Telecom Network Monitoring with Streaming SQL

Telecommunications networks generate massive volumes of data — call detail records (CDRs), network performance metrics, subscriber activity, and equipment alerts. Streaming SQL processes this data in real time for network quality monitoring, anomaly detection, and subscriber analytics.

Network Monitoring Views

-- Cell tower performance
CREATE MATERIALIZED VIEW tower_health AS
SELECT tower_id, sector,
  AVG(signal_strength) as avg_signal,
  COUNT(*) FILTER (WHERE call_drop = true) as drops_5min,
  COUNT(*) as total_calls_5min,
  COUNT(*) FILTER (WHERE call_drop = true)::DECIMAL / NULLIF(COUNT(*), 0) as drop_rate
FROM cdr_events WHERE ts > NOW()-INTERVAL '5 minutes'
GROUP BY tower_id, sector;

-- Subscriber anomaly detection
CREATE MATERIALIZED VIEW subscriber_anomalies AS
SELECT subscriber_id,
  SUM(data_usage_mb) FILTER (WHERE ts > NOW()-INTERVAL '1 hour') as data_1h,
  COUNT(DISTINCT tower_id) FILTER (WHERE ts > NOW()-INTERVAL '1 hour') as towers_1h
FROM subscriber_events
GROUP BY subscriber_id
HAVING SUM(data_usage_mb) > 1000 OR COUNT(DISTINCT tower_id) > 20;

Frequently Asked Questions

Can streaming SQL handle telco-scale data?

Telecom networks generate millions of CDRs per second. RisingWave processes this with horizontal scaling, computing aggregations across compute nodes in parallel.

How does real-time monitoring improve network quality?

Real-time monitoring detects cell tower degradation, call drop spikes, and capacity issues as they happen — enabling proactive remediation before customers are affected.