Abstract

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.

Highlights

  • Over the last decade, the concept of data lake has emerged as a reference for data storage and exploitation

  • 4.1 Overview of AUDAL To demonstrate the use of Data Lake Benchmark (DLBench), we evaluate AUDAL [16], a data lake system designed as part of a management science project, to allow automatic and advanced analyses on various textual documents and spreadsheet files

  • AUDAL is implemented on a cluster of three VMware virtual machines (VMs)

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Summary

Introduction

The concept of data lake has emerged as a reference for data storage and exploitation. A data lake is a large repository for storing and analyzing data of any type and size, kept in their raw format [3]. The concept of data lake still lacks standards [15]. There is no commonly shared approach to build, nor to evaluate a data lake. Existing data lake architectures are often evaluated in diverse and specific ways, and are hardly comparable with each other. There is a need of benchmarks to allow objective and comparative evaluation of data lake implementations. There are several benchmarks for big data systems in the literature, but none of them considers the wide range of possible analyses in data lakes

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