Data engineers build the systems that collect data from where it is created, transform it into forms people and machines can use, and deliver it reliably to those who need it. This lesson maps the landscape so every later topic has a place.
The lifecycle
Generation in source systems, ingestion into the platform, storage in raw and refined forms, transformation into models, serving to analytics, applications and machine learning, and the cross-cutting concerns of quality, security, orchestration and cost.
Batch and streaming
Batch processes data in scheduled chunks; streaming processes events continuously as they arrive. Most platforms use both, and choosing wrongly is a common source of complexity.
The engineer’s responsibilities
Correctness, timeliness, reliability, cost and the trust of consumers. A pipeline that runs but produces wrong numbers is worse than one that fails loudly.
Action Step
Draw the data lifecycle for an organisation you know, naming the source systems, the ingestion methods, the storage layers and the consumers. Mark where batch and streaming each apply.
This course is vendor-independent: it is not affiliated with, endorsed by or accredited by any tool vendor or certification body, names products only for identification, and issues no credential. Verify current documentation before applying anything in production.