Why risk your data project with manual testing?
iceDQ automates big data lake testing to catch errors before they reach downstream systems. It validates transformations, tests billions of records, and reconciles data across Databricks, Snowflake, AWS, Azure, GCP, and Hadoop—without sampling or manual intervention. Deliver trusted, certified data at scale.
End-to-end testing automation designed for complex data pipeline testing.
Connect and validate data across source, staging, and target systems, whether on-premise or cloud, using iceDQ’s 150 plus ready-to-use connectors.
iceDQ’s powerful capabilities allow you to handle complex test logic across any source or target system, ensuring comprehensive data integrity testing.
Design complex scenarios and validations to detect rare edge cases and data mismatches that traditional sampling methods miss.
Trigger automated ETL testing in your CI / CD pipeline using API-first design and connect with tools like Jenkins or Git.
Automatically generate rules across thousands of tables and columns to ensure full coverage with minimal setup.
Reuse test cases across dev, QA, and production environments to standardize data testing and speed up regression testing cycles.
We have standardized iceDQ for all our cloud migration.
We probably saved 5000 hours ($500,000) on the data migration project.
BMC was able to achieve 100% test coverage after iceDQ implementation.
RuleGen utility helped Pfizer reduce the duration of IT testing from 24 months to 2 months.
iceDQ has enabled testers to keep up with the pace of developers. And reduced the testing time by half.
Not only did we achieve near perfect quality, but we also saved time and money on the project.
Accelerate Testing with Prebuilt Data Reliability Checks
Runs complex test conditions using custom expressions.
Validates for NULLs, spaces, or
empty values.
Verifies if the attribute contains only the specified list of values.
Checks if the string value can be cast to a specific data type.
Ensures values fall within a specified range.
Validates string values against selected date formats
Matches values against a defined regular expression.
Detects duplicates based on a combination of one or more attributes.
Checks the length of each attribute value.
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