Kx Qualifies WekaIO to Run with Its kdb+ Database in AWS EC2
SAN JOSE, Calif., June 28, 2018 -- WekaIO, an innovation leader in high-performance, scalable file storage for data intensive applications, today announced that Kx, creator of the world’s fastest time series database, has qualified WekaIO Matrix storage solution for use with kdb+ in the Amazon Web Services (AWS) Elastic Compute Cloud (EC2) for machine learning applications.
This qualification of Matrix comes after Kx conducted its own rigorous benchmark testing of storage solutions available in AWS EC2 as part of a process for determining their suitability for Kx customers looking to migrate their historical kdb+ database (HDB) to the cloud.
“Kdb+ customers considering a migration of their historical market data into a cloud service provider can expect good data access performance and great service metrics from the solution,” said Glenn Wright, Systems Architect at Kx. “Matrix offers kdb+ a combination of good read performance and metadata operational latency, being one or two orders of magnitude better than EFS, Storage Gateway, and all open-source products we tested. Matrix exhibited block-like low operational latencies for some metadata functions and good aggregate throughputs for the small random reads with kdb+. WekaIO Matrix will also be of interest to kdb+ customers wanting to store some of their historical data online but using Amazon S3. Matrix achieves this by presenting kdb+ with a normal file-system access point, internally managing the tiering and access rules for any data moved to the lower-cost S3 solution.”
The cloud is the cornerstone of digital transformation and thus the number of companies migrating workloads to the cloud continues to grow rapidly. Databases have however been disadvantaged in this race to the cloud by a lack of storage solutions that can effectively deliver low latency data access similar to what customers have come to expect from their on-premises architectures. WekaIO Matrix is a fully distributed and parallel file system with an optimized network stack that solves these latency issues and delivers the highest performance shared storage solution available in the AWS Cloud. The powerful combination of elastic compute resources, Matrix, and kdb+ software results in a comprehensive solution that can handle the most demanding data and metadata intensive operations common in financial trading and market data analysis.
“I am pleased with the progress we are making with Kx and this qualification as a suitable storage solution for kdb+ in AWS EC2 is a testament to our deep knowledge of architecting platforms to support HPC in the cloud. When the enterprise needs immediate access to real-time and historical data to support their algorithmic trading workloads, they will find that the speed and performance of the kdb+ database in concert with Matrix is pure magic,” said Liran Zvibel, Co-founder and CEO at WekaIO. “What should also be noteworthy to kdb+ users who are considering moving their workload to AWS is the versatility of WekaIO Matrix, which, in addition to public cloud, also supports private cloud, containerized, virtualized and bare metal environments.”
For more information on Kx’s benchmark testing of WekaIO Matrix and other storage systems, read the Kx blog post.
WekaIO leapfrogs legacy storage infrastructures and future-proofs datacenters by delivering the world’s fastest parallel file system with the most flexible deployment options—on-premises, cloud, or cloud bursting. WekaIO Matrix software is ideally suited for latency-sensitive business applications at scale such as AI, machine learning, life sciences research, genomics, Big Data analytics, and any data-intensive technical workload.
Kx is a suite of enterprise-level products centered around kdb+, the world’s fastest time series database. Kdb+ is optimized for ingesting, analyzing, and storing massive amounts of structured data. The combination of the columnar design of kdb+ and its in-memory capabilities means it offers greater speed and efficiency than typical relational databases. Its native support for time-series operations vastly improves both the speed and performance of queries, aggregation, and analysis of structured data.