#15 mindful machines original series, big data batch processing (2) #118
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…rocessing (2).md 英文已排版
…rocessing (2).md some translation added
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ghost
assigned 
hanxiaomax
added
B4-Reviewing
hanxiaomax
approved these changes
Nov 7, 2018
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| - **[Apache Hadoop MapReduce](https://hortonworks.com/apache/mapreduce/)：** 大数据生态系统的基石，它提供了一种高效处理拍字节级别数据的方法 |
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| - **[Apache Hadoop MapReduce](https://hortonworks.com/apache/mapreduce/)：** 大数据生态系统的基石，它提供了一种高效处理拍字节级别数据的方法 | |
| - **[Apache Hadoop MapReduce](https://hortonworks.com/apache/mapreduce/)：** 大数据生态系统的基石，它提供了一种高效处理拍字节级(petabytes)别数据的方法 |
不是十分常见，保留原文，照顾不熟悉的读者
| The traditional SQL database may seem an odd choice however, in addition to simply scaling vertically, with [sharding](https://en.wikipedia.org/wiki/Shard_(database_architecture)) and read-replicas it can scale across multiple nodes. In the following points I’m focusing more on these databases as analytical data stores (relatively few large queries) rather than traditional databases (massive numbers of relatively small queries). | ||
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| 传统的 SQL | ||
| 数据库看上去可能不是个常规的选择。但是，除了简单的纵向扩展，它还可以通过[分片](https://en.wikipedia.org/wiki/Shard_(database_architecture))（sharding）和只读副本（read-replicas）进行跨节点扩展在后文中，我会更多的将这些数据库作为分析型数据库（相对少量的大型查询），而不是传统数据库（大量的小型查询）来分析。 |
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认领校对 |
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(提交翻译请使用下面的模板，其他类型pr请删除)
closes #15
1.译者信息
(转载时的署名信息，有需要请补充)
2.疑问区
(可在此区域列举有疑问的语句，提示校对人员重点关注)
This isn’t a complete list of available technologies but rather the highlight reel that, among other things, explicitly avoids enterprise solutions although does cover PaaS.
Performance compared to Spark is hard to get numbers for
Spark’s biggest code contributor and commercial backer (Databricks) markets how much faster it’s proprietary PaaS version is than the open source version which creates skewed incentives for them.
3.自查表
4.发布信息（译者无需填写）
发布链接：
@hanxiaomax