Numascale and 1degreenorth Offer
NumaQ — a Scalable In-Memory Processing and Big Data Analytics Appliance
NumaQ represents a new generation of in-memory-based analytics appliances that scales to thousands of cores and terabytes of memory for data and memory-intensive workloads.
Numascale, in collaboration with 1degreenorth, offer an innovative approach to big data analytics by providing in-memory capabilities via NumaQ systems. NumaQ, developed by 1degreenorth, employs Numascale's scalable shared memory technology to offer a new generation of high performance in-memory analytics capabilities that provide the power of a cluster coupled with the simplicity of a desktop.
NumaQ is a state-of-the-art x86 shared memory system from 1degreenorth that is specifically designed for big data in-memory analytics. The system uses high quality servers, connected with Numascale's NumaConnect scalable SMP fabric and a tuned Linux operating system. 1degreenorth's engineers have integrated the popular open source R statistical programming environment from Revolution Analytics with Numascale’s NumaManager to provide a desktop-like experience in a commodity cluster system, with hundreds of cores and terabytes of RAM that is easy to manage and use.
Designed and specifically engineered for in-memory big data analytics, NumaQ accelerates processing by providing a set of optimized appliances. The in-memory analytics appliance is supported by the R2 analytics programming environment and the MonetDB column-store analytics database. In addition, an in-memory Apache Spark Appliance is provided by the R2 analytics programming environment, just as with Hadoop clusters.
The NumaQ standard system configurations scale from 128 cores with 1TB memory to 4,096 cores with 32TB of global shared memory in a single system image environment. The systems are based on Dell’s industry-standard server platform, Red Hat® Enterprise Linux®, and Numascale’s NumaConnect.
NumaQ was developed by 1degreenorth as a result of a close collaboration between the companies over several years.