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.
A data warehouse is a central database built for analysis, holding cleaned, structured data from many systems. Analysts and BI tools query it for reports, dashboards and planning.
Reverse ETL is syncing modelled data from a warehouse back into operational tools such as CRMs, support desks and ad platforms. Teams can then act on it where they work.
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.
Change data capture detects inserts, updates and deletes in a database and streams those changes to other systems. Targets stay in sync without repeated full reloads.
Data integration is combining data from different sources into a consistent, unified view. Sources include applications, databases, files and partners, and the result serves people and systems alike.
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.
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