Does this skill modify my Dremio data?

No, this skill is designed for investigation and reporting only. It performs read-only queries and schema inspections to assess quality without altering the underlying data.

How does this skill detect data anomalies?

It performs statistical range checks using MIN, MAX, and AVG functions, combined with APPROX_COUNT_DISTINCT to identify unexpected cardinality or value distributions that fall outside normal bounds.

Does this skill require specific Dremio permissions?

Yes, the user must have the necessary privileges to run 'drs schema' and 'drs query' commands on the target Dremio Cloud datasets.

Can it check for data freshness?

Yes, the skill includes a dedicated freshness check step that queries the most recent timestamps in your data to ensure updates are occurring within expected intervals.

Dremio Data Quality Investigator

Name: Dremio Data Quality Investigator
Author: dremio

bydremio

•

Gestión de Bases de Datos

Performs automated data quality audits including null analysis, duplicate detection, and anomaly screening for Dremio Cloud datasets.

The Dremio Data Quality Investigator skill empowers Claude to proactively monitor and report on the integrity of Dremio Cloud data sources. By automating complex SQL-based checks for schema drift, nullability, data freshness, and statistical outliers, it helps data engineers and analysts identify pipeline failures or data corruption before they impact downstream applications. This skill provides a structured workflow that transitions from initial schema discovery and sampling to a comprehensive, actionable quality report, ensuring data reliability across your lakehouse.

Características Principales

01Schema drift verification and nullability assessment

025 GitHub stars

03Data freshness and update frequency monitoring

04Statistical outlier and numeric range validation

05Automated null and duplicate detection via optimized SQL queries

06Standardized data quality reporting for stakeholder review

Casos de Uso

01Troubleshooting ETL pipeline failures or identifying the source of stale data

02Auditing a new Dremio dataset for production readiness and constraint validation

03Identifying primary key violations and duplicate records in large-scale datasets

Características Principales

01Schema drift verification and nullability assessment

025 GitHub stars

03Data freshness and update frequency monitoring

04Statistical outlier and numeric range validation

05Automated null and duplicate detection via optimized SQL queries

06Standardized data quality reporting for stakeholder review

Casos de Uso

01Troubleshooting ETL pipeline failures or identifying the source of stale data

02Auditing a new Dremio dataset for production readiness and constraint validation

03Identifying primary key violations and duplicate records in large-scale datasets