Data Quality & Validation • Data Profiling & StatisticsMedium⏱️ ~3 min
Production Integration and Workflow
Where Profiling Fits:
Data profiling is not a side tool you run occasionally. It sits in the critical path of production data pipelines, integrated at multiple stages from raw ingestion through to analytics and Machine Learning (ML) serving.
Real Time Monitoring During Ingestion:
As events stream into a log store or land in a data lake, inline profiling samples 0.1 to 1 percent of records. This lightweight check validates schema conformance, tracks null rates in critical fields like
user_id or transaction_amount, and monitors distinct counts per time window.
If a field that should be 99.9 percent non null suddenly drops to 80 percent, the system alerts within 5 to 10 minutes, not hours later when daily reports fail. For a system processing 10 billion events daily with p50 end to end latency of 5 to 10 minutes and p99 under 30 minutes, catching schema breaks early prevents cascading failures in downstream jobs.
Batch Profiling in the Warehouse:
Once data lands in a partitioned warehouse table, heavier daily profiling runs. This computes comprehensive statistics: row counts, distinct counts (approximate), top N values, histograms, min and max, quantiles, null percentages, and referential integrity checks across tables. For a company maintaining a 100 TB warehouse with 5 TB of new data daily, profiling new partitions completes in 10 to 20 minutes using 100 to 200 distributed workers.
Quality Gates and Automation:
Profiling results feed directly into orchestration logic. Pipelines can implement quality gates that block or mark downstream Extract Transform Load (ETL) jobs when statistics fall outside bounds. For example, if orders_per_day drops more than 20 percent from a 7 day moving average, the pipeline pauses and alerts on call engineers before corrupting dashboards or triggering incorrect ML retraining.
⚠️ Common Pitfall: Adding profiling without capacity planning can saturate compute clusters. Heavyweight profiling of a 50 TB partition during peak hours can slow ETL jobs by increasing read Input/Output Operations Per Second (IOPS), pushing end to end latency beyond Service Level Objectives (SLOs).
Query Optimization and ML Drift Detection:
At companies like Netflix and Meta, profiling statistics directly improve system performance. Query optimizers read table and column cardinality to choose efficient join orders, potentially changing query latency from minutes to seconds. In ML feature stores, profiling compares training and serving distributions: if Kullback Leibler (KL) divergence exceeds a threshold, it indicates data drift that could degrade model accuracy.
Metadata Catalog and Observability:
All profiling results live in a metadata catalog, queryable by data consumers deciding whether a table is stable enough for a new dashboard or feature. Metrics export to monitoring dashboards, creating time series for completeness, validity, and consistency that teams track just like application latency or error rates.💡 Key Takeaways
✓Profiling integrates at multiple stages: lightweight sampling during ingestion for 5 to 10 minute alerts, comprehensive batch profiling on warehouse partitions in 10 to 20 minutes
✓Quality gates block downstream ETL jobs when statistics violate bounds, preventing cascade failures before they corrupt dashboards or ML models
✓Query optimizers use cardinality and distribution statistics to choose join orders, changing query latency from minutes to seconds
✓Capacity planning is critical: profiling 50 TB during peak hours can saturate clusters and push ETL latency beyond SLOs without proper isolation
📌 Examples
1A null rate spike from 0.1% to 80% in <code>user_id</code> field triggers alert within 10 minutes during ingestion, caught by sampling 0.1% of 10 billion daily events
2ML feature store detects training vs serving drift when KL divergence of <code>click_rate</code> distribution exceeds 0.05 threshold, preventing model degradation