Real-Time Crypto Analytics with Streaming SQL

Cybersecurity requires real-time threat detection — analyzing network traffic, authentication logs, and system events as they happen. Stream processing enables Security Information and Event Management (SIEM) with sub-second detection latency.

Threat Detection Patterns

-- Brute force detection (failed logins)
CREATE MATERIALIZED VIEW brute_force AS
SELECT source_ip, target_user,
  COUNT(*) as failed_attempts_5min
FROM auth_logs WHERE status='failed' AND ts>NOW()-INTERVAL '5 minutes'
GROUP BY source_ip, target_user
HAVING COUNT(*) > 10;

-- Lateral movement detection
CREATE MATERIALIZED VIEW lateral_movement AS
SELECT source_user, COUNT(DISTINCT target_host) as unique_hosts_1h
FROM network_connections WHERE ts>NOW()-INTERVAL '1 hour'
GROUP BY source_user HAVING COUNT(DISTINCT target_host) > 20;

-- Data exfiltration alert
CREATE MATERIALIZED VIEW exfiltration AS
SELECT source_ip, SUM(bytes_out) as total_bytes_1h
FROM network_traffic WHERE ts>NOW()-INTERVAL '1 hour' AND destination_type='external'
GROUP BY source_ip HAVING SUM(bytes_out) > 1073741824; -- >1GB

Frequently Asked Questions

Can streaming SQL replace a SIEM?

For detection rules and alerting, streaming SQL handles the real-time analytics component of a SIEM. Full SIEM functionality also requires log storage, investigation tools, compliance reporting, and incident management — which require additional tools.