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Weka Sets 6 Records On STAC-M3 With WekaFS Parallel File System On Amazon EC2

Benchmark results demonstrate Weka’s ability to help financial services institutions migrate tick-analytics workloads to the AWS cloud and outperform on-premises

Weka the fastest-growing data platform for artificial intelligence/machine learning , life sciences research, and high-performance computing (HPC), announced record-breaking performance of its Weka File System on Amazon Elastic Compute Cloud (Amazon EC2) according to the STAC-M3™ Benchmark. An independent audit, conducted by Securities Technology Analysis Center (STAC®), showed that the Weka solution broke 6 STAC-M3 records, confirming that the WekaFS POSIX-compliant file system on Amazon Web Services (AWS) is a capable and performant option for enterprises looking to enjoy the elasticity and agility of tick analytics in the cloud. Financial services use cases such as algorithmic trading, quantitative analytics, and back testing can benefit from these results for hybrid and cloud native workflows.

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The STAC-M3 benchmark suite is the industry standard for testing solutions that enable high-speed analytics on time-series data, such as tick-by-tick market data (aka “tick analytics” stacks). STAC-M3 specifications were developed by the STAC Benchmark Council, which consists of over 400 financial institutions and 50 vendor organizations. User firms include the largest global banks, brokerage houses, exchanges, hedge funds, proprietary trading shops, and other market participants.

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This testing was performed on Amazon EC2 Non-Volatile Memory Express (NVMe) instances using a kdb+ 4.0 database by KX Systems. Testing included the baseline STAC-M3 suite (Antuco) and the scaling suite (Kanaga).

Key result highlights for this solution, with 15 database server nodes and 40 storage nodes, include:

  • Outperformed all publicly disclosed results in 3 of the 5 throughput benchmarks in the STAC-M3 Kanaga suite (STAC-M3.β1.1T.{3,4,5}YRHIBID.BPS)
  • Outperformed all publicly disclosed results in 3 of 24 mean-response-time benchmarks in the STAC-M3 Kanaga suite
  • Versus a kdb+ 4.0 solution running on a 10-node cluster with 60TB of persistent memory (KDB200603), was faster in 16 of 24 Kanaga and 9 of 17 Antuco benchmarks
  • Versus a kdb+ 3.6 solution on a parallel file system with 15 database servers accessing all-flash storage appliances (KDB200915), was faster in 20 of 24 Kanaga benchmarks and 4 of 17 Antuco benchmarks
  • Versus a kdb+ 3.6 solution involving 9 database servers accessing networked flash storage (KDB200914), was faster in 15 of 17 Antuco benchmarks

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