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Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms.

Any good continuous integration solution provides the basics of triggering and reporting.The real value of continuous integration isn't in the server that executes it, but in the data that comes out of it.The ability to see trends in code metrics, unit and integration tests, and to download the freshest build products as the team works on the project far exceed any minor differences between two really great continuous integration servers.

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Apache Mahout is scalable to reasonably large data sets. It's core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigram. It can be run on single machine as well as clusters of machines. The core libraries are desgined in a way that provide good performance for non-distributed algorithms. It's an Apache product and desgined to for scalable to support your business case.