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HOUDAL : A Data Lake Implemented for Public Housing

Authors
  • Scholly, Etienne
  • Favre, Cécile
  • Ferey, Eric
  • Loudcher, Sabine
Publication Date
Jan 01, 2021
Identifiers
DOI: 10.5220/0010418200390050
OAI: oai:HAL:hal-03573726v1
Source
HAL
Keywords
Language
English
License
Unknown
External links

Abstract

Like all areas of economic activity, public housing is impacted by the rise of big data. While Business Intelligence and Data Science analyses are more or less mastered by social landlords, combining them inside a shared environment is still a challenge. Moreover, processing big data, such as geographical open data that sometimes exceed the capacity of traditional tools, raises a second issue. To face these problems, we propose to use a data lake, a system in which data of any type can be stored and from which various analyses can be performed. In this paper, we present a real use case on public housing that fueled our motivation to introduce a data lake. We also propose a data lake framework that is versatile enough to meet the challenges induced by the use case. Finally, we present HOUDAL, an implementation of a data lake based on our framework, which is operational and used by a social landlord.

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