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Data governance and data quality go hand in hand. Atlan has long supported native integrations with Anomalo, Monte Carlo, and Soda to help organizations monitor and improve data quality. Atlan is expanding this support to select data warehouses, starting with Snowflake.
Snowflake's system data metric functions (DMFs) enable defining and enforcing data quality rules at the warehouse level. Atlan's integration with Snowflake in turn empowers users to:
- Define and enforce data quality rules directly within Atlan.
- Monitor data quality insights in asset discovery, lineage, and data products.
- Enhance trust and reliability by making quality metrics visible and actionable.
Supported rules
Atlan supports a set of predefined data quality rules:
- Blank and null checks — blank count, blank percentage, null count, null percentage
- Freshness metrics — data freshness tracking
- Statistical insights — average value, minimum value, maximum value, standard deviation
- Uniqueness and duplicates — duplicate count, unique count
The results of these checks are then classified into core data quality dimensions:
- Accuracy — ensuring correctness and reliability of data
- Timeliness — validating data freshness and latency
- Validity — checking data against predefined formats and constraints
- Completeness — measuring missing or incomplete data