Abstract
During recent years, data lakes emerged as a way to manage large amounts of heterogeneous data for modern data analytics. Although various work on individual aspects of data lakes exists, there is no comprehensive data lake architecture yet. Concepts that describe themselves as a “data lake architecture” are only partial. In this work, we introduce the data lake architecture framework. It supports the definition of data lake architectures by defining nine architectural aspects, i.e., perspectives on a data lake, such as data storage or data modeling, and by exploring the interdependencies between these aspects. The included methodology helps to choose appropriate concepts to instantiate each aspect. To evaluate the framework, we use it to configure an exemplary data lake architecture for a real-world data lake implementation. This final assessment shows that our framework provides comprehensive guidance in the configuration of a data lake architecture.
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