Data Observability

Monitor data pipelines, detect anomalies, and ensure data reliability

9 min read

Data observability provides visibility into the health of your data systems. Like application observability for software, it helps you understand what's happening with your data, why issues occur, and how to resolve them quickly.

Observability Pillars

Freshness

Is the data up-to-date? When was it last updated?

Volume

Is the data complete? Are there unexpected changes in row counts?

Schema

Has the schema changed? Are there new or removed columns?

Distribution

Are values within expected ranges? Are there anomalies?

Key Metrics to Monitor

Data freshness SLA

Pipeline success rate

Schema change frequency

Null value percentage

Duplicate records

Data drift detection

Query performance

Storage utilization

Cost per pipeline

Observability Tools

Monte CarloBigeyeAcceldataDatabandGreat ExpectationsElementaryMetaplane