In addition to volume and velocity, Big data is also characterized by its variety. Variety in structure and semantics requires new integration approaches which can resolve the integration challenges also for large volumes of data. Data lakes should reduce the upfront integration costs and provide a more flexible way for data integration and analysis, as source data is loaded in its original structure to the data lake repository. Some syntactic transformation might be applied to enable access to the data in one common repository; however, a deep semantic integration is done only after the initial loading of the data into the data lake. Thereby, data is easily made available and can be restructured, aggregated, and transformed as required by later applications. Metadata management is a crucial component in a data lake, as the source data needs to be described by metadata to capture its semantics. We developed a Generic and Extensible Metadata Management System for data lakes (called GEMMS) that aims at the automatic extraction of metadata from a wide variety of data sources. Furthermore, the metadata is managed in an extensible metamodel that distinguishes structural and semantical metadata. The use case applied for evaluation is from the life science domain where the data is often stored only in files which hinders data access and efficient querying. The GEMMS framework has been proven to be useful in this domain. Especially, the extensibility and flexibility of the framework are important, as data and metadata structures in scientific experiments cannot be defined a priori.@en

The heterogeneity of sources in Big Data systems requires new integration approaches which can handle the large volume of the data as well as its variety. Data lakes have been proposed to reduce the upfront integration costs and to provide more _exibility in integrating and analyzing information. In data lakes, data from the sources is copied in its original structure to a repository; only a syntactic integration is done as data is stored in a common semi-structured format. Metadata plays an important role, as the source data is not loaded into an integrated repository with a uni_ed schema; the data has to come with its own metadata. This paper presents GEMMS, a Generic and Extensible Metadata Management System for data lakes which extracts metadata from the sources and manages the structural and semantical information in an extensible metamodel. The system has been developed with a focus on scienti_c data management in the life sciences which is often only _le-based with limited query functionality. The evaluation shows the usefulness in this domain, but also the _exibility and extensibility of our approach which makes GEMMS also applicable to other domains. @en