Real-Time Recommendation Systems with Streaming SQL

AI-powered data quality monitoring uses statistical analysis and anomaly detection on streaming data to catch quality issues — null spikes, schema drift, distribution changes, volume anomalies — as data flows through pipelines, not hours later in batch validation.

Data Quality Checks with Streaming SQL

-- Null rate monitoring
CREATE MATERIALIZED VIEW null_rates AS
SELECT source_name,
  COUNT(*) as total_5min,
  COUNT(*) FILTER (WHERE user_id IS NULL)::DECIMAL / COUNT(*) as null_rate_user_id,
  COUNT(*) FILTER (WHERE amount IS NULL)::DECIMAL / COUNT(*) as null_rate_amount
FROM pipeline_events WHERE ts > NOW()-INTERVAL '5 minutes'
GROUP BY source_name;

-- Volume anomaly detection
CREATE MATERIALIZED VIEW volume_check AS
SELECT source_name,
  COUNT(*) FILTER (WHERE ts > NOW()-INTERVAL '5 minutes') as current_5min,
  COUNT(*) FILTER (WHERE ts BETWEEN NOW()-INTERVAL '1 hour' AND NOW()-INTERVAL '55 minutes') as baseline_5min,
  CASE WHEN COUNT(*) FILTER (WHERE ts BETWEEN NOW()-INTERVAL '1 hour' AND NOW()-INTERVAL '55 minutes') > 0
    THEN COUNT(*) FILTER (WHERE ts > NOW()-INTERVAL '5 minutes')::DECIMAL /
         COUNT(*) FILTER (WHERE ts BETWEEN NOW()-INTERVAL '1 hour' AND NOW()-INTERVAL '55 minutes')
    ELSE 0 END as volume_ratio
FROM pipeline_events GROUP BY source_name;

-- Alert on quality issues
CREATE MATERIALIZED VIEW quality_alerts AS
SELECT * FROM null_rates WHERE null_rate_user_id > 0.05
UNION ALL
SELECT source_name, current_5min, null, null FROM volume_check WHERE volume_ratio < 0.5 OR volume_ratio > 2.0;

Frequently Asked Questions

What data quality issues can streaming SQL detect?

Null spikes, volume anomalies (sudden drops or spikes), distribution shifts (mean/stddev changes), duplicate rate increases, and schema violations. For semantic quality (incorrect values that are syntactically valid), ML-based approaches are needed.