Data pipelines are the circulatory system of any modern analytics stack. When they fail silently, bad data flows downstream, decisions get made on faulty numbers, and trust in analytics erodes.
Sign 1 — Your dashboards lag by more than a day. If executives are looking at yesterday's data when business conditions change by the hour, your pipeline's batch cadence is misaligned with decision velocity. The fix is usually incremental loading and micro-batch or streaming architectures.
Sign 2 — NULL values keep appearing in unexpected places. A single upstream schema change can silently null out entire columns. Implement schema registry checks and alerting on null rate thresholds at ingestion time.
Sign 3 — Row counts diverge between source and destination. Discrepancies as small as 0.01% can indicate deduplication failures or join explosions. Add reconciliation assertions to your transformation layer.
Sign 4 — Pipeline run times keep growing. Unbounded table scans, missing partition pruning, and accumulating data skew will eventually cause your pipelines to time out. Partition aggressively and benchmark cardinality growth.
Sign 5 — You have no test coverage on transformations. Would you deploy application code with zero tests? The same discipline applies to SQL and Python transforms. dbt tests, Great Expectations, and Soda Core all lower the bar significantly.