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A repeatable flow that ingests, transforms, validates, and moves data from sources into a place an app or model can use.
A data pipeline is the plumbing behind useful AI products: pull records from APIs, files, databases, or events; clean and normalize them; validate shape and quality; then load them into a warehouse, vector DB, feature store, or product table. Agents rely on pipelines because fresh, trusted data beats prompt stuffing. Example: nightly ingest of support tickets, chunking and embedding them, then publishing a searchable index for RAG.
Plainly
Think of Data Pipeline as a labeled box where an app keeps important things. A repeatable flow that ingests, transforms, validates, and moves data from sources into a place an app or model can use.
In practice
Use it when a feature reads, writes, migrates, validates, or audits stored information. In practice, define the owner, input, output, and failure mode before you rely on it.