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The Modern AI-Powered ETL Testing Platform

The #1 ETL Testing Tool for
Data Pipelines

Bad ETL equals bad data. iceDQ validates every transformation and reconciles source to target across pipelines, warehouses, and cloud migrations. No sampling. No manual effort.

data quality dashboards that show you everything

Now AI-agent ready. Via MCP, connect iceDQ to Claude, ChatGPT, or any AI platform -- describe your ETL testing needs and let the agent handle the rest.

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Reconcile SAP BW data against BW HANA in iceDQ — compare values and detect mismatches across migrated warehouse datasets.

Trusted by Fortune 500 companies

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Source to Target Validation

ETL Data Validation and Reconciliation Across Source and Target Systems

After ETL execution, iceDQ connects to both source and target systems and applies rules to verify that data was processed correctly. Compare at row and column level, validate complex transformation logic, and detect every mismatch before it reaches production.

  • Securely connect to databases, files, cloud platforms, and data lakes via 150+ connectors
  • Detect missing records, duplicates, and mismatched values at row and column level
  • Validate complex transformations, filters, and aggregations with conditional expressions and fuzzy logic
  • Reusable test suites across Dev, QA, UAT, and production ETL runs
ETL Data Validation and Reconciliation Across Source and Target Systems
⚡
100% record coverage No sampling, ever

ETL Data Quality and Validation

Validate Business Logic and ETL Transformations Before Deployment

iceDQ validates input data before it enters the ETL pipeline, ensuring bad data is caught early and never impacts downstream systems. Define and enforce business rules, detect anomalies, and certify ETL output against functional requirements before it goes live.

  • Check completeness - all expected records, columns, and mandatory fields present before processing
  • Validate formats, data types, date patterns, numeric ranges, and reference data integrity
  • Business-level reconciliation - compare related entities like orders vs shipments or accounts vs transactions
  • GenAI assistant generates checks from natural language prompts - no coding required
Validate Business Logic and ETL Transformations Before Deployment
🔗
Catch issues early Before they reach production
✨ AI Agent Ready

AI-Powered ETL Testing Automation

Agentic ETL Testing Automation: From Discovery to Results

Describe your ETL testing needs in plain English. iceDQ's AI Agent discovers your pipeline, profiles your data, generates rules, schedules execution, and delivers results - all from a single conversation. Connect via MCP to Claude, ChatGPT, or any AI platform.

  • Discover connections, schemas, and tables automatically without opening a single screen
  • Profile source and target tables and receive intelligent check recommendations based on actual data
  • Generate hundreds of ETL testing rules from a natural language description or mapping document
  • Trigger executions and retrieve pass/fail status and exception breakdowns through natural language
Agentic ETL Testing Automation: From Discovery to Results
📊
AI-native Claude, ChatGPT and more

ETL Regression Testing and DataOps

Embed Automated ETL Testing Directly into Your CI/CD Pipeline

iceDQ is built API-first, enabling you to embed ETL testing directly into your CI/CD pipelines for continuous validation across development, QA, and production. Automate ETL regression test packs, track exceptions over time, and catch pipeline regressions before they ship.

  • Trigger rules from Jenkins, Azure Pipelines, Bamboo, or any CI/CD tool via REST APIs
  • Run regression test packs on every deployment to validate ETL changes before they reach production
  • Push test results and exceptions to JIRA, Azure Test Plans, HP ALM, and ServiceNow
  • Schedule with built-in scheduler or integrate with Airflow, Control-M, Tidal, and AutoSys
Embed Automated ETL Testing Directly into Your CI/CD Pipeline
✨
API-first Works with any CI/CD tool
See iceDQ validate and reconcile your ETL pipeline - live demo in 30 minutes No setup required. Walk through your exact ETL data validation use case with our team.

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Manual scripts can't replace a purpose-built
ETL testing tool

See how iceDQ transforms ETL testing across every key dimension - from data validation coverage to compliance readiness.

