Building a Real-Time Complex Event Processing Platform for Investment eCommerce
Defined and delivered a distributed Lambda Architecture integrating Spark and Cassandra to enable real-time content recommendations,
behavioural alerts, and analytics within a global securities trading platform.
behavioural alerts, and analytics within a global securities trading platform.
The Client
A top-tier global investment bank and financial services institution, recognised as one of the largest wealth and asset managers in the world. The organisation manages approximately US $7 trillion in assets under management, serving institutional, corporate, and private clients across all major financial markets.
With a workforce of 100,000+ professionals and operations spanning 50+ countries, the bank maintains strategic hubs in key global financial centres, including a major presence in the heart of the City of London. The institution is classified as a global systemically important bank (G-SIB), reflecting its scale, complexity, and critical role in international capital markets.
With a workforce of 100,000+ professionals and operations spanning 50+ countries, the bank maintains strategic hubs in key global financial centres, including a major presence in the heart of the City of London. The institution is classified as a global systemically important bank (G-SIB), reflecting its scale, complexity, and critical role in international capital markets.
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Assets under management
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Employees worldwide
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The Challenge
The bank’s global multi-asset digital platform unified research, analytics, pricing, and execution into a single interface used by clients, sales teams, and traders worldwide. While this environment generated significant volumes of behavioural, content, and market events, it lacked a dedicated capability to analyse and react to them in real time. Business stakeholders identified four priority needs:
- Deliver personalised content recommendations based on user search behaviour.
- Provide sales teams with contextual alerts derived from client activity.
- Detect barrier price knock-in/knock-out events and notify relevant users.
- Generate structured analytics on research readership and engagement.
Addressing these requirements required introducing a scalable Complex Event Processing (CEP) layer capable of correlating real-time and batch events across distributed systems, and integrating seamlessly with the existing global alerting framework.
- Deliver personalised content recommendations based on user search behaviour.
- Provide sales teams with contextual alerts derived from client activity.
- Detect barrier price knock-in/knock-out events and notify relevant users.
- Generate structured analytics on research readership and engagement.
Addressing these requirements required introducing a scalable Complex Event Processing (CEP) layer capable of correlating real-time and batch events across distributed systems, and integrating seamlessly with the existing global alerting framework.
Our Solution
Designed and implemented a distributed Lambda Architecture introducing Complex Event Processing (CEP) capabilities into the bank’s global securities platform, enabling real-time and batch event correlation at scale.
The solution combined high-performance, open-source technologies with enterprise integration components to deliver a production-grade streaming analytics layer fully aligned with the bank’s DevOps standards.
The solution combined high-performance, open-source technologies with enterprise integration components to deliver a production-grade streaming analytics layer fully aligned with the bank’s DevOps standards.
Event-Driven Architecture
Defined and deployed a scalable CEP framework leveraging Spark Streaming and Cassandra to process high-frequency behavioural and market events. The architecture supported both real-time streams and batch ingestion and processing.
Personalised content engine
Implemented streaming pipelines to analyse user search activity and interaction patterns, dynamically generating tailored content alerts and contextual recommendations.
Market Event Detection
Integrated end-of-day pricing feeds to detect barrier price knock-in/knock-out conditions, automatically triggering notifications to traders and relevant sales users.
Research Usage Analytics
Built batch analytics processes to measure readership patterns, aggregating author- and publication-level engagement metrics to support editorial and commercial insights.
Enterprise Integration & APIs
Integrated the CEP layer with existing ESB, monitoring, and REST services, ensuring seamless interoperability with the bank’s alerting infrastructure and operational systems.
DevOps & Industrialisation
Embedded Spark within the bank’s Continuous Deployment framework, enabling iterative Agile delivery, production rollout across 7 global data centres, and knowledge transfer to internal platform teams.
The Value
The introduction of a production-grade Complex Event Processing capability transformed the platform into a real-time, behaviour-driven digital ecosystem. By correlating user activity, content interactions, and market events at scale, the organisation enhanced content relevance, improved responsiveness to trading conditions, and established a scalable foundation for future real-time analytics innovation.
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CEP Use Cases
Four real-time and batch use cases industrialised and deployed in production.
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Global Data Centers
Distributed CEP architecture deployed across seven international data centres.
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Project duration
End-to-end CEP platform delivered from design to production in under six months.
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Real-Time User Engagement
Enabled ingestion and processing of forward curves, pricing time series, fundamentals, and weather datasets into governed storage layers.
Faster Market Responsiveness
Enabled timely detection of key market events and automated notifications, improving reaction speed within trading and sales workflows.
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Scalable Event-Driven Foundation
Established a production-grade real-time analytics layer, creating a reusable foundation for future streaming, personalisation, and advanced analytics initiatives.
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