问题描述
当前，我使用的体系结构采用了一些数据源，因为其中的数据源托管在云中，因此这些数据源是在本地暂存的。无论如何，其他都在本地托管，因此我执行的ETL直接从源头获取。我看不出为其他来源创建舞台的意义。
Currently, the architecture I work with takes a few data sources out of which one is staged locally because it's hosted in the cloud. The others are hosted locally anyway, so the ETL I perform takes it directly from the source. I don't really see the point in creating a stage for the other sources.
1）将本地托管的源复制到本地阶段是否有明显的好处？
1) Is there a distinct benefit to duplicating the locally hosted source into a local stage?
2）将舞台托管在单独的计算机上还是与Warehouse托管在同一台计算机上是个更好的主意？
2) Is it a better idea to host the stage on a separate machine or the same one as the Warehouse?
3）如果我想减少ETL时间，那是什么？这样做的好方法？我正在考虑对数据进行分区，以便比存档数据更频繁地提取重要信息。这是一个好方法吗，我有什么选择？
3) If I'm trying to reduce my ETL time, what's a good way to do so? I was considering partitioning my data so that the important information is pulled more frequently than the "archived data". Is this a good approach, and what are my alternatives?
推荐答案
@omgitsdev我想澄清一些概念。
@omgitsdev There are a few concepts I would like to clarify.
您的文件可以托管在任何地方-本地或云中
文件被加载到一个临时表中，以加载到您的数据仓库中。这个过程称为暂存。
Your files can be hosted anywhere - locally or on cloudThe files are loaded into a temporary table to be loaded into your Data Warehouse. This process is called staging.
从概念上讲，您可以将暂存区放在任何地方；但是，为了减少连接性问题，我们在同一数据库中创建了一个单独的架构并进行了阶段化。
Conceptually you can have your staging area anywhere; however to reduce connectivity issues, we create a separate schema in the same database and stage them. This will ensure that your performance is not hampered by connectivity issues.
通常，您将事实表按保存日期的列进行分区；
you generally partition your fact table by the column which holds the date; this is easier and also the most recent partitions hold the latest data;
基于卷，您可以将其设为每月，每季度或每年的分区；在某些情况下，我们还会创建每日或每小时分区。
Based on the volume, you either make it a monthly, quarterly or yearly partition; there are situations where we also create daily or hourly partitions.
通过确保临时表与数据仓库表位于单独的磁盘中，还可以提高性能。
Your performance can also be accelerated by ensuring that the staging tables are in a separate disk from the data warehouse tables.
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