Data Pipeline

What is Data Pipeline?

A pipeline is the engineered version of 'move this data from there to here, but do these things to it on the way'. The classic shape is Extract-Transform-Load (ETL); modern variants flip the order to Extract-Load-Transform (ELT) so the transform happens inside the destination warehouse where compute is cheap. Pipelines can be batch (one job per scheduled interval) or streaming (every event flows through individually as it arrives). Reliability is the key engineering property: a pipeline that runs 99% of the time but fails silently on the 1% creates worse data trust than a pipeline that doesn't exist. Modern pipelines include observability — every run logged, every record traced, every failure alertable.

Why it matters

• Makes data movement repeatable, observable, and recoverable — vs ad-hoc copy-paste.
• Powers near-real-time analytics by streaming events instead of batching daily.
• Foundation for any non-trivial integration — once volume passes a threshold, ad-hoc breaks.

Use cases

• CRM-to-warehouse pipeline. Every CRM change streamed to the warehouse within minutes.
• Event pipeline. Product events flow through to marketing automation for trigger-based plays.
• Reverse-ETL pipeline. Modeled data from the warehouse pushed back into operational tools.