DLBench+: A benchmark for quantitative and qualitative data lake assessment

In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly shared criteria for comparing data lake systems. Thus, w...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sawadogo, Pegdwendé N. [verfasserIn] Darmont, Jérôme [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Data lakes Benchmarking Textual documents Tabular data Quality and metrics

Übergeordnetes Werk:	Enthalten in: Data & knowledge engineering - Amsterdam [u.a.] : Elsevier, 1985, 145
Übergeordnetes Werk:	volume:145

DOI / URN:	10.1016/j.datak.2023.102154

Katalog-ID:	ELV009687416

Internformat


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520			\|a In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly shared criteria for comparing data lake systems. Thus, we introduce in this paper 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 CSV 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. Beyond a purely quantitative assessment, we also propose a methodology to qualitatively evaluate data lake systems through the assessment of user experience. As a proof of concept, we use DLBench+ to evaluate an open source data lake system we developed.
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650		4	\|a Benchmarking
650		4	\|a Textual documents
650		4	\|a Tabular data
650		4	\|a Quality and metrics
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912			\|a GBV_ILN_2055
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912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
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912			\|a GBV_ILN_2153
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matchkey_str	sawadogopegdwendndarmontjrme:2023----:lecaecmrfruniaienqaiaiea
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publishDate	2023
allfields	10.1016/j.datak.2023.102154 doi (DE-627)ELV009687416 (ELSEVIER)S0169-023X(23)00014-9 DE-627 ger DE-627 rda eng 004 VZ 54.64 bkl Sawadogo, Pegdwendé N. verfasserin aut DLBench+: A benchmark for quantitative and qualitative data lake assessment 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly shared criteria for comparing data lake systems. Thus, we introduce in this paper 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 CSV 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. Beyond a purely quantitative assessment, we also propose a methodology to qualitatively evaluate data lake systems through the assessment of user experience. As a proof of concept, we use DLBench+ to evaluate an open source data lake system we developed. Data lakes Benchmarking Textual documents Tabular data Quality and metrics Darmont, Jérôme verfasserin aut Enthalten in Data & knowledge engineering Amsterdam [u.a.] : Elsevier, 1985 145 Online-Ressource (DE-627)265783518 (DE-600)1466273-5 (DE-576)074890816 nnns volume:145 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 Datenbanken VZ AR 145
spelling	10.1016/j.datak.2023.102154 doi (DE-627)ELV009687416 (ELSEVIER)S0169-023X(23)00014-9 DE-627 ger DE-627 rda eng 004 VZ 54.64 bkl Sawadogo, Pegdwendé N. verfasserin aut DLBench+: A benchmark for quantitative and qualitative data lake assessment 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly shared criteria for comparing data lake systems. Thus, we introduce in this paper 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 CSV 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. Beyond a purely quantitative assessment, we also propose a methodology to qualitatively evaluate data lake systems through the assessment of user experience. As a proof of concept, we use DLBench+ to evaluate an open source data lake system we developed. Data lakes Benchmarking Textual documents Tabular data Quality and metrics Darmont, Jérôme verfasserin aut Enthalten in Data & knowledge engineering Amsterdam [u.a.] : Elsevier, 1985 145 Online-Ressource (DE-627)265783518 (DE-600)1466273-5 (DE-576)074890816 nnns volume:145 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 Datenbanken VZ AR 145
allfields_unstemmed	10.1016/j.datak.2023.102154 doi (DE-627)ELV009687416 (ELSEVIER)S0169-023X(23)00014-9 DE-627 ger DE-627 rda eng 004 VZ 54.64 bkl Sawadogo, Pegdwendé N. verfasserin aut DLBench+: A benchmark for quantitative and qualitative data lake assessment 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly shared criteria for comparing data lake systems. Thus, we introduce in this paper 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 CSV 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. Beyond a purely quantitative assessment, we also propose a methodology to qualitatively evaluate data lake systems through the assessment of user experience. As a proof of concept, we use DLBench+ to evaluate an open source data lake system we developed. Data lakes Benchmarking Textual documents Tabular data Quality and metrics Darmont, Jérôme verfasserin aut Enthalten in Data & knowledge engineering Amsterdam [u.a.] : Elsevier, 1985 145 Online-Ressource (DE-627)265783518 (DE-600)1466273-5 (DE-576)074890816 nnns volume:145 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 Datenbanken VZ AR 145
