DQ Guardian
Data Quality & Rules Engine
Scale your enterprise data without compromise.
Problem
Teams define data quality rules across multiple systems, treat profiling as an afterthought, and rely on manual reconciliation, leading to inconsistencies and errors. As a result, data teams spend up to 80% of their time firefighting quality issues instead of focusing on strategic initiatives.
Purpose
Features
Prebuilt Rules (100+)
Covers accuracy, completeness, consistency, uniqueness.
AI Rule Co-Pilot
Create rules via natural language or SQL.
Data Profiling
Stats, trends, anomaly detection.
DQ Lifecycle
Track issues end-to-end with root cause and trends.
Alerts
Threshold, anomaly, and SLA-based alerts across Slack, Teams, email, Jira, ServiceNow.
Scorecards
Unified quality scores with trends for business visibility.
Quarantine Access
Direct access to failed records in Databricks.
Domain-Aware Quality Governance
Align data quality with business domains like Finance, Supply Chain, Customer, and Operations
Assign clear domain ownership to datasets and pipelines
Apply domain-specific validation rules and thresholds
Track quality performance using domain-level scorecards
Route issues automatically to the responsible domain teams
Monitor and enforce domain-specific SLAs
Impact
How DQ Guardian Works?
Use Cases
The Challenge
During cloud migrations (on-prem to platforms like Databricks or Snowflake), organizations must verify data integrity across millions or billions of records with zero tolerance for data loss or corruption.
Solution:
With Eagle Eye IQ’s DQ Guardian, teams run automated pre-migration and post-migration validation checks that compare row counts, schema integrity, null distributions, and statistical data profiles across source and target systems.
Eagle Eye IQ enables 100% migration confidence by replacing weeks of manual spot-checking with automated, scalable validation across entire datasets.