Data Lakes
| dc.contributor.author | Mathis, Christian | |
| dc.date.accessioned | 2018-01-08T08:08:01Z | |
| dc.date.available | 2018-01-08T08:08:01Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | By moving data into a centralized, scalable storage location inside an organization – the data lake – companies and other institutions aim to discover new information and to generate value from the data. The data lake can help to overcome organizational boundaries and system complexity. However, to generate value from the data, additional techniques, tools, and processes need to be established which help to overcome data integration and other challenges around this approach. Although there is a certain agreed-on notion of the central idea, there is no accepted definition what components or functionality a data lake has or how an architecture looks like. Throughout this article, we will start with the central idea and discuss various related aspects and technologies. | |
| dc.identifier.pissn | 1610-1995 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11020 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Datenbank-Spektrum: Vol. 17, No. 3 | |
| dc.relation.ispartofseries | Datenbank-Spektrum | |
| dc.title | Data Lakes | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 293 | |
| gi.citation.startPage | 289 |