Direct Answer
Modernizing a market data pipeline involves replacing legacy “request-response” models with event-driven, streaming architectures. By utilizing in-memory processing and high-speed binary feeds, firms can ingest millions of messages per second and respond to market movements in real-time.
| Feature | Legacy Middleware | Modern Low-Latency Pipeline |
| Data Source | Aggregated API Polling | Low-latency streaming feed |
| Processing | Disk-based / Batching | In-Memory / Real-time |
| Connectivity | Remote Cloud Gateways | Co-located or low-latency feeds |
| Latency | Milliseconds to Seconds | Microseconds (in optimized setups) |
Frequently Asked Questions
- Is Kafka good for market data? Kafka can achieve low latency with careful tuning, but for the most time-sensitive tasks, lighter-weight alternatives or custom ring buffers are often preferred.
- Why move to in-memory processing? Reading and writing to a disk is significantly slower than RAM, making in-memory databases essential for real-time order books.
Who This Is For (and Who It’s Not)
Who This Is For
- Engineering leads redesigning legacy infrastructure for better performance.
- Teams scaling their systems to handle increased market volatility and volume.
Who This Is NOT For
- Firms with low-frequency data needs where latency is not a bottleneck.