Distributed Systems in Finance

Traditional settlement takes days because banks cannot trust shared state. Canton Network addresses this with domain-based consensus and cryptographic privacy, enabling real-time settlement without sacrificing confidentiality.

Modern trading systems rely on message-oriented architectures to move market data, orders, and risk events between services with predictable latency. Delivery guarantees, broker architectures, and ordering constraints shape how these systems behave under load.

Financial systems process billions of time-series data points with sub-millisecond query requirements: constraints that break traditional databases. Scaling these workloads requires different architectural choices, from vertical scaling to intelligent sharding schemes and specialized layouts for market data.

How do trading systems observe themselves in real time? This article breaks down the telemetry architecture that keeps distributed systems visible under extreme latency pressure.

Modern trading systems rely on directed acyclic graphs (DAGs) that branch, merge, and transform real-time feeds into many parallel consumers: matching engines, risk checks, analytics, surveillance, and storage. These ingestion DAGs exist to isolate failure, control fan-out, and preserve latency and correctness under extreme market conditions.

Financial streaming systems must remain correct when reality intervenes. This article dissects crash mid-window recovery, checkpoint corruption, idempotent effects and deterministic replay when failures occur.