  In 2010, the concept of data lake emerged as an alternative to data
warehouses for big data management. Data lakes follow a schema-on-read approach
to provide rich and flexible analyses. However, although trendy in both the
industry and academia, the concept of data lake is still maturing, and there
are still few methodological approaches to data lake design. Thus, we introduce
a new approach to design a data lake and propose an extensive metadata system
to activate richer features than those usually supported in data lake
approaches. We implement our approach in the AUDAL data lake, where we jointly
exploit both textual documents and tabular data, in contrast with structured
and/or semi-structured data typically processed in data lakes from the
literature. Furthermore, we also innovate by leveraging metadata to activate
both data retrieval and content analysis, including Text-OLAP and SQL querying.
Finally, we show the feasibility of our approach using a real-word use case on
the one hand, and a benchmark on the other hand.
