Developers might additionally choose to leverage the embedded SQLite features for relational data storage.
开发者也可以另外选择内嵌的SQLite特性来存储关系型数据。
This differs greatly from relational databases, which rely heavily on relationships to normalize data storage.
这与十分依赖规范化数据存储关系的关系型数据库有很大不同。
Relational databases are, by their nature, more flexible than hierarchical data storage structures such as XML.
就其性质而言，关系数据库比层次数据存储结构(如xml)更灵活。
Others took the view that relational databases are harder to use for developers, and that alternative data formats and workloads are best served by different data storage mechanisms.
其他人认为，对开发者来说，关系型数据库更加难于使用，不同的数据存储机制能够为多变的数据格式和工作负载提供更好的支持。
S3 is primarily geared towards file storage so although it makes a good choice for cheaply hosting images and CSS stylesheets, it's a not a good choice for storing relational or structured data.
S3主要适用于文件存储，虽然它为图片和CSS样式表的低成本存储提供了一个不错的选择，但在存储关系型数据和结构化数据上它不是一个好的选择。
Since relational data and indexes are stored in separate storage objects within a table space, they have separate read and write counters.
由于关系数据和索引存储在表空间内不同的存储对象中，因此它们具有不同的读和写计数器。
Sharding is a particularly cost-effective decision for organizations tied to a relational infrastructure that cannot continue to upgrade hardware to meet the need for massively scalable data storage.
切分对于绑定于关系基础架构、无法继续升级硬件以满足大量可伸缩数据存储要求的组织来说是一个非常成本高效的决策。
These recommendations address the database schema, the choice between XML and relational storage, definition of indexes, and physical data organization with partitioning and clustering options.
这些建议涉及了数据库模式、XML与关系存储之间的选择、索引的定义以及带有分区和集群选项的物理数据组织。
In order to better understand the hybrid storage, look at a view of how the XML data logically appears to be stored inside a relational database.
为了更好地了解混合型存储，我们来看看XML数据的逻辑视图，体会XML数据如何看起来像是存储在关系数据库表中。
Now, DB2 has introduced an optimized data storage engine for XML data alongside the existing relational engine.
现在，DB 2引入了优化的数据存储引擎，以便与现有关系引擎一起支持XML数据。
If the change is to the relational storage of the data itself, however, then suddenly you're in a whole new world of complexity, so much so that an entire book has been written on the subject.
但是，如果更改发生在数据的关系存储本身上，那么就突然进入一个全新的、复杂的领域，这个专题复杂到足够写一本书。
Small XML documents that occupy less than 32kb of storage space can be inlined with any relational data present in the same row, and the entire row can be compressed.
占用的存储空间少于32kb的小型XML文档可以与关系数据一起内联存储在同一行中，可以对整行进行压缩。
Now, DB2 has introduced an optimized data storage engine for XML data alongside its existing relational engine.
现在，DB2在它已有的关系引擎的基础上，更是引入了一种用于XML数据优化的数据存储引擎。
In DB2 9, XML data is stored in the different storage location than the relational data.
在DB 29中，XML数据和关系数据存储在不同的位置。
DB2 Express-C is a leading edge hybrid data server capable of supporting both relational and pure XML storage.
DB 2Express - C是一种先进的混合型数据服务器，可以支持关系数据库和纯x ML存储。
SimpleDB is a simple data storage that lacks the sophistication of a full-fledged Relational Database Management System (RDBMS), while providing scalable key-value storage.
SimpleDB是一个简单的数据存储，它缺乏一个完全成熟的关系数据库管理系统(RDBMS)所拥有的完善的功能，但是提供了一种可伸缩的键值存储。
Although Pixie can use an RDBMS as a storage mechanism, it doesn't decompose your object into relational data of any sort.
尽管Pixie可使用RDBMS作为存储机制，但它没有将您的对象分解成任何一种关系数据。
Most Domino content consists of messaging and collaboration data unsuitable for relational storage and manipulation.
很多Domino内容包含不适合进行关系存储和操纵的消息和协作数据。
Koi USES a JDBC-accessed relational database as data storage.
Koi使用以jdbc访问的关系数据库作为数据存储。
Let's consider the following sample table to discuss the storage options for XML data. The table contains relational data and XML data.
考虑下面的示例表，讨论面向XML数据的存储选项。
Storage and retrieval from the data store are similar to using a relational database, but they're entirely proprietary to Google.
数据存储和获取操作与使用关系数据库时相似，但完全是 Google 专有的技术。
A true relational DBMS would allow for a fully normalized database at the logical level, while providing physical storage of data that is tuned for high performance.
一个真正的关系型数据库管理系统，既在逻辑层考虑到完全规范化的数据库，又提供数据的物理存储，这些数据为了高性能而被调整。
The paper discusses, analyzes, compares two kind of XML data storage patterns based on relational database document pattern and element pattern.
本文论述、分析和比较了基于关系型数据库的XML数据的文档存储模式和元素存储模式。
Relational database systems have been around for a few decades and have been hugely successful in solving data storage, serving, and processing problems over the years.
关系数据库系统已经存在了几十年，在解决数据存储、服务和处理问题上都取得了巨大的成功。
However, getting data from many large relational tables can require massive amounts of processing and storage.
然而，从很多个大关系表中取出数据可能需要海量的处理和存储。
The comparison study on the original and relational storage of GML spatial data.
GML空间数据的原生存储与关系存储的比较研究。
In the database layer, a data warehouse based on relational storage mechanism is used.
数据库层采用了基于关系型存储的数据仓库。
Traditional relational database and vast amounts of XML data storage and management are faced with enormous challenges.
传统的关系数据库和海量的XML数据存储和管理都面临着巨大的挑战。
What is more, the traditional database driver handles data by collection of rows which requires converting object data to relational one for persistent storage.
而且传统的数据库驱动技术，是以行集的方式而不是以对象的方式处理数据，需要将对象模型进行转换，实现对象数据到数据库的持久化存储。
Analyzing the architecture of object-relational access layer and OID generating strategies, 128 bit random number algorithms is not adapted to the increment of the data processing and data storage.
分析对象-关系访问层体系结构和OID生成策略，128位随机数算法生成的OID已不适应数据信息量不断增加和海量数据存储的需求。
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