各种数据中心的Azure资源 [英] Azure Resources in various data centers

问题描述

您好奇......我们正在使用Data Factory和数据库创建ETL管道，以便将数据从azure数据湖移动到azure数据仓库。在俄勒冈州有一个原始资源，onprem，在美国东部2上的数据库，然后在美国西部2的数据仓库是
，有什么后果？这不会导致数据横穿整个大陆来完成管道？仅仅在美国西部2上使用我们的数据库也不是更有效吗？ 

解决方案

您好，

Azure数据工厂可以访问其他Azure区域中的数据存储和计算服务，以使用计算服务在数据存储之间移动数据或处理数据。

Azure数据工厂本身不存储任何数据。它允许您创建数据驱动的工作流，以协调支持的数据存储之间的数据移动以及使用其他区域或本地环境中的计算服务处理数据。
它还允许您通过使用程序和UI机制来监视和管理工作流。

虽然数据工厂仅在某些地区可用，但是支持数据移动的服务Data Factory在全球多个地区均可提供。如果数据存储位于防火墙后面，则在本地环境中安装了
的自托管集成运行时会移动数据。

举个例子，我们假设您的计算环境（如Azure HDInsight群集和Azure机器学习）已在西欧地区运行。您可以在美国东部或东美2创建和使用Azure数据工厂实例，并将其用于
计划西欧计算环境中的作业。 Data Factory在计算环境中触发作业需要几毫秒的时间，但在计算环境中运行作业的时间不会改变。

希望这会有所帮助。

Hi was wondering... we are creating an ETL pipeline using Data Factory and databricks to move data from azure data lake to azure data warehouse. What are the ramifications of having an original source, onprem in Oregon, databricks on the Est US 2 and then our data warehouse on West US 2. Wouldn't this cause the data to transverse the continent to complete the pipeline? Is it not more efficient to just have our databricks on the West US 2 also? 

解决方案

Hello,

Azure data factory can access data stores and compute services in other Azure regions to move data between data stores or process data using compute services.

Azure Data Factory itself does not store any data. It lets you create data-driven workflows to orchestrate the movement of data between supported data stores and the processing of data using compute services in other regions or in an on-premises environment. It also allows you to monitor and manage workflows by using both programmatic and UI mechanisms.

Although Data Factory is available only in certain regions, the service that powers the data movement in Data Factory is available globally in several regions. If a data store is behind a firewall, then a Self-hosted Integration Runtime that's installed in your on-premises environment moves the data instead.

For an example, let's assume that your compute environments such as Azure HDInsight cluster and Azure Machine Learning are running out of the West Europe region. You can create and use an Azure Data Factory instance in East US or East US 2 and use it to schedule jobs on your compute environments in West Europe. It takes a few milliseconds for Data Factory to trigger the job on your compute environment, but the time for running the job on your computing environment does not change.

Hope this helps.

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