基于Amazon红移的异构数据源的统一空间分析

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI:10.1145/3318464.3384704

Nemanja Borić, Hinnerk Gildhoff, M. Karavelas, I. Pandis, Ioanna Tsalouchidou

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引用次数: 6

摘要

企业公司使用空间数据进行决策优化，并获得有关其业务和服务位置的新见解。行业依赖于有效地组合来自不同来源的空间和业务数据，例如数据仓库、地理空间信息系统、事务系统和数据湖，在这些来源中可以以结构化或非结构化的形式找到空间数据。在这个演示中，我们展示了Amazon Redshift的空间功能，以及它与其他Amazon服务(如Amazon Aurora PostgreSQL和Amazon S3)的集成。我们专注于该功能的设计和功能，包括Redshift最先进的优化器中的扩展，以推动空间处理接近数据存储的位置。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Unified Spatial Analytics from Heterogeneous Sources with Amazon Redshift

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.

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Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

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