect downstream consumers.