Data Pipeline

A data pipeline is a chain of automated stages that collects data, optionally validates and transforms it, and delivers it somewhere useful. It may run on a schedule (batch) or continuously (streaming). ETL is one common type.

Why it matters

Reports, dashboards, and machine learning models are only as fresh and correct as the pipeline feeding them. Pipelines fail quietly: an API changes, a file arrives late, or a disk fills, and downstream numbers go stale without any visible error.

Example

web events -> queue -> validate -> enrich -> warehouse -> dashboard

A simple safeguard is a heartbeat: the final step pings a monitoring URL after each successful run, and you are alerted if the ping does not arrive. See heartbeat monitoring.

Common mistakes

• No data-quality checks, so bad records flow all the way to reports.
• No retries or alerting for failed steps.
• Tight coupling, so one source change breaks every consumer.

Related

• ETL
• Data warehouse
• Heartbeat monitoring
• Data cleaning

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