RISELab, the successor to the U.C.

Berkeley group that created Apache Spark, is hatching a project that could replace Spark—or at least displace it for key applications.Ray is a distributed framework designed for low-latency real-time processing, such as machine learning.

Created by two doctoral students at RISELab, Philipp Moritz and Robert Nishihara, it works with Python to run jobs either on a single machine or distributed across a cluster, using C++ for components that need speed.[ Jump into Microsoft’s drag-and-drop machine learning studio: Get started with Azure Machine Learning. | The InfoWorld review roundup: AWS, Microsoft, Databricks, Google, HPE, and IBM machine learning in the cloud. ]
The main aim for Ray, according to an article at Datanami, is to create a framework that can provide better speeds than Spark.
Spark was intended to be faster than what it replaced (mainly, MapReduce), but it still suffers from design decisions that make it difficult to write applications with “complex task dependencies” because of its internal synchronization mechanisms.To read this article in full or to leave a comment, please click here

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