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.
Apache Spark is an open-source engine for processing large datasets in parallel across a cluster of machines. Teams use it for batch jobs, streaming and machine learning.
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.
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.
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