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.
CI/CD is the practice of building and testing every code change automatically, then releasing changes that pass through a repeatable pipeline. It shortens the gap between writing code and running it in production.
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.
ETL (extract, transform, load) cleans and reshapes data before loading it into a target system. ELT (extract, load, transform) loads raw data first and does the transformation inside the target, usually a warehouse.
Batch processing is handling data in scheduled groups, such as hourly or nightly runs, rather than processing each record the moment it arrives. Payroll runs, overnight warehouse loads and monthly billing are common examples, where completeness matters more than instant results.
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.
A data engineer builds the pipelines and storage that move and prepare data, while a data scientist analyses that data and builds models to answer questions. Most teams need the first before the second can be productive.
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