Nemanja Borić, Hinnerk Gildhoff, M. Karavelas, I. Pandis, Ioanna Tsalouchidou
{"title":"Unified Spatial Analytics from Heterogeneous Sources with Amazon Redshift","authors":"Nemanja Borić, Hinnerk Gildhoff, M. Karavelas, I. Pandis, Ioanna Tsalouchidou","doi":"10.1145/3318464.3384704","DOIUrl":null,"url":null,"abstract":"Enterprise companies use spatial data for decision optimization and gain new insights regarding the locality of their business and services. Industries rely on efficiently combining spatial and business data from different sources, such as data warehouses, geospatial information systems, transactional systems, and data lakes, where spatial data can be found in structured or unstructured form. In this demonstration we present the spatial functionality of Amazon Redshift and its integration with other Amazon services, such as Amazon Aurora PostgreSQL and Amazon S3. We focus on the design and functionality of the feature, including the extensions in Redshift's state-of-the-art optimizer to push spatial processing close to where the data is stored.","PeriodicalId":436122,"journal":{"name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3318464.3384704","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Enterprise companies use spatial data for decision optimization and gain new insights regarding the locality of their business and services. Industries rely on efficiently combining spatial and business data from different sources, such as data warehouses, geospatial information systems, transactional systems, and data lakes, where spatial data can be found in structured or unstructured form. In this demonstration we present the spatial functionality of Amazon Redshift and its integration with other Amazon services, such as Amazon Aurora PostgreSQL and Amazon S3. We focus on the design and functionality of the feature, including the extensions in Redshift's state-of-the-art optimizer to push spatial processing close to where the data is stored.