Data Observability

Data observability — monitoring data health на всех этапах pipeline. Analog к application observability, но для data.

5 pillars

Freshness: последнее обновление within SLA. Volume: rows counts в expected range. Distribution: value statistics в norms. Schema: no unexpected changes. Lineage: dependencies clear. Все measurable, alertable.

Tools

Monte Carlo: enterprise, comprehensive. Bigeye: SQL-first. Datafold: focused на data diffs. Great Expectations: open-source, tests-focused. Soda: open-source + cloud. Own implementation: dbt + custom monitors. Costs $30K–$200K/год для managed.

iGaming context

Financial reporting depends on data accuracy. Missed data: regulator fines. Wrong data: bad business decisions. Fraud detection: silent data corruption может hide fraud. Compliance: audit trails с verification. Critical infrastructure.

Implementation

Layer 1: schema и volume tests в pipelines. Layer 2: distribution monitoring baseline. Layer 3: lineage tracking. Layer 4: root cause analysis tooling. Layer 5: automatic incident response. Gradual maturity model — start simple.