Can I use this skill for Python data processing?

Yes, it includes patterns for Great Expectations, which is the industry standard for validating Python DataFrames, CSV files, and other non-SQL data sources.

How does this skill help with dbt projects?

It provides ready-to-use templates for schema tests, relationship checks, and custom SQL assertions (like revenue validation) to ensure your dbt models meet business requirements.

What data quality dimensions are covered?

The skill focuses on five core dimensions: Completeness (missing values), Uniqueness (duplicates), Validity (range/regex), Consistency (cross-source matches), and Timeliness (freshness).

Does it support proactive data monitoring?

Yes, it offers guidance on tracking row count trends, detecting schema drift, and setting up freshness SLAs to alert you of issues before they propagate.

Data Quality Expert

Name: Data Quality Expert
Author: timequity

bytimequity

보안 및 테스팅

Ensures data integrity and reliability using dbt tests, Great Expectations, and advanced monitoring strategies.

소개

The Data Quality skill equips Claude with specialized knowledge to implement robust testing frameworks across data pipelines. It provides structured guidance on five key quality dimensions—completeness, uniqueness, validity, consistency, and timeliness—enabling developers to build high-trust data environments. Whether configuring dbt schema tests, writing custom SQL assertions, or implementing Great Expectations in Python, this skill helps identify and prevent data anomalies, schema drift, and pipeline failures before they impact downstream analytics or machine learning models.

주요 기능

Standardized dbt schema, relationship, and custom SQL testing patterns.
0 GitHub stars
Great Expectations integration for Python-based data validation.
Anomaly detection logic for row count and null percentage trends.
Strategies for monitoring schema drift and freshness SLAs.
Comprehensive data quality dimension mapping for systematic audits.

사용 사례

Setting up proactive monitoring for data freshness and reliability in production warehouses.
Automating data validation within dbt-managed analytics engineering workflows.
Implementing Python-based quality checks for CSVs and DataFrames using Great Expectations.

소개

주요 기능

Standardized dbt schema, relationship, and custom SQL testing patterns.
0 GitHub stars
Great Expectations integration for Python-based data validation.
Anomaly detection logic for row count and null percentage trends.
Strategies for monitoring schema drift and freshness SLAs.
Comprehensive data quality dimension mapping for systematic audits.

사용 사례

Setting up proactive monitoring for data freshness and reliability in production warehouses.
Automating data validation within dbt-managed analytics engineering workflows.
Implementing Python-based quality checks for CSVs and DataFrames using Great Expectations.