Considering the data growth in the last decade, OLTP servers are also handling a massive amount of data that demands an optimal tool for performing extract, transform, and load (ETL) operations.
鉴于十年以来的数据增长，OLTP服务器现在也在处理大规模数据，这就需要一个优化工具来执行提取、转换和加载(etl)操作。
Once this portion is complete and tested, the article guides you through enhancing the job to extract, transform, and load XML data into the DWADMIN.HOLDINGS table.
在这个部分完成并经过测试之后，本文将带领您改进该作业，以提取和转换XML数据，然后将其装载到DWADMIN . holdings表。
Structured source data collected by operational systems—on patients, medical claims, and providers—was fed through extract, transform, load (ETL) processes into the data warehouse.
业务系统收集的结构化源数据 —关于患者、医疗索赔和供应商 —通过提取、转换、加载（ETL）流程输入数据仓库。
Data is loaded through standard extract, transform, and load (ETL) connectors, and is then available for queries from business intelligence and analytics applications.
数据通过标准的提取、转换和加载(etl)连接器加载，然后可供商业智能和分析应用程序进行查询。
The MDI technologies also include a set of enterprise application integration (EAI), enterprise information integration (EII), and extract, transform, and load (ETL) technologies.
MDI技术还包括企业应用程序集成(EAI)、企业信息集成(EII)以及提取、转换和装载(etl)技术。
You may also want to consider using DPF if you are doing extract, transform, and load operations (ETL) and your batch window is small.
如果要处理提取、转换和载入操作(etl)，而批处理窗口又比较小，那么可以考虑使用DPF。
ETL services support the initial and incremental extract, transform, and load of data from one or more source systems to meet the needs of one or more targets, such as a data Warehouse and MDM system.
ETL服务支持从一个或多个源系统提取、转换和装载数据，从而满足一个或多个目标系统(比如数据仓库或MDM系统)的需要。
Extract, Transform, and Load (ETL) technologies can be used here.
可以在此处使用提取、转换和加载(Extract, Transform, and Load,ETL)技术。
The scenario described in this article uses operational data stored in DB2 pureXML as input to the extract, transform, and load (ETL) job that you'll design to populate a DB2-based data warehouse.
本文描述的场景使用DB2pureXML中存储的操作型数据作为提取、转换和装载（ETL）任务的输入， 这些任务用于填充一个基于DB2 的数据仓库。
Microsoft and Unisys announced a record for loading data into a relational database using an Extract, Transform and load (ETL) tool.
微软与Unisys公司宣布了一项纪录，它是关于关系数据库的数据提取，转换和加载(etl的)工具。
This mapping is used by the XML ODBC driver to provide data in relational form to the extract, transform, and load framework of IBM Rational Insight (Rational Insight).
XMLODBC驱动器使用这种映射，来提供关系格式的数据，以提取，转变，并载入IBMRationalInsight（Rational Insight）的框架。
ETL is often used to extract data from the source system, transform data into a compatible format with the target system, and then load into a target system, such as a data warehouse or data mart.
etl常用于从源系统中提取数据，将数据转换为与目标系统相兼容的格式，然后将其装载到目标系统中，例如数据仓库或者数据市场。
Another way is to have the extract, transform, and load (ETL) process update the summary table as it loads the fact table.
另一种方法是使提取、转换和加载(etl)流程在加载事实表时更新汇总表。
The tried-and-true technique of extract, transform, and load, or ETL, is one way to manage this.
行之有效的提取、转换和加载或etl技术是管理这个的一种方式。
However, prior knowledge of data warehousing, ETL (extract-transform-load) technology, and XML will help when reading this article series.
不过，具备数据仓库、ETL(提取-转换-装载)技术和XML知识对理解本系列文章有帮助。
The common requirement is to fetch Notes data into DataStage, perform an extract, transform, and load (ETL), and then pass this data to other Information Server modules for more ETL processing.
常见的需求是将Notes数据提取到DataStage中、执行解压、转换和加载(etl)，然后将此数据传递给其他InformationServer模块以便于完成更多etl处理。
Extract-transform-load (ETL) is one of the oldest technologies for data integration and is closely allied with data warehousing and business intelligence.
提取-转换-加载(Extract- transform - load,ETL)是用于数据集成的最古老的技术之一，且和数据储存和业务智能紧密结合。
One common way to do this is to build a data warehouse and extract, transform and load data from these diverse applications to deliver a common view of data from across these multiple applications.
为此，一个常见的做法就是，建立一个数据仓库，然后抽取、转换和加载来自这些不同应用软件的数据，为来自这多个应用软件的数据提供统一视图。
One common way to do this is to build a data warehouse and extract, transform and load data from these diverse applications to deliver a common view of data from across these multiple applications.
为此，一个常见的做法就是，建立一个数据仓库，然后抽取、转换和加载来自这些不同应用软件的数据，为来自这多个应用软件的数据提供统一视图。
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