Data engineering

Data engineering is the work of getting data from where it is produced to where it is useful. Data engineers build pipelines that pull data from application databases, APIs and event streams, store it in a warehouse or lake, clean and reshape it, and serve it to dashboards, applications and machine learning models.

How it works

Most pipelines have four stages: ingest, store, transform and serve. Ingestion uses batch jobs or change data capture and streams. Storage is often a columnar database or object storage. Transformations turn raw tables into modelled ones, through ETL or ELT jobs.

For example, 2 million orders a day replicate from the application database into the warehouse with CDC. A scheduled job builds a daily_revenue table, and the finance dashboard refreshes from it every 15 minutes.

Why it matters in production

Pipelines are production systems. A renamed column upstream can silently break every report downstream. Data teams need the same practices as service teams: schema change alerts, data quality checks, freshness monitoring and on-call ownership.