About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

Low Latency – It’s All About Big Fast Data

Subscribe to our newsletter

When one hears the term ‘Big Data’ one is drawn to thinking about batch analysis of vast datasets, such as web logs, click streams and, apparently, phone records. Technologies like Hadoop and NoSQL are mentioned, already in the context of their lack of performance, and murky ROI. But in the financial markets, the world of big data is different: it’s real-time, it’s high velocity, and it packs enormous value. Which is why we’re continuing to focus on how low-latency technologies are converging with the big data world to move beyond simple – and increasingly obsolete – ‘fastest execution’ trading models.

Indeed, October 8 in London (and November 12 in New York City) will see our Low-Latency Summit focus on “Big Fast Data for Automated Trading” … and already we have defined a set of morning ‘big picture’ plenary panels to explore different facets of the emerging convergence:

Low-Latency: No Longer a Strategy. So What Is?

Outright speed is no longer enough, even if one can afford it. Intelligent trading, aka smart trading, is the new focus. It means making better trading decisions through data-driven analytics, and executing with competitive latency, at an affordable cost. What are the best approaches to participating in the intelligent trading marketplace?

News and Social Media for Trading – Analytics over Latency?

Trading strategies driven at least in part by news and social media updates are being increasingly adopted, and generating investment returns that many are taking note of. What sources of information are suitable for what trading strategies, and what approaches are available for connecting to and processing this potentially lucrative Big Data world?

In-Memory in The Real World – Your Competitors are Already There

RAM is up to 100,000 times faster than disk, so its use to minimize I/O latency for storage is well understood. Recent hardware/software advances have made in-memory computing more usable for large amounts of data, making it an approach that is relevant to both low-latency and big data processing. How is in-memory playing out for trading applications?

And that’s just the morning. In the afternoon, specific technologies and approaches will be explored in a number of ‘drill down’ workshops.  More to come on those.

Check out the in-progress London event information here, and register in advance, as space is limited. If you work for a financial institution, then we might invite you for free – email ron@a-teamgroup.com to see whether you qualify and get the free code.

Subscribe to our newsletter

Related content

WEBINAR

Upcoming Webinar: From 24/7 to Event-Driven: Engineering the Next-Generation Exchange Platform

Date: 28 April 2026 Time: 10:00am ET / 3:00pm London / 4:00pm CET Duration: 50 minutes What digital asset and prediction markets are teaching traditional exchanges about availability, agility and time-to-market. New market structures and regulatory changes are forcing exchange operators to rethink the foundations of their technology stacks. Digital asset exchanges, prediction markets and...

BLOG

When Margin Moves Upstream: How TT is Reworking Trading Decisions After the OpenGamma Deal

More than a month after completing its acquisition of OpenGamma, Trading Technologies is beginning to articulate how the deal is intended to change the way firms think about margin, capital efficiency, and trading decision-making. Rather than positioning margin as a downstream risk or treasury concern, TT is now framing capital efficiency as a front-office variable...

EVENT

RegTech Summit London

Now in its 9th year, the RegTech Summit in London will bring together the RegTech ecosystem to explore how the European capital markets financial industry can leverage technology to drive innovation, cut costs and support regulatory change.

GUIDE

The Reference Data Utility Handbook

The potential of a reference data utility model has been discussed for many years, and while early implementations failed to gain traction, the model has now come of age as financial institutions look for new data management models that can solve the challenges of operational cost reduction, improved data quality and regulatory compliance. The multi-tenanted...