Publicação
Benchmark of market cloud data warehouse technologies
| Resumo: | Over the past two decades, the way computing resources are been developed, deployed, upgraded, and applied changed dramatically, with more and more software and hardware solutions being transferred to cloud technologies. Data Warehouses (DW), defined as a way of organizing corporate data in an integrated manner over (sequential) time periods, "structured & disposed" in order to generate a "single data source", were also affected by the evolution, thus giving rise to the concept of Cloud Data Warehouse (CDW). This technology allows users to be more technologically free, as they do not need to spend time investing in software and hardware, they only pay for the resources they used and the infrastructure itself has greater flexibility and scalability. However, selecting the most suitable platform or technology for a CDW can be a complex task due to the large number of factors that can influence the decision and due to the existing offer in the market. The objective of this paper is to describe the process of benchmarking a set of CDW platforms, with the goal of analyzing and exposing each one’s performance results. These platforms are Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse. The metrics to be measured are data loading and query running time, and alias running times. For this benchmark, the dataset used was Star Schema Benchmark (SSB), a dataset based on the well-known TPC Benchmark™ H (TPC-H). |
|---|---|
| Autores principais: | Oliveira e Sá, Jorge |
| Outros Autores: | Renata Gonçalves; Kaldeich, Claus |
| Assunto: | Data Warehouse Cloud Computing Cloud Data Warehouse Cloud Data Warehouse Technologies |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1866877899572772864 |
|---|---|
| author | Oliveira e Sá, Jorge |
| author2 | Renata Gonçalves Kaldeich, Claus |
| author2_role | author author |
| author_facet | Oliveira e Sá, Jorge Renata Gonçalves Kaldeich, Claus |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Oliveira e Sá, Jorge\"},{\"Person.name\":\"Renata Gonçalves\"},{\"Person.name\":\"Kaldeich, Claus\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Oliveira e Sá, Jorge Renata Gonçalves Kaldeich, Claus |
| datacite.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-03-14T20:42:17Z |
| datacite.date.embargoed.fl_str_mv | 2024-03-14T20:42:17Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Data Warehouse Cloud Computing Cloud Data Warehouse Cloud Data Warehouse Technologies |
| datacite.titles.title.fl_str_mv | Benchmark of market cloud data warehouse technologies |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | Oliveira e Sá, Jorge Renata Gonçalves Kaldeich, Claus |
| dc.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-03-14T20:42:17Z |
| dc.date.embargoed.fl_str_mv | 2024-03-14T20:42:17Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/89565 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.rights.rights.copyright.fl_str_mv | openAccess |
| dc.subject.none.fl_str_mv | Data Warehouse Cloud Computing Cloud Data Warehouse Cloud Data Warehouse Technologies |
| dc.title.fl_str_mv | Benchmark of market cloud data warehouse technologies |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | Over the past two decades, the way computing resources are been developed, deployed, upgraded, and applied changed dramatically, with more and more software and hardware solutions being transferred to cloud technologies. Data Warehouses (DW), defined as a way of organizing corporate data in an integrated manner over (sequential) time periods, "structured & disposed" in order to generate a "single data source", were also affected by the evolution, thus giving rise to the concept of Cloud Data Warehouse (CDW). This technology allows users to be more technologically free, as they do not need to spend time investing in software and hardware, they only pay for the resources they used and the infrastructure itself has greater flexibility and scalability. However, selecting the most suitable platform or technology for a CDW can be a complex task due to the large number of factors that can influence the decision and due to the existing offer in the market. The objective of this paper is to describe the process of benchmarking a set of CDW platforms, with the goal of analyzing and exposing each one’s performance results. These platforms are Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse. The metrics to be measured are data loading and query running time, and alias running times. For this benchmark, the dataset used was Star Schema Benchmark (SSB), a dataset based on the well-known TPC Benchmark™ H (TPC-H). |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://repositorium.uminho.pt/bitstreams/983f4eba-c233-43d2-952a-d5ae6e6a6989/download |
| id | rum_a4d43aeb9df75e2bc4b97c7c5ea49345 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/89565 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/89565 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Oliveira e Sá, Jorge Renata Gonçalves Kaldeich, Claus |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Elsevier |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engElsevierporOver the past two decades, the way computing resources are been developed, deployed, upgraded, and applied changed dramatically, with more and more software and hardware solutions being transferred to cloud technologies. Data Warehouses (DW), defined as a way of organizing corporate data in an integrated manner over (sequential) time periods, "structured & disposed" in order to generate a "single data source", were also affected by the evolution, thus giving rise to the concept of Cloud Data Warehouse (CDW). This technology allows users to be more technologically free, as they do not need to spend time investing in software and hardware, they only pay for the resources they used and the infrastructure itself has greater flexibility and scalability. However, selecting the most suitable platform or technology for a CDW can be a complex task due to the large number of factors that can influence the decision and due to the existing offer in the market. The objective of this paper is to describe the process of benchmarking a set of CDW platforms, with the goal of analyzing and exposing each one’s performance results. These platforms are Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse. The metrics to be measured are data loading and query running time, and alias running times. For this benchmark, the dataset used was Star Schema Benchmark (SSB), a dataset based on the well-known TPC Benchmark™ H (TPC-H).application/pdfporBenchmark of market cloud data warehouse technologiesOliveira e Sá, JorgeRenata GonçalvesKaldeich, ClausHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISSNIsPartOf1877-0509DOIIsPartOf10.1016/j.procs.2024.06.2892024-03-14T20:42:17Z20242024-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/89565http://purl.org/coar/access_right/c_abf2open accessData WarehouseCloud ComputingCloud Data WarehouseCloud Data Warehouse Technologies635896 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paper2024http://creativecommons.org/licenses/by-nc-nd/4.0/openAccesshttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorium.uminho.pt/bitstreams/983f4eba-c233-43d2-952a-d5ae6e6a6989/download |
| spellingShingle | Benchmark of market cloud data warehouse technologies Oliveira e Sá, Jorge Data Warehouse Cloud Computing Cloud Data Warehouse Cloud Data Warehouse Technologies |
| status | SINGLETON |
| subject.fl_str_mv | Data Warehouse Cloud Computing Cloud Data Warehouse Cloud Data Warehouse Technologies |
| title | Benchmark of market cloud data warehouse technologies |
| title_full | Benchmark of market cloud data warehouse technologies |
| title_fullStr | Benchmark of market cloud data warehouse technologies |
| title_full_unstemmed | Benchmark of market cloud data warehouse technologies |
| title_short | Benchmark of market cloud data warehouse technologies |
| title_sort | Benchmark of market cloud data warehouse technologies |
| topic | Data Warehouse Cloud Computing Cloud Data Warehouse Cloud Data Warehouse Technologies |
| topic_facet | Data Warehouse Cloud Computing Cloud Data Warehouse Cloud Data Warehouse Technologies |
| url | https://hdl.handle.net/1822/89565 |
| visible | 1 |