Beeks Analytics is a suite of products that records and analyses latencies at network and application level, delivering exceptional, real-time insight into performance so that our clients can build, maintain, and deliver a trading environment that gives their customers the best trading experience.
Beeks Analytics provides unrivalled visibility that enables you to track and analyse the real-time performance of every single message traversing business critical processes. Identify outliers and bottlenecks, understand capacity issues, verify new system roll-out performance, and ultimately ensure delivery of a consistent and high-quality trading environment.
Some example use cases that are solved by Beeks Analytics:
Do I have performance problems with any of my trading counterparties?
Is the problem inside my infrastructure or outside my infrastructure?
I need an overview of the flows going across my network to assist with troubleshooting and capacity.
I need to maintain Service Level Agreements within my trading system for my customers.
I need to exactly reproduce a period of sub-par performance in my trading system to try some possible solutions.
Beeks Analytics architecture is engineered to deliver on the four critical pillars of modern observability infrastructure: Open Architecture, Open Scaling, Open Consumption, and Open Data. Our goals are:
To operate with an open architecture, which provides the benefit of multiple high volume integration points with your organisation’s own systems.
Allowing your organisation to fully own the data produced by the Analytics system.
Enables the organisation to run the Analytics software on their own hardware.
To allow open scaling, which allows the capacity of the system to handle load to scale up with commercially available server hardware improvements.
To be modular and licenced to support open consumption.
This means that, for example, if you only need the high performing VMX-Capture layer and don’t require the in-depth analytics that the VMX-Analysis layer provides, we’ll ensure the software is licensed and priced accordingly.
It also means that we’re transparent about the drivers of our pricing - core count required for the analysis, which we make clear in our transparent performance metrics.
To support open data by ensuring that you have full access to, and control over, all monitoring data generated by the platform.
Rather than locking data into proprietary interfaces, our architecture emphasises direct and flexible data accessibility.
Our Advanced Configurable Decoder™ (ACD) ensures an agile, lower-cost way to monitor internal messaging data on the wire.
The Kafka-based Core Data Feed provides robust scalability and fault tolerance even under high message rates to provide lossless, low latency data streams. Use of Kafka frees you from vendor lock-in, since Kafka connectors exist for virtually every modern data processing framework, making it straightforward to combine Beeks Analytics output with your broader enterprise data.
The role of the Core Data Feed
The Beeks Analytics Core Data Feed (CDF) provides a powerful foundation for AI development by delivering a consistent, high-volume stream of normalised trading data in real time. Because the CDF unifies multiple data streams into one central analytics pipeline, it removes the complexities that typically arise when data is scattered across numerous capture points. This significantly reduces integration overhead, making it easier to feed large, high-quality datasets into AI platforms. In short, the CDF’s consolidated approach unlocks new possibilities for machine learning projects, because machine learning exports do not have to juggle multiple streams or worry about synchronisation issues when training algorithms.
Moreover, CDF’s flexible configuration ensures that AI models receive only the necessary data, preserving compute resources and accelerating the training loop. Since developers can fine-tune which metrics or decoded messages get passed into Market Edge Intelligence or to the client’s own AI models, they can prevent the “noise” of unnecessary data. This efficiency not only bolsters model performance but also reduces the risk of processing bottlenecks or system failures. As a result, financial institutions can build and refine high-performance trading analytics faster, with fewer hurdles to real-world deployment.
For more about the Core Data Feed, see the Analytics Core Data Feed Guide.