Data engineering is the discipline of building and running the systems that collect, store, move and prepare data. Its output is reliable data that analysts, data scientists and product features can use.
A data pipeline is an automated series of steps that moves data from where it is created to where it is stored, analysed or used. Steps along the way may extract, clean, reshape or enrich it.
Data science is the discipline of extracting insight and predictions from data by combining statistics, programming and domain knowledge. It turns raw records into answers, forecasts and models that guide decisions.
DataOps is a set of practices that applies DevOps and agile ideas to data work. Automation, testing, version control and monitoring help teams deliver data products quickly and reliably.
A data engineer builds and maintains the pipelines, storage and tooling that move data from source systems into places where it can be trusted and used. Companies usually need one before analytics or AI work can scale.
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