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Last updated : Nov 10, 2015
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
Nov 2015
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

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
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.
Jan 2015
Assess ? Worth exploring with the goal of understanding how it will affect your enterprise.

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
Assess ? Worth exploring with the goal of understanding how it will affect your enterprise.
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.
Published : Jul 08, 2014
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