2
0
In today’s cloud-first landscape, the integrity of data pipelines is crucial for operational success, regulatory compliance, and business decision-making.
This blog, “Snowflake Data Quality Framework: Validate, Monitor, and Trust Your Data,” will walk you through a Snowflake-native, dynamic, and extensible Data Quality (DQ) Framework — capable of automatically validating data pipelines, logging results, and monitoring anomalies in near real-time.
Imagine a Financial Services Company dealing with customer onboarding and loan processing across hundreds of clients daily.
The critical requirements are:
- Ensure that customer email addresses follow valid formats (REGEX check).
- Validate that phone numbers are exactly 10 digits (LENGTH check).
- Ensure mandatory fields like SSN, Customer ID, and Created Date are NOT NULL.
- Validate that loan order amounts fall within approved credit limits (VALUE RANGE check).
- Ensure Order IDs are always unique (DISTINCT check).
- Ensure Order Dates fall within the current financial year (DATE RANGE check).
Failing any of these checks can lead to financial exposure, compliance issues, or reputation loss.
Solution Overview: Snowflake-native Dynamic DQ Framework
Solution Overview: Snowflake-native Dynamic DQ Framework
Using only Snowflake, we developed a Dynamic, Config-Driven Data Quality Framework that:
- Stores rules centrally in a DQ_CONFIG table (no hardcoding required).
- Applies DQ rules dynamically across multiple tables and columns.
- Supports multiple rule types: NOT NULL, REGEX, VALUE RANGE, LENGTH CHECK, DISTINCT, DATE RANGE CHECK.
- Logs all results (pass/fail) into a DQ_LOG table with timestamps, error counts, and failure rates.
- No external orchestrators or tools required — pure Snowflake native.
- Easily extendable by adding a new row in DQ_CONFIG — no need to modify stored procedure.
Architecture Flow (Simple and Effective):
Architecture Flow (Simple and Effective):
-
DQ_CONFIG table defines the rules for each column.
-
Stored Procedure reads DQ_CONFIG dynamically.
-
Executes appropriate checks (NOT NULL, VALUE RANGE, etc.).
-
Captures TOTAL records vs FAILED records.
-
Logs everything into DQ_LOG table.
-
Based on DQ_LOG → trigger alerts (Optional Future Scope).
Rule Examples Implemented:
Technical Implementation:
Technical Implementation:
DQ Proc:
Validate the Output:
Conclusion:
Snowflake’s flexibility combined with this dynamic DQ framework empowers enterprises to proactively catch data issues before they cascade into production failures. With zero external scheduling, dynamic rule application, and automated logging,
this solution drastically simplifies Data Quality Monitoring at scale.
