Definition
Data pipelines ingest raw events or files, clean and transform them, and load results into warehouses, lakes, or feature stores on a schedule or in real time.
Reliable pipelines are idempotent, monitored, and versioned — bad pipelines silently corrupt downstream models and dashboards.
In simple terms
A data pipeline is a factory assembly line for information: raw materials enter, get inspected and shaped, and exit as finished products ready to use.
Where you see it
- Nightly ETL jobs sync app databases to BigQuery.
- Corpus builders deduplicate and LID-filter Somali web crawl data.
- Streaming pipelines process click events for recommendations.
How it works
1.Extract
Pull from APIs, databases, files, or streams.
2.Transform
Parse, validate, join, aggregate.
3.Load
Write to warehouse, parquet files, or vector index.
4.Monitor
Alert on failures, schema drift, or volume anomalies.
Why it matters
- Most ML projects spend more time on data pipelines than on model architecture.
Often confused
Pipelines are only for big companies.
Even small teams need reproducible scripts to refresh training data.