  In the last few years, the concept of data lake has become trendy for data
storage and analysis. Thus, several design alternatives have been proposed to
build data lake systems. However, these proposals are difficult to evaluate as
there are no commonly shared criteria for comparing data lake systems. Thus, we
introduce DLBench, a benchmark to evaluate and compare data lake
implementations that support textual and/or tabular contents. More concretely,
we propose a data model made of both textual and raw tabular documents, a
workload model composed of a set of various tasks, as well as a set of
performance-based metrics, all relevant to the context of data lakes. As a
proof of concept, we use DLBench to evaluate an open source data lake system we
previously developed.
