A data pipeline is the sequence of steps that moves information from a source to a usable destination. For web data, that sequence may include URL discovery, request scheduling, fetching, parsing, validation, deduplication, and storage. Thinking of it as a pipeline makes failures easier to locate than treating “the scraper” as one opaque program.
A practical layout
Keep each stage’s input and output clear. A fetcher returns a response plus status and timing details; a parser turns that response into candidate records; validation checks required fields and types; storage writes accepted records while preserving source metadata. Failed requests and invalid records should have their own paths so they can be reviewed or retried safely.
The stages do not all need separate services. A small job can run them in one process. Separation becomes useful when one stage has a different workload, needs independent retries, or is causing delays elsewhere. Measure where time and errors occur before adding queues or distributed workers.
Idempotency matters when a task retries. A stable record key or URL fingerprint can prevent a repeated run from silently creating duplicate rows. Keep enough provenance to tell a fresh result from a retry or an update.
Leave a Reply