GARTNER RESEARCH · ENTERPRISE DATA QUALITY

$15M/year.
From pipelines that looked healthy.

Gartner estimates enterprises spend over $15M annually remediating data defects — most originating in production pipelines that passed every orchestration check. iceDQ closes the gap.

Find Out What Silent Defects Are Costing Your Team → See how it works ↓
$15MAnnual enterprise
remediation cost
87%Cost reduction
with iceDQ
90%Reduction in manual
validation effort
0Production defects
PLATFORM INTEGRATION

Built for Any Platform. No rearchitecting required.

iceDQ works across Databricks, Snowflake, Azure Synapse, SQL Server, and 100+ connectors — embedding controls inside your existing ETL without rearchitecting. iceDQ deploys as whitebox checks co-located with your ETL — not as a downstream observability layer.

01
Catch defects at origin
Whitebox checks inside the pipeline — not downstream in an observability layer
02
Stop — don’t just alert
Failure exit code halts the job before defects propagate downstream
03
Full-volume validation
Source-to-target reconciliation — every row, every column, not statistical sampling
04
100% attribute coverage
Checksum and count checks at every layer transition
05
Expression engine for complex logic
Validates intricate transformation rules natively — no SQL overhead
06
CI/CD regression packs
Reusable test suites that travel from dev to staging to production

"Enterprises using iceDQ cut remediation costs by 87%, reduced manual validation effort by 90%, and achieved zero production defects on mission-critical migrations."

iceDQ · Proven in Production · icedq.com
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Find Out What Silent Defects Are Costing Your Team

We tailor every demo to your stack and use case. No generic slides — live exploration of how iceDQ maps to your architecture.

Platform-specific demo We show iceDQ inside your platform — your layer structure, your pipeline patterns.
No rearchitecting Whitebox checks deploy alongside your existing ETL - CI/CD ready, no pipeline rewrites needed.
ROI walkthrough We model the cost reduction based on your pipeline volume and current validation approach.

Book a Demo

Talk to Adnan · iceDQ Data Monitoring Specialist

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