Can I use this for very large tables?

Yes, the skill utilizes efficient aggregation queries and includes sampling techniques for representative rows, though performance will depend on your database engine's resources.

Does this skill modify my database data?

No, the skill only performs read-only SELECT queries and metadata lookups to generate profiles, statistics, and quality assessments.

What specific data quality dimensions are evaluated?

The skill evaluates five key dimensions: completeness (null checks), uniqueness (duplicate detection), freshness (timestamp analysis), validity (range/format checks), and logical consistency.

What databases does the Profiling Tables skill support?

It is designed to work with any database that supports standard SQL and provides an INFORMATION_SCHEMA, such as PostgreSQL, Snowflake, BigQuery, and Redshift.

Profiling Tables

Name: Profiling Tables
Author: astronomer

byastronomer

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데이터베이스 관리

Generates comprehensive data profiles and quality assessments for database tables to help engineers and analysts understand dataset structure and health.

소개

This skill automates the process of data discovery by performing a deep-dive analysis of SQL database tables. It extracts schema metadata, calculates type-specific column statistics, analyzes cardinality, and performs a multi-dimensional data quality assessment covering completeness, uniqueness, freshness, validity, and consistency. It is an essential tool for data engineers, analysts, and developers who need to quickly grasp the nuances of an unfamiliar dataset, identify potential data issues, or generate documentation without writing dozens of manual exploratory queries.

주요 기능

Type-specific column statistics for numeric, string, and date types
Automated schema and metadata extraction from INFORMATION_SCHEMA
Multi-dimensional data quality scoring and issue identification
77 GitHub stars
Representative data sampling and structured summary reporting
Cardinality and frequency analysis for categorical data distribution

사용 사례

Generating detailed data documentation and summaries for internal catalogs
Onboarding new team members to unfamiliar database schemas and datasets
Performing data quality audits prior to building ETL pipelines or ML models

소개

주요 기능

Type-specific column statistics for numeric, string, and date types
Automated schema and metadata extraction from INFORMATION_SCHEMA
Multi-dimensional data quality scoring and issue identification
77 GitHub stars
Representative data sampling and structured summary reporting
Cardinality and frequency analysis for categorical data distribution

사용 사례

Generating detailed data documentation and summaries for internal catalogs
Onboarding new team members to unfamiliar database schemas and datasets
Performing data quality audits prior to building ETL pipelines or ML models