Approach: Use message queues (Kafka), distributed caching (Redis), and microservices to decouple data ingestion from processing.

Considerations: Balance sub-100ms latency with reliability. Handle network partitions, data loss, and maintain consistency across exchanges.

Key Points: Use connection pooling, circuit breakers, and monitoring (Prometheus/Grafana) for 99.99% uptime across millions of daily events.

Approach: Isolate scope by checking logs and metrics, reproduce locally, then apply fixes.

Considerations: Prioritize accuracy over speed. Ensure data integrity and rollback procedures protect client trust and regulatory compliance.

Key Points: Document root cause, use distributed tracing to trace transactions, and add automated tests to prevent regression.

Approach: Use a rule engine with DSL (Drools/JSON) supporting dynamic updates without redeployment.

Considerations: Balance detection sensitivity with false positives. Engage compliance and risk teams to refine thresholds and ensure regulatory requirements.

Key Points: Enable real-time alerting, A/B testing of rule changes, and audit trails for flagged trades per compliance.