Data Quality & Validation

Catching bad data before it reaches downstream consumers — schema checks, freshness, and anomaly detection.

What it is

Data quality validation checks that data flowing through a pipeline meets expectations — schema, completeness, freshness, and statistical sanity — before downstream consumers (dashboards, models) trust it.

Key points

Schema validation: catches structural drift — a column renamed, a type changed, a field unexpectedly nullable — at the pipeline boundary instead of downstream.
Freshness checks: alert when expected data hasn't landed on schedule, which is often the first sign of an upstream failure.
Anomaly detection: statistical checks (row counts, value distributions) that catch silent corruption a schema check wouldn't — e.g. a join that silently drops most rows.
The core principle: fail loudly and early at the pipeline boundary, rather than letting bad data silently propagate into dashboards or models where it's much harder to trace back.