Data warehousing refers to the process of collecting, storing, and managing large volumes of data from various sources in a centralized repository, known as a data warehouse. This system is designed to support business intelligence (BI) activities, such as data analysis, reporting, and decision-making.
Key characteristics of a data warehouse include:
Subject-Oriented: Focuses on specific areas of interest, like sales or finance.
Integrated: Combines data from multiple sources, ensuring consistency and uniformity.
Time-Variant: Tracks and stores historical data over time.
Non-Volatile: Once data is entered into the warehouse, it is not altered or deleted, allowing for consistent analysis.
Data warehouses allow businesses to analyze historical data, uncover trends, and make data-driven decisions. They are optimized for querying and reporting rather than transactional tasks.
数据仓储工业Data warehousing industry 能够拯救美国锈带吗？不如按着五行金木水火土看看pros and cons。
金 - 风险投资 venture capital ，锈带的风投不说不存在至多也只是IT发达地方的几十几百分子一。IT业到处都是嗷嗷待哺。
木 - 人才，数据仓储不是比特币挖矿，雇几个老农看看机房门就行，看看数据仓储排头兵，微软，亚马逊，古狗，甲骨文，IBM, Sap, 哪一家不是IT业老大，为何，数据仓储必须和云计算结合才能形成business model- platform as a service。锈带哪来这些IT人才。
水 - 运输logistics和环境，当然数据仓储不需要卡车司机和搬运工，数据仓储的logistics 是强大的互联网连通，锈带可否与人相比？环境就不用说了，公共设施社会治安等等没一样可以吸引人才和投资。
火 - 能源，数据仓储需要巨量的能源供应，若用化石燃料，会造成巨大的污染和温室气体，今后的数据仓储工业的发展必然和可再生能源的发展息息相关，锈带别说可再生能源工业，本来强大的化石燃料发电机组都已生锈成了废铁。
土 - 土地，没错，锈带有大量荒废的厂区和廉价工业地皮，仅此而已。那些厂房根本不能转为数据仓库，必须推倒重建，放水防火放窃，恒温恒湿恒源。与其在那里建仓，不如在内华达沙漠建更便宜，而且太阳能充足。
所以锈带振兴靠数据仓储不是上策，而是要靠制造业翻身和变革，老根底新生命。
是电老虎，用电量巨大，一般都放在边远地区。
至于database, data warehouse, 和data lake 的异同，在数据工程里另当别论。
你还data lake 了
瞎扯
那是rational 的数据从原来的table 发展起来的
你的data lake 是现在cloud 的概念
咱不再说data lake 这些浅显概念了吧，网上一搜一大把，再不济叫chatgpt写一篇也丰简由己，说一个具体例子，我团队的一项工作四年前在on pram SQL server上仅仅做data validation 要日夜不停花14天，如今在delta lake 上至多只要14分钟。
Data lake 的backend solution 我知道是怎么回事
Data warehousing refers to the process of collecting, storing, and managing large volumes of data from various sources in a centralized repository, known as a data warehouse. This system is designed to support business intelligence (BI) activities, such as data analysis, reporting, and decision-making.
Key characteristics of a data warehouse include:
Subject-Oriented: Focuses on specific areas of interest, like sales or finance.
Integrated: Combines data from multiple sources, ensuring consistency and uniformity.
Time-Variant: Tracks and stores historical data over time.
Non-Volatile: Once data is entered into the warehouse, it is not altered or deleted, allowing for consistent analysis.
Data warehouses allow businesses to analyze historical data, uncover trends, and make data-driven decisions. They are optimized for querying and reporting rather than transactional tasks.