IBM Spectrum Computing uses intelligent workload and policy-driven resource management to optimise resources across the data centre, on premises and in the cloud. Now scalable to over 160,000 cores, IBM provides you with the latest advances in software-defined computing technology to help you unleash the power of your distributed mission-critical high performance computing (HPC) infrastructure, for up to 150x faster, analytics and big data applications as well as new generation open source frameworks such as Hadoop and Spark.
To accelerate business insights from all their data, IT managers are adopting a new generation of technologies such as Apache Spark, noSQL databases and containers. But traditional IT server configurations, hypervisor environments and storage silos do not work well for these modern approaches because they are not optimised for distributed computing. IBM Spectrum Conductor with Spark meets this challenge with software defined infrastructure technology that is designed for distributed environments and enables organisations to deploy Apache Spark efficiently and effectively, supporting multiple versions and instances of Spark and a broad set of born-in-the-cloud application frameworks.
Red Bull Racing: the hard science behind Formula One
Formula One is an incredibly fast moving sport. The speed of innovation is extreme – as many as 30,000 engineering changes are made over the course of a season. Fast, highly accurate data-driven decision making, whether it’s in the factory or on the circuit, is critical for success. Join Red Bull Racing as they discuss the high-tech innovations needed to achieve success and how IBM solutions help – from the drawing board to race day! And learn how you can translate this to achieve 30-50% greater throughput for your research, engineering and design workloads.
IBM Spectrum Symphony is a highly scalable, high-throughput, low-latency workload and resource management software solution for compute- and data-intensive analytics applications. It can reallocate more than 1,000 compute engines per second to different workloads and, with sub-millisecond overhead per task, provides throughput of up to 17,000 tasks per second.
Learn how to optimise your big data analytics infrastructure for performance, flexibility and long-term value and get insights from IDC Research VP Carl Olofson on deploying Spark in a production environment.