## 3 Related Work
### Data Administration
Research approaches therefore attempt to create a reference model for a data lake in which, besides the raw data, harmonized data (i.e., data with a consolidated schema) and distilled data (i.e., aggregated data) are also kept in dedicated zones. In a sandbox zone, data scientists can play around with the data at will, in order to enable exploratory data analytics and provide the full flexibility of a data lake [66]. While this unlimited freedom initially sounds appealing, this might flood the data lake with too much irrelevant data--the data lake increasingly degenerates into a data swamp in which no useful data can be found. This can be prevented on the one hand by a systematic metadata management to keep an overview of all collected data [67] and on the other hand by sanitizing the raw data in terms of detecting integrity violations in the data and dealing with them to maintain the quality [68]. In practice, however, such monolithic data stores are prone to be exceedingly difficult to manage in terms of governance and operation. Research therefore aims to develop a data mesh in which the central data lake is split into distributed, independently managed data silos [69]. Other approaches focus on achieving an optimal trade-off between the flexibility of data lakes (i.e., support for all current and future use cases) and the structured organization of a data warehouses (i.e., efficient data processing and effective information retrieval) [70]. To this end, the data lakehouse architecture supports both classic BI and exploratory data analytics by means of a transaction layer that provides a logical ETL process on top of a data lake [71]. Data Provisioning.In recent years, self-service BI has become increasingly relevant, i.e., users should be able to conduct customized analytics autonomously for their individual use cases [72]. However, a basic requirement to this end is that there is simple access to the relevant data of the required quality [73]. Due to the large amount of data required for today's analyses, the data available internally in a corporation is often not sufficient. Therefore, data from external providers are required as well. As a consequence, data is not only a commodity but has also become a tradable good. To address this new strategic role of data, infrastructures for data marketplaces are being developed to allow customers to find and obtain data products [74]. However, a data marketplace is not a traditional warehouse, but because of the intangible nature of data, rather, a storefront for the available data products. Customers can select the data they want from a data catalog and the marketplace then acts as an interface to the respective data store [75]. From a data provider perspective, one of the most important functionalities that a data marketplace has to offer for this purpose is a comprehensive metadata management system that allows them to describe their data. This includes descriptive information about the data themselves (e.g., their data model or content descriptions) as well as information about the conditions under which they are permitted to be accessed (e.g., their price or their permitted usage) [76]. Since a marketplace usually represents the storefront for multiple third-party data providers, the metadata of all these providers must be merged to assemble a holistic data catalog [77]. From a customer perspective, the data marketplace must facilitate data retrieval. To this end, two main functionalities must be supported: On the one hand, it must be possible to find relevant data from all available sources (e.g., content- or quality-wise), and on the other hand, data acquisition has to be simple [78]. Comprehensive metadata management is required to this end as well [79]. One of the most important aspects of a data marketplace for both sides, however, is trust. Only if data providers can assume that confidentiality and privacy are guaranteed with regard to their data and customers can rely on the authenticity and quality of the offered data, they will use a data marketplace [80].