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In 2010, the concept of data lake emerged as an alternative to datawarehouses for big data management. Data lakes follow a schema-on-read approachto provide rich and flexible analyses. However, although trendy in both theindustry and academia, the concept of data lake is still maturing, and thereare still few methodological approaches to data lake design. Thus, we introducea new approach to design a data lake and propose an extensive metadata systemto activate richer features than those usually supported in data lakeapproaches. We implement our approach in the AUDAL data lake, where we jointlyexploit both textual documents and tabular data, in contrast with structuredand/or semi-structured data typically processed in data lakes from theliterature. Furthermore, we also innovate by leveraging metadata to activateboth 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 onthe one hand, and a benchmark on the other hand.

Joint Management and Analysis of Textual Documents and Tabular Data within the AUDAL Data Lake