ODBMS.org Interview with Kx Data Engineer Jamie O’Mahony
8 Aug 2017
Kx engineer Jamie O'Mahony was recently interviewed by Roberto Zicari of ODBMS.org about design considerations in large-scale database applications. The questions discussed include: "What are the typical mistakes made on large scale data projects? In your opinion, how can they be avoided in practice?;" "How do you ensure data quality?" and "What are the typical mistakes made on large scale data projects? In your opinion, how can they be avoided in practice?"
Kx 1.1 billion taxi ride benchmark highlights advantages of kdb+ architecture
25 Jan 2017
Stellar performance in third-party benchmarks is a tradition at Kx, and now we can add a new benchmark to the list, the taxi ride benchmark developed by Mark Litwintschik. This latest benchmark queries a 1.1 billion New York City taxi ride dataset. Our results were over four orders of magnitude faster than any other CPU technology and comparable to GPU-based code.
Dave Thomas on Fast Big Data for Financial Oversight
23 Nov 2016
Dave Thomas, Chief Scientist at Kx Labs, gave an informative talk on processing Fast Big Data at the YOW! developers' conference in Sydney, Australia in September 2016. The video of his talk has just been posted, and it is a great way to get an overview of the advantages of a simplified technology stack with kdb+ for large-scale data analytics. In a wide-ranging discussion, Dave outlined the enormous challenges facing data scientists in processing the massive volumes of data required to detect financial fraud, identity theft and an increasing range of cyber threats.
Real-time Insights and Decision Making with Fast, Big Data
9 Nov 2016
Businesses are processing exponentially more data from clicks, swipes, micropayments, cyber packets, social feeds and meter readings today. The financial services industry has been doing this on a large scale for the past two decades. FIS has coped with steadily increasing data volumes by using a simple scalable data architecture composed of a real-time database (RDB) and a historical database (HDB)