ETL job details and operational metadata.
ETL作业详细信息和操作元数据。
ETL of short-term history snapshot and configuration data.
短期历史快照和配置数据的etl。
The issue needs to be addressed with a scalable ETL architecture.
这个问题需要用一个可伸缩的ETL架构来解决。
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。
First, the ETL places a significant load on your production databases.
首先，ETL给生产数据库增加了明显的负担。
After all, the acronym ETL stands for Extraction, Transformation and Load.
毕竟，首字母缩写词etl代表提取、转换和加载。
Data propagation: ETL process to aggregate and enhance data for data marts.
数据传播：用于为数据集市聚集和增强数据的ETL过程。
At three levels, ETL processing and data quality throughout all the data processing.
在三个层次中，ETL处理及数据质量管理贯穿整个数据处理过程。
As you might expect, there are several ways to program an ETL job for this scenario.
正如您所预料的，有多种方式为本场景编写ETL任务。
Figure 25 shows a sample ETL job that illustrates the TDEE data extract and load features.
图25所示的示例ETL作业演示了TDEE数据提取和加载特性。
Figure 27 shows a sample ETL job that illustrates the TDMLOAD data export and load features.
图27所示的示例ETL作业演示了TDMLOAD数据导出和加载特性。
The study on the approach of ETL is one of the most important parts of data warehouse research.
在数据仓库的建设中，ETL流程的实现是最复杂的工作之一。
Once you understand the ETL process, you can apply the same process to other database environments.
一旦理解了ETL过程，你可以在其他的数据库环境中应用相同的过程。
Metadata management: business requirements, data models, ETL process designs, user manuals, and so on.
元数据管理：业务需求、数据模型、ETL过程设计、用户手册，等等。
In this section, a simple ETL job is designed to illustrate the design steps and the extract functions.
本节将设计一个简单的ETL任务，以演示设计步骤和提取功能。
Generally, ETL processes manually specify when to update MQT rather than allow this to happen automatically.
通常，ETL过程是手动地指定何时更新MQT，而不是让更新自动发生。
The subsequent use-case scenarios sections detail four different cases that highlight the benefits of T-ETL.
随后的用例场景小节详细描述能突出 T-ETL的优点的4 种不同用例。
The data staging module stores the original data and temporary data from all data sources for ETL processing.
数据分级模块存储所有数据源的初始数据和临时数据，以用于ETL处理。
Designing ETL jobs (or data flows) is an important aspect of integrating XML into data warehouse environments.
设计ETL任务(或数据流)是将XML集成到数据仓库环境的重要方面。
This section USES two ETL jobs to illustrate the steps to look up the Teradata data based on the input records.
本节将使用两个ETL作业演示根据输入记录查找teradata数据的步骤。
Unlike ETL and data replication, event publishing does not know where the data is going and how it will be used.
和ETL以及数据复制不同，事件发布并不清楚数据的去向以及如何使用数据。
This section USES a sample ETL job to illustrate the steps to extract data from the Teradata table named Orders.
本节将使用一个示例ETL作业来演示从名称为Orders的teradata表中提取数据的步骤。
A third method is to have the summary tables rebuilt after the ETL process using SQL sum and aggregate functions.
第三种方法是在ETL流程之后使用SQLSUM和汇总函数来重构汇总表。
While this example USES Immediate refresh, deferred refresh MQTs are generally more compatible with ETL activity.
虽然这个例子使用立即(Immediate)刷新，但推迟刷新的MQT通常更适合ETL活动。
They have also released Oozie, a workflow engine for Hadoop, which has become the de-facto ETL standard at Yahoo.
他们还发布了Oozie，一个Hadoop的工作流引擎，这已在雅虎成为事实上的ETL标准。
The T-ETL job shown in Figure 4 executed in just 127 seconds -- an overall elapsed time improvement of 38 percent.
而图4 中显示的 T-ETL 作业的执行时间只有 127秒 —— 总体执行时间缩短了 38% 之多。
The data repository module stores all integrated business data, it is the final target of data acquisition ETL process.
数据存储库存储所有的集成业务数据，它是数据获取ETL过程的最终目标。
This operational information will be very helpful to debug problems later, such as inconsistent data or ETL job failure.
这些操作信息对于调试问题非常有帮助，比如数据不一致或ETL作业失败。
Finally, a fourth DataStage ETL job (TO_MART) builds out a data mart (mart) that three Cognos BI reports are then using.
最后，第四个DataStageETL任务(TO_MART)构建了一个数据集市(MART)，供三个CognosBI报告使用。
In this case, ETL simply implies that I'm going to analyze some MongoDB data and create new documents based on that data.
在本例中，ETL仅表示我要分析一些MongoDB数据并基于该数据创建新文档。
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