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更新于 : Nov 10, 2015
不在本期内容中
这一条目不在当前版本的技术雷达中。如果它出现在最近几期中,那么它很有可能仍然具有相关参考价值。如果这一条目出现在更早的雷达中,那么它很有可能已经不再具有相关性,我们的评估将不再适用于当下。很遗憾我们没有足够的带宽来持续评估以往的雷达内容。 了解更多
Nov 2015
试验 ? 值得一试。了解为何要构建这一能力是很重要的。企业应当在风险可控的前提下在项目中尝试应用此项技术。

Apache Spark has been steadily gaining ground as a fast and general engine for large-scale data processing. The engine is written in Scala and is well suited for applications that reuse a working set of data across multiple parallel operations. It’s designed to work as a standalone cluster or as part of Hadoop YARN cluster. It can access data from sources such as HDFS, Cassandra, S3 etc. Spark also offers many higher level operators in order to ease the development of data parallel applications. As a generic data processing platform it has enabled development of many higher level tools such as interactive SQL (Spark SQL), real time streaming (Spark Streaming), machine learning library (MLib), R-on-Spark etc.

May 2015
试验 ? 值得一试。了解为何要构建这一能力是很重要的。企业应当在风险可控的前提下在项目中尝试应用此项技术。
Jan 2015
评估 ? 在了解它将对你的企业产生什么影响的前提下值得探索

For iterative processing such as machine learning and interactive analysis, Hadoop map-reduce does not work very well because of its batch-oriented nature. Spark is a fast and general engine for large-scale data processing. It aims to extend map-reduce for iterative algorithms and interactive low latency data mining. It also ships with a machine learning library.

Jul 2014
评估 ? 在了解它将对你的企业产生什么影响的前提下值得探索
For iterative processing such as machine learning and interactive analysis, Hadoop map-reduce does not work very well because of its batch-oriented nature. Spark is a fast and general engine for large-scale data processing. It aims to extend map-reduce for iterative algorithms and interactive low latency data mining. It also ships with a machine learning library.
发布于 : Jul 08, 2014
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