Cloud computing is the basis for large data processing, big data is an extension of cloud computing.
云计算是大数据处理的基础，大数据是云计算的延伸。
Large data processing was relatively well solved by means of compiling and running of expression evaluation and code optimization.
通过表达式求值的编译执行和代码优化等手段，较好地解决了大数据量的处理问题。
Now as a top-level Apache project, Hadoop is supported and used by many companies such as IBM, Google, Yahoo!, and Facebook, and has become the industry de facto framework for large data processing.
Hadoop现在是顶级Apache项目，IBM、Google、Yahoo!和Facebook等许多公司都支持和使用 Hadoop，它已经成为大规模数据处理方面事实上的行业标准框架。
So you can download first few megabytes of a large file and start processing that data while you request the next chunk.
所以对于大型文件，你可以做到先下载起初的几兆数据，并在处理的同时下载后续的内容。
The amount of data transferred between processing sites may be large, so a corresponding network capability is required for adequate performance.
处理站点之间传输的数据量可能非常大，因此要实现足够的性能，需要有相应的网络能力。
Large, complex data sets escalate traditional data mining techniques to new levels of processing demand.
大型、复杂的数据集将传统的数据挖掘技术推向新层次的处理要求。
Large data sets with escalating complexity have pushed traditional data mining techniques to new levels of processing demand.
复杂性不断提升的大型数据集已经将传统的数据挖掘技术推向新层次的处理要求。
Most healthcare organizations have a large portfolio of systems with redundant processing and data.
绝大多数医疗组织都有一个庞大的包含冗余处理和数据的系统集合。
Processing of data as batch or real-time, including handling large volumes of large data sets.
批处理或实时的处理数据，包括处理大容量的大数据集。
To achieve performance improvements, they also need to operate on large scale data - they rely on large scale cluster processing.
为了提升性能，他们还需要操纵大范围数据：使用大范围集群处理。
The value-add is the ability to chunk up data and store them in a grid, with varying degrees of redundancy, which allows for processing of very large data sets entirely in memory.
另外还有一些新的功能，用于将数据区块化存储到网格中，使用不同级别的冗余，以及在内存中处理超大型的数据集。
We have a background job to load a large amount of IsoDeal data by chunks for downstream processing.
我们有一个后台任务，分段加载大量的IsoDeal数据用于后续处理。
A simple example, but one that illustrates the real power behind Hadoop and why it's becoming such a popular framework for processing large data sets with custom or proprietary algorithms.
虽然是一个简单的示例，但是通过自定义的和专有的算法说明了Hadoop背后真实的力量以及为什么Hadoop正在成为一种用于处理大型数据集的流行框架。
For example, an Oracle database, either Online Transaction Processing (OLTP) or a data Warehouse application, can benefit when using large pages.
例如Oracle数据库，无论是联机事务处理(OLTP)或者数据仓库应用程序，都可以从大型页面的使用中获益。
Transformation Extender can also support advanced transformation requirements, such as efficient processing of large data records or messages, and advanced data validation without complex coding.
Transformation Extender还支持高级的转换需求，例如大型数据记录或消息的高效处理，以及无需复杂编码的高级数据验证。
If you are processing a large XML data tree, keep in mind that SimpleXML loads the full XML document tree into memory before processing.
如果您处理的是一个大型的XML数据树，那么请务必注意SimpleXML在处理前是需要将整个XML文档加载到内存中的。
It has enabled the growth and processing of increasingly large data sets such as the web, the world's books, and video.
日益庞大的数据能够得以处理，这些数据包括web、世界的书籍和视频等。
As a conceptual framework for processing huge data sets, MapReduce is highly optimized for distributed problem-solving using a large number of computers.
作为处理大数据集的概念框架，MapReduce对于使用许多计算机来解决分布式问题而言是高度优化的。
It was developed within Google as a mechanism for processing large amounts of raw data, for example, crawled documents or web request logs.
Google对这个模型进行了实现，用来处理巨量的数据，例如网络爬虫得到的文档和web访问到的记录。
Parallel computing involves combining large amounts of data and spreading that data across each of the compute nodes for faster processing.
并行计算要涉及到组合大量数据并将那些数据分布到每个计算结点，以进行更快的处理。
Batch and Utilities: Batch and utility programs typically deal with large amounts of data, often processing the data in a sequential manner.
批处理和实用程序：批处理和实用程序通常处理大量的数据，并且常常以一种连续的方式处理数据。
CEP offers a high throughput and low latency solution for processing large amounts of monitoring data.
CEP为处理大量的监测数据提供了一个高吞吐量及低延迟的解决方案。
In the same way, the generated intermediate data is processed in parallel, making the approach ideal for processing very large amounts of data.
使用相同的方法，已生成的中间数据将被并行处理，这是处理大量数据的理想方法。
Simultaneously, the resources of many computers in a network are applied to a single problem that requires a lot of computer processing cycles or access to large amounts of data.
同时，网络中许多计算机的资源应用于需要许多计算机处理周期或访问大量数据的单一问题。
A large chunk of binary data takes up less space than its encoded representation, so MTOM can reduce transmission time (although it incurs some processing overhead).
大块二进制数据所占的空间比其编码的表示形式更小，因此MTOM可以减少传输时间(尽管它会引入某些处理开销)。
Hadoop is an Apache Software Foundation project that consists of a set of tools for storing and processing large amounts of unstructured data.
Hadoop是一个Apache软件基金会项目，包含一系列用于存储和处理大量非结构化数据的工具集。
The point of this exercise is to create a system that allows the MapReduce magic of distributed processing of large amounts of data to happen closer to the data itself.
这项工作的关键点在于创建一个系统。这个系统允许MapReduce算法在尽可能靠近数据本身的地方采用分布式处理大量数据。
Therefore, consider a combination of MapReduce-style parallel processing and load balancing when planning to process a large amount of data on your cloud system.
因此，在计划在云系统上处理大量数据时，考虑MapReduce样式并行处理和负载平衡的组合。
Google introduced the idea of MapReduce as a programming model for processing or generating large sets of data.
Google引用MapReduce的概念作为处理或生成大型数据集的编程模型。
Google introduced the idea of MapReduce as a programming model for processing or generating large sets of data.
Google引用MapReduce的概念作为处理或生成大型数据集的编程模型。
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