Initiatives like Industry 4.0 aim to accelerate data-based decision support in enterprises. These initiatives generate large amounts of heterogeneous data that need to be stored and managed. It is not always clear what benefits this data will later bring to the company. As a result, it is usually not possible to decide at the time of data collection whether and what value the data will have. To avoid losing any potentially important information, all data are stored in their raw format in an enterprise-wide data lake.
While concepts for the topic of data lakes exist, there is no comprehensive view on the different partial aspects. Thus, it remains unclear how enterprises can conceptualize and realize a successful data lake. Interdependencies between existing concepts are often unresearched. The goal of this project is therefore to define a framework for an implementable data lake architecture.
Funded by: Robert Bosch GmbH
[GGHE21] Giebler, C. et al.: The Data Lake Architecture Framework: A Foundation for Building a Comprehensive Data Lake Architecture. In: Proceedings der 19. Fachtagung Datenbanksysteme für Business, Technologie und Web (BTW 2021) (2021).
[GGHS20] Giebler, Corinna; Gröger, Christoph; Hoos, Eva; Schwarz, Holger; Mitschang, Bernhard: A Zone Reference Model for Enterprise-Grade Data Lake Management. In: Proceedings of the 24th IEEE Enterprise Computing Conference (EDOC 2020) (2020).
[GGHE20] Giebler, Corinna; Gröger, Christoph; Hoos, Eva; Eichler, Rebecca; Schwarz, Holger; Mitschang, Bernhard: Data Lakes auf den Grund gegangen. Datenbank-Spektrum. 20 (1), 57–69 (2020).
[GGHS19] Giebler, Corinna; Gröger, Christoph; Hoos, Eva; Schwarz, Holger: Modeling Data Lakes with Data Vault: Practical Experiences, Assessment, and Lessons Learned. In: Proceedings of the 38th Conference on Conceptual Modeling (ER 2019) (2019).
[GGHS19] Giebler, Corinna; Gröger, Christoph; Hoos, Eva; Schwarz, Holger; Mitschang, Bernhard: Leveraging the Data Lake - Current State and Challenges. In: Proceedings of the 21st International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2019) (2019).