data-observability

What problem does it solve?

This Skill helps you catch and prevent bad data—like nulls, duplicates, schema drift, and anomalous distributions—before it breaks downstream analytics or pipelines.

Core Features & Use Cases

• Infrastructure-level data validation: Establish Great Expectations suites, Soda checks, and custom SQL quality monitors to enforce quality rules.
• Schema drift & anomaly monitoring: Track source schema changes and detect issues such as volume spikes or distribution shifts with baseline profiling.
• Operational guardrails: Define thresholds and tiered alerting so warnings and critical failures are handled differently.

Quick Start

Use the data-observability skill to design a Great Expectations suite for a critical dataset, including the quality rules, baseline metrics, and integration points for alerting.