allfieldsGer	10.1016/j.datak.2023.102154 doi (DE-627)ELV009687416 (ELSEVIER)S0169-023X(23)00014-9 DE-627 ger DE-627 rda eng 004 VZ 54.64 bkl Sawadogo, Pegdwendé N. verfasserin aut DLBench+: A benchmark for quantitative and qualitative data lake assessment 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly shared criteria for comparing data lake systems. Thus, we introduce in this paper 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 CSV 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. Beyond a purely quantitative assessment, we also propose a methodology to qualitatively evaluate data lake systems through the assessment of user experience. As a proof of concept, we use DLBench+ to evaluate an open source data lake system we developed. Data lakes Benchmarking Textual documents Tabular data Quality and metrics Darmont, Jérôme verfasserin aut Enthalten in Data & knowledge engineering Amsterdam [u.a.] : Elsevier, 1985 145 Online-Ressource (DE-627)265783518 (DE-600)1466273-5 (DE-576)074890816 nnns volume:145 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 Datenbanken VZ AR 145
allfieldsSound	10.1016/j.datak.2023.102154 doi (DE-627)ELV009687416 (ELSEVIER)S0169-023X(23)00014-9 DE-627 ger DE-627 rda eng 004 VZ 54.64 bkl Sawadogo, Pegdwendé N. verfasserin aut DLBench+: A benchmark for quantitative and qualitative data lake assessment 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly shared criteria for comparing data lake systems. Thus, we introduce in this paper 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 CSV 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. Beyond a purely quantitative assessment, we also propose a methodology to qualitatively evaluate data lake systems through the assessment of user experience. As a proof of concept, we use DLBench+ to evaluate an open source data lake system we developed. Data lakes Benchmarking Textual documents Tabular data Quality and metrics Darmont, Jérôme verfasserin aut Enthalten in Data & knowledge engineering Amsterdam [u.a.] : Elsevier, 1985 145 Online-Ressource (DE-627)265783518 (DE-600)1466273-5 (DE-576)074890816 nnns volume:145 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-BBI GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 Datenbanken VZ AR 145
language	English
source	Enthalten in Data & knowledge engineering 145 volume:145
sourceStr	Enthalten in Data & knowledge engineering 145 volume:145
format_phy_str_mv	Article
bklname	Datenbanken
institution	findex.gbv.de
topic_facet	Data lakes Benchmarking Textual documents Tabular data Quality and metrics
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author	Sawadogo, Pegdwendé N.
spellingShingle	Sawadogo, Pegdwendé N. ddc 004 bkl 54.64 misc Data lakes misc Benchmarking misc Textual documents misc Tabular data misc Quality and metrics DLBench+: A benchmark for quantitative and qualitative data lake assessment
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topic_title	004 VZ 54.64 bkl DLBench+: A benchmark for quantitative and qualitative data lake assessment Data lakes Benchmarking Textual documents Tabular data Quality and metrics
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title	DLBench+: A benchmark for quantitative and qualitative data lake assessment
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title_full	DLBench+: A benchmark for quantitative and qualitative data lake assessment
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title_sort	dlbench+: a benchmark for quantitative and qualitative data lake assessment
title_auth	DLBench+: A benchmark for quantitative and qualitative data lake assessment
abstract	In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly shared criteria for comparing data lake systems. Thus, we introduce in this paper 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 CSV 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. Beyond a purely quantitative assessment, we also propose a methodology to qualitatively evaluate data lake systems through the assessment of user experience. As a proof of concept, we use DLBench+ to evaluate an open source data lake system we developed.
abstractGer	In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly shared criteria for comparing data lake systems. Thus, we introduce in this paper 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 CSV 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. Beyond a purely quantitative assessment, we also propose a methodology to qualitatively evaluate data lake systems through the assessment of user experience. As a proof of concept, we use DLBench+ to evaluate an open source data lake system we developed.
abstract_unstemmed	In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several approaches have been proposed to build data lake systems. However, such proposals are difficult to evaluate as there are no commonly shared criteria for comparing data lake systems. Thus, we introduce in this paper 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 CSV 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. Beyond a purely quantitative assessment, we also propose a methodology to qualitatively evaluate data lake systems through the assessment of user experience. As a proof of concept, we use DLBench+ to evaluate an open source data lake system we developed.
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title_short	DLBench+: A benchmark for quantitative and qualitative data lake assessment
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