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 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 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.
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 lake is a large, low-cost store that holds raw data in its original format, structured or not, until someone needs to process or analyse it.
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