The Critical Need for Real-Time Financial Data

In today's financial markets, milliseconds matter. With over $6.6 trillion changing hands daily in global equity markets alone, traders live and die by the speed of their data. High-frequency firms execute orders in microseconds, where even the slightest delay can mean missed opportunities or costly slippage.

Financial applications now operate at unprecedented scale—trading platforms push live prices to millions of users across thousands of assets, while portfolio managers track risk exposures in real time. Market data giants like Bloomberg and Reuters pump out live updates to countless subscribers, and algorithmic traders demand feeds with near-instant delivery. The explosion of mobile trading has only raised the stakes, forcing systems to maintain reliability across spotty networks and unpredictable connectivity.

Beyond Kafka: Building Real-Time Financial Feeds for the Modern Web

Apache Kafka® has become a cornerstone for enterprise data distribution, serving as a high-throughput pipeline for transactional and analytical data. However, at scale, this comes at a significant cost—large enterprises often spend millions of dollars annually on Kafka infrastructure. Reducing these operational expenses has emerged as a key priority for many organizations.

Beyond cost, financial data distribution presents unique challenges. Traffic patterns are highly volatile, requiring clusters to scale dynamically to meet fluctuating demand. Traditional Kafka’s partition-centric architecture complicates elastic scaling, as expanding capacity involves cumbersome partition reassignments and data migration. Additionally, Kafka alone cannot natively support WebSocket or HTTP-based delivery, forcing enterprises to deploy supplementary infrastructure layers. Mobile client support further exacerbates the complexity of data dissemination.

AutoMQ addresses these challenges with its diskless architecture, eliminating the cost and scalability limitations of traditional Kafka. By decoupling storage and compute, AutoMQ enables seamless horizontal scaling without partition relocation overhead. When integrated with Lightstreamer, it forms a powerful, elastic real-time data distribution platform—optimized for financial use cases—while reducing infrastructure costs and operational complexity.

The Solution: AutoMQ + Lightstreamer Architecture

Our solution addresses these challenges by combining two complementary technologies:

AutoMQ stores data directly in S3-compatible object storage, eliminating the need for expensive local disk arrays. Its elastic scaling capabilities allow compute and storage to be scaled independently based on workload demands, resulting in significant cost reductions compared to traditional disk-based Kafka deployments. Most importantly, AutoMQ maintains 100% API compatibility, ensuring existing Kafka applications work without modification.

Lightstreamer is a real-time streaming server that bridges the gap between Kafka and end-user applications. It efficiently serves millions of concurrent connections through massive fanout capabilities and adaptive streaming technology that automatically adjusts data flow based on network conditions. Supporting WebSocket, HTTP, and native mobile protocols, Lightstreamer can traverse firewalls and proxies while delivering data with low latency and high reliability to web browsers, mobile applications, and smart devices worldwide.

The overall architecture is illustrated in the following diagram, showcasing a comprehensive real-time stock trading data pipeline. Upstream, a producer continuously generates live stock trading price data and streams it to the AutoMQ cluster. Leveraging AutoMQ's 100% Apache Kafka compatibility, data ingestion occurs seamlessly without any protocol modifications or custom integrations. AutoMQ's innovative diskless architecture, built on object storage foundations, enables dynamic scaling capabilities that automatically adapt to fluctuating data volumes while providing virtually unlimited data retention capacity.

Downstream, the architecture employs the Lightstreamer Kafka Connector as an intelligent bridge, efficiently distributing real-time market data to diverse client applications through Lightstreamer's adaptive streaming platform. The demonstration features a responsive web application as the primary downstream consumer, though the architecture readily supports various client types including mobile applications, IoT devices, and edge computing systems. This end-to-end solution addresses the complete spectrum of challenges in financial data streaming, from high-throughput data production through reliable intermediate processing to intelligent last-mile delivery, creating a robust and scalable foundation for modern trading platforms and financial applications.

Launch Real-Time Stock Price Streaming

As introduced above, we have implemented a specific case of real-time stock price data sharing, and the relevant code can be viewed in the Lightstreamer's Kafka connector repository. You can quickly start up with docker compose using a script. It will automatically start the producer, connector, and lightstreamer related components.

Once deployed, the system provides several interfaces for monitoring and demonstration. The Stock Price Demo is available at http://localhost:8080/QuickStart for viewing real-time stock price updates. Cluster monitoring and topic management can be accessed through Kafka UI at http://localhost:12000, while S3 storage management is available via the MinIO Console at http://localhost:9001 using the credentials minioadmin/minioadmin.

We can see the continuously incoming data on the AutoMQ Topic, and at the same time, transaction data for stock market price can be seen on the web page.

Conclusion

The integration of AutoMQ and Lightstreamer provides a powerful solution for real-time financial data streaming that addresses the key challenges of modern trading platforms and financial applications. By combining AutoMQ's cloud-native Kafka distribution with Lightstreamer's intelligent streaming capabilities, organizations can build scalable, cost-effective systems that deliver real-time market data to millions of concurrent users.

This architecture pattern extends beyond financial services to any application requiring high-throughput data ingestion with massive fanout capabilities, making it a valuable solution for IoT platforms, gaming systems, and real-time analytics applications. The relevant code and examples are open-sourced in the Github repository, and you are welcome to pull and experience them.