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Rules and dimensions

TL;DR

Reference for available data quality rules and classification dimensions in Snowflake data quality.

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This document lists the data quality rules and classification dimensions available in Snowflake Data Quality Studio.

Predefined data quality rules​

During the private preview, Atlan provides a set of predefined data quality rules, including:

  • Blank & Null Checks

    • Blank count
    • Blank percentage
    • Null count
    • Null percentage

  • Volume Checks

    • Row count

  • Freshness Metrics

    • Data freshness tracking

  • Statistical Data Exploration

    • Average value
    • Minimum value
    • Maximum value
    • Standard deviation

  • Uniqueness & Duplicates

    • Duplicate count
    • Unique count

  • String Validations

    • Regex
    • String Length
    • Valid Values
    • Reference

  • Reconciliation Checks

    • Row Count Reconciliation
    • Average Reconciliation
    • Sum Reconciliation
    • Duplicate Count Reconciliation
    • Unique Count Reconciliation

Data quality dimensions​

To provide better context and insights, Atlan classifies results into key data quality dimensions:

  • ✅ Accuracy: Verifying correctness and reliability
  • ⏳ Timeliness: Validating data freshness and latency
  • 📏 Validity: Checking data formats and constraints
  • 📋 Completeness: Measuring missing or incomplete data
  • 🔗 Consistency: Maintaining data follows the same format and standards across datasets
  • 🔢 Uniqueness: Verifying data records are distinct and free from duplicates
  • 📊 Volume: Measuring data quantity and row counts

See also​