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.
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.
A data lake keeps raw data of any format cheaply for later use; a data warehouse holds cleaned, structured data modelled for fast queries and reporting. Many teams run both, or a lakehouse that combines them.
A data lakehouse combines the cheap, flexible storage of a data lake with warehouse features such as tables, transactions and fast SQL. Both kinds of work run on the same copy of the data.
Data modeling is designing how data is structured, named and related, so it can be stored efficiently and queried correctly. It applies to both application databases and analytics platforms.
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.
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