Note that a data lake does not necessarily replace the data warehouse, but rather often complements it. As such, you store your raw data from various sources in a centralised data store (Hadoop-like, NoSQL, etc.). From there, you prune, clean, select, and potentially aggregate data that you would like to provide in a quality-controlled way to your business users, in a data warehouse. This data warehouse most often will be a more traditional relational data store (usually some flavour of SQL database), which allows users to select data from a curated, pre-selected slice of the overall data stored in the data lake, and which enables easier integration with common reporting tools, whether more traditional standard reporting tools or self-service BI tools.
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Note that a data lake does not necessarily replace the data warehouse, but rather often complements it. As such, you store your raw data from various sources in a centralised data store (Hadoop-like, NoSQL, etc.). From there, you prune, clean, select, and potentially aggregate data that you would like to provide in a quality-controlled way to your business users, in a data warehouse. This data warehouse most often will be a more traditional relational data store (usually some flavour of SQL database), which allows users to select data from a curated, pre-selected slice of the overall data stored in the data lake, and which enables easier integration with common reporting tools, whether more traditional standard reporting tools or self-service BI tools.