See the video below on how in-memory technology from ScaleOut Software can boost MapReduce 40x for financial applications, such as real-time risk analytics.
Subscribe to our newsletter
A-Team Insight Blogs
Leveraging In-Memory for Real-Time MapReduce Analytics
Subscribe to our newsletter
Related content
WEBINAR
Recorded Webinar: Navigating the Build vs Buy Dilemma: Cloud Strategies for Accelerating Quantitative Research
For many quantitative trading firms and asset managers, building a self-provisioned historical market data environment remains one of the most time-consuming and resource-intensive steps in establishing a new research capability. Sourcing data, normalising symbologies, handling corporate actions and maintaining infrastructure can take months and absorb significant budget before a single model is tested. At the...
BLOG
BMLL Adds Kalshi Data as Prediction Markets Move Deeper into Institutional Workflows
BMLL, the market data and analytics provider, has added historical prediction market data from Kalshi to its global market data platform, giving quantitative and systematic investment firms a standardised dataset for incorporating event probabilities into macro research, backtesting and trading models. Under the partnership announced today, Kalshi’s historical order book will be normalised into the...
EVENT
TEST Event page 1
Now in its 15th year the TradingTech Summit London brings together the European trading technology capital markets industry and examines the latest changes and innovations in trading technology and explores how technology is being deployed to create an edge in sell side and buy side capital markets financial institutions.
GUIDE
AI in Capital Markets Handbook 2026
AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...