Automate data quality validation across pipeline stages with Great Expectations.
npx skills add https://github.com/mahg-es/araya --skill data-quality-mahg-es
Please help me install this Agent Skill. Skill: data-quality Source: https://github.com/mahg-es/araya/tree/main/skills/data-quality Command: npx skills add https://github.com/mahg-es/araya --skill data-quality-mahg-es
Bad data in leads to bad decisions. Implementing automated data quality checks ensures data entering analytics and models is complete, accurate, and timely, preventing downstream issues.
Run a data quality validation pass on the latest daily batch to generate a quality report.
High-intent search queries and answers about installing and using this skill.
Per-table column expectations enforce data quality by validating schema existence, non-null constraints, and uniqueness. These validation checks prevent downstream analytics issues by ensuring data completeness, accuracy, and timeliness across pipeline batches.
Quality gates enforce data pipeline validation by triggering configurable warn, fail, or alert actions based on validation results. They use per-table column expectations and drift handling to generate actionable reports and dashboards for anomaly detection.
Yes, you can implement custom validators alongside Great Expectations to enforce data quality gates. This approach supports per-table schema validation, anomaly detection, and configurable reporting for pipeline data completeness and accuracy.
Drift handling in data quality validation identifies and manages unexpected changes in data trends across pipeline stages. It works with anomaly detection and row-count checks to trigger quality gates and generate actionable alert reports.
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wshobsonImplement data quality validation frameworks using Great Expectations, dbt tests, and data contracts.
armanzeroeightImplement data quality checks and validation rules for data pipelines.