Capability Manual Testing iceDQ Recommended
ETL pipeline record coverageSample only - 5 to 10% of records Billions of records, 100% coverage
Source to target reconciliationManual, error-prone scripts per pipeline Built-in, row and column level, 150+ connectors
Business logic validationHand-written per transformation rule AI auto-generated, SQL and Groovy scripting
Business-level reconciliationNot supported Aggregate reconciliation with transaction-level drill-down
Pre-pipeline input validationLimited or manual spot checks 10 prebuilt checks, GenAI-assisted rule creation
CI/CD pipeline integrationNot supported API-first, Jenkins, Azure Pipelines, Bamboo compatible
AI agent and MCP connectivityNot available Native MCP agent for Claude, ChatGPT, and more
Exception reportingManual logs and documentation Granular record and column-level defect reports

Real outcomes from real ETL deployments

Fortune 500 teams across industries have automated ETL testing, eliminated production defects, and cut validation costs with iceDQ.

Investment Bank
Automated ETL regression testing across 200+ pipelines, eliminating all production data defects
Testing Cycle
8 weeks10 days
Automation
0% 100%
Prod Defects
12/quarterZero
88% faster regression cycles
Pharmaceutical Company
Scaled source-to-target reconciliation from 40 to 400+ ETL pipelines, cutting validation costs by 75%
Pipelines Tested
40/month400+/month
Testing Cost
$380K $95K
Data Defects
MultipleZero
75% cost reduction
Insurance Company
Replaced 40 hours per week of manual ETL reconciliation with fully automated testing in under 3 months
Manual Effort
40 hrs/week2 hrs/week
ETL Deploy Time
6 weeks1 week
Team Required
8 people3 people
95% effort reduction
Want results like these on your next ETL project? Our team will map iceDQ to your exact pipeline and data environment.

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Accelerate ETL testing with prebuilt data validation and
quality checks

Launch ETL data validation immediately with a library of prebuilt checks - no custom scripting required.

custom
Custom
Complex conditions using custom expressions
custom
Completeness
Validates for NULLs, spaces, or empty values
custom
Datatype
Checks if values can be cast to a specific type
custom
Range
Ensures values fall within a defined range
custom
Contains
Verifies attribute contains only specified values
custom
Date
Validates strings against selected date formats
custom
Pattern
Matches values against a regular expression
custom
Duplicate
Detects duplicates across one or more attributes
custom
Length
Checks the length of each attribute value
custom
Reconciliation
Cross-system record matching and validation

What our customers say

"

We have standardized iceDQ for all our cloud migration. It has become the foundation of our data testing practice.

"

We probably saved 5,000 hours - approximately $500,000 - on the data migration project alone.

"

BMC was able to achieve 100% test coverage after iceDQ implementation. The improvement was immediate.

"

RuleGen helped Pfizer reduce IT testing duration from 24 months to 2 months. A transformation in how we work.

"

iceDQ enabled testers to keep up with the pace of developers and reduced testing time by half across all projects.

"

Not only did we achieve near-perfect quality, but we also saved significant time and money on the project.

Everything you need for ETL testing automation,
out of the box

⚙️Parameterization
⚙️Rules Wizard
⚙️Data Integrity Testing
⚙️Data Monitoring
⚙️Built-In Scheduler
⚙️User-Defined Functions
⚙️Flat File Testing
⚙️SAP HANA Migration Testing
⚙️Reporting and Analytics
⚙️Security - LDAP and SSO
⚙️Query Designer
⚙️Regression Testing
⚙️Salesforce Migration Testing
⚙️Alerts and Notifications
⚙️Integrated Key Vault

Ready to automate your ETL testing?

Join Fortune 500 teams who have eliminated manual ETL data validation and reconciliation with iceDQ - cutting testing time by up to 70%.

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Learn more about ETL testing automation

📊
Blog

Data Testing vs Data Monitoring
vs Data Observability

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📖
Guide

What is ETL Testing: Concepts, Types, Examples & Scenarios

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🏆
Case Study

iceDQ Success Story: One Bank,
14 Use Cases

Read More →