求翻译:large volumes of data是什么意思?待解决 悬赏分:1 - 离问题结束还有 large volumes of data问题补充:匿名 2013-05-23 12:21:38 正在翻译,请等待... 匿名 2013-05-23 12:23:18 大量的数据。 匿名 2013-05-23 12:24:58 大数据量 匿名 2013-05-23 12:26:38 大量的数据 匿名 ...
Embodiments provide a data persisting mechanism that allows for efficient, unobtrusive persisting of large volumes of data while optimizing the use of system resources by the persisting process. In an embodiment, the persisting process includes a self-tuning algorithm that constantly monitors persistence ...
Transportable modules can also be used for publishing data marts. A data mart is normally a portion of a larger data warehouse for single or departmental access. At times, creating a data mart amounts to copying what has been collected and processed in the data warehouse. A transportable modul...
When you run the vendor aging data storage process to export the vendor aging report, the results can go to an external system using our data management framework. This feature provides an efficient way to report on data when there are large volumes of it. See also Ve...
Using the Warehouse Builder designer, a Warehouse Builder you first create a Transportable Module, and specify the source database location and the target database location. Then you select the database objects to be included in the Transportable Module. The metadata of the selected objects are im...
In today’s data-centric organizations, especially those dealing with large volumes of data distributed across multiple cloud providers (often due to M&A activities), the challenge of leveraging all data assets is both critical and complex. Two pot...
However, the pipeline components add overhead when processing large volumes of data, which can become critical in real-world scenarios. This paper presents a gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers. In this model, the gears ...
The solution to the above problem is pretty straightforward. If working with a large amount of data is causing us an issue, let’s split the dataset into a couple of smaller chunks. Then extract and process each part separately. You can even run each th...
an application for a particular time range, need high computational power and can be extremely slow. Performing sampling on the raw data is an option for attribute discovery. However, such an approach would also mean that we would miss sparse or rare attributes within large volumes of data. ...
In the other, better test runs, you can see how increasing degrees of parallelism can deliver huge performance boosts to organizations that load and integrate very large volumes of data. Data Model The organization in this scenario uses a very si